Genetic and Environmental Influences on the Relation Between Attention Problems and Attention Deficit Hyperactivity Disorder

Objective The assessment of symptoms of ADHD in children is usually based on a clinical interview or a behavior checklist. The aim of the present study is to investigate the extent to which these instruments measure an underlying construct and to estimate the genetic and environmental influences on individual differences in ADHD. Methods Maternal ratings were collected on 10,916 twins from 5,458 families. Child Behavior Checklist (CBCL) ratings were available for 10,018, 6,565, and 5,780 twins at the ages 7, 10, and 12, respectively. The Conners Rating Scale (4,887 twins) and the DSM interview (1,006 twins) were completed at age 12. The magnitude of genetic and environmental influences on the variance of the three measures of ADHD and the covariance among the three measures of ADHD was obtained. Results Phenotypic correlations range between .45 and .77. Variances and covariances of the measurements were explained mainly by genetic influences. The model that provided the best account of the data included an independent pathway for additive and dominant genetic effects. The genetic correlations among the measures collected at age 12 varied between .63 and 1.00. Conclusions The genetic overlap between questionnaire ratings and the DSM-IV diagnosis of ADHD is high. Clinical and research implications of these findings are presented.
Introduction
As is the case for all psychiatric disorders, the diagnosis of attention deficit hyperactivity disorder (ADHD) is not based on a specific pathological agent, such as a microbe, a toxin, or a genetic mutation, but rather on the collection of signs and symptoms and evidence of impairment that occur together more frequently than expected by chance (Todd et al. 2005). The presence of these symptoms is usually established by direct observation, or by the completion of a clinical interview or questionnaire by the parent or teacher of a child. Instruments vary with respect to the included symptoms, the exact manner of data collection (checklist or interview), and the response format (e.g., yes/no versus Likert scale). In the present paper, we investigated if (co)variance of the scores on different instruments can be explained by a common underlying construct and to what extent this common factor is influenced by genetic and environmental factors. The focus is on three widely used instruments: the Child Behavior Checklist (CBCL; Achenbach 1991), the Conners Parent Rating Scale-Revised:Short version (CPRS-R:S; Conners 2001), and the Diagnostic and Statistical Manual of Mental Disorders-4th edition (DSM-IV; American Psychiatric Association 1994).
The CBCL-Attention Problem scale (CBCL-AP) was developed by means of factor analyses, and includes eleven items. The psychometric properties and methods to establish the reliability of the syndrome are discussed in detail elsewhere (Achenbach 1991). Despite its name, the scale assesses problems related both to attention and hyperactivity. The CBCL has sex- and age-specific norms, which are useful in assessing a child’s risk for ADHD. The CPRS-R:S ADHD-index comprises the 12 best items for distinguishing children with ADHD from children without ADHD as assessed by the DSM (Conners 2001). As with the CBCL, sex- and age-specific norm scores are available, allowing the clinician to determine whether a given child is at risk for ADHD. DSM-IV ADHD is assessed on the basis of 18 symptoms; nine relate to inattention, and nine relate to hyperactivity/impulsivity. In the DSM framework, ADHD is viewed as a categorical trait; i.e., children either do or do not meet criteria for ADHD. The norms for clinical diagnosis do not vary as a function of sex or age of the child. Table 1 contains the symptoms included in the CBCL-AP scale, the CPRS-R:S ADHD-index and DSM-IV ADHD.
Table 1An overview of the Child Behaviour Checklist (CBCL), Conners Parent Rating Scale-Revised:Short version (CPRS-R:S), and the Diagnostic and Statistical Manual of Mental Disorders-4th edition symptomsScaleSymptomCBCL Attention ProblemsActs too young for his/her ageCan’t concentrate, can’t pay attention for longCan’t sit still, restless, or hyperactiveConfused or seems to be in a fogDaydreams or gets lost in his/her thoughtsImpulsive or acts without thinkingNervous, high-strung, or tenseNervous movements or twitchingPoor school workPoorly coordinated or clumsyStares blanklyCPRS-R:S ADHD-indexInattentive, easily distractedShort attention spanFidgets with hands or feet or squirms in seatMessy or disorganized at home or schoolOnly attends if it is something he/she is very interested inDistractibility or attention span a problemAvoids, expresses reluctance about, or has difficulties engaging in tasks that require sustained mental effort (such as schoolwork or homework)Gets distracted when given instructions to do somethingHas trouble concentrating in classLeaves seat in classroom or in other situations in which remaining seated is expectedDoes not follow through on instructions and fails to finish schoolwork, chores, or duties in the workplace (not due to oppositional behavior or failure to understand directions)Easily frustrated in effortsDSM-IV ADHDInattentionOften fails to give close attention to details or makes careless mistakes in schoolwork, work, or other activitiesOften has difficulty sustaining attention in tasks or play activitiesOften does not seem to listen when spoken to directlyOften does not follow through on instructions and fails to finish schoolwork, chores, or duties in the workplace (not due to oppositional behavior or failure to understand instructions)Often has difficulty organizing tasks and activitiesOften avoids, dislikes, or is reluctant to engage in tasks that require sustained mental effort (such as schoolwork or homework)Often loses things necessary for tasks or activities (e.g., toys, school assignments, pencils, books, or tools)Is often easily distracted by extraneous stimuliIs often forgetful in daily activitiesHyperactivityOften fidgets with hands or feet or squirms in seatOften leaves seat in classroom or in other situations in which remaining seated is expectedOften runs about or climbs excessively in situations in which it is inappropriateOften has difficulty playing or engaging in leisure activities quietlyIs often “on the go” or often acts as if “driven by a motor”Often talks excessivelyOften blurts out answers before questions have been completedOften has difficult awaiting turnOften interrupts or intrudes on others (e.g., butts into conversations or games)
Although the CBCL, DSM, and CPRS-R:S focus on different symptoms, and are based on distinct assumptions, the scores of these instruments are strongly related. CBCL-AP scores predict the presence of ADHD (Gould et al. 1993; Chen et al. 1994; Eiraldi et al. 2000; Lengua et al. 2001; Sprafkin et al. 2002; Hudziak et al. 2004). In a non-referred sample enriched for ADHD, about 50% of the children with a high CBCL-AP score were diagnosed with ADHD compared to 3% of the children with a low CBCL-AP score (Derks et al. 2006). Although these results imply a good convergence between the CBCL and a DSM-IV interview, the relation is clearly less than perfect. The CPRS-R:S ADHD-I was developed for assessing children at risk for ADHD based on a DSM-IV diagnosis (Conners 2001). Conners (2001) showed that the CPRS-R:S ADHD-I is a good screening instrument for DSM-IV ADHD with a sensitivity of 100%, a specificity of 92.5%, and an overall correct classification rate of 96.3%. As far as we know, the relation between CBCL-AP and the CPRS-R:S ADHD-I has not been studied, but given that they are both related to DSM-IV ADHD, these are likely to be correlated.
Genetic studies of psychiatric disorders are complicated by the lack of clear diagnostic tests (Hudziak 2001). Heritability estimates in epidemiological genetic studies, and the results of gene-finding studies may depend on the exact instrument that is used to assess ADHD. Although a number of papers have established the convergence between CBCL-AP and DSM-IV ADHD, the causal factors underpinning this relationship remain unclear. Is it the result of genetic overlap, environmental overlap, or both? This is an important question, which may determine the progress in gene finding studies. If variance in alternative measures of ADHD is explained by different genes, we would expect disagreement in the results of studies using different instruments. If the same genes explain variance in these measures, the data from studies using different instruments may be combined in order to increase statistical power (Boomsma 1996; Boomsma and Dolan 1998). Assuming that the convergence between different instruments will be less than perfect, part of the variance will be attributable by instrument-specific factors. It is important to investigate the nature of such factors. If the divergence among instruments is merely a matter of measurement error, we would expect no genetic influences on the instrument-specific factors. Genetic influences on the instrument-specific factors, on the other hand, would suggest that the instruments tap partly unique aspects of children’s behavior.
Genetic and environmental influences on individual differences in behavior can be studied in genetically informative designs, such as the classical twin design. Such studies have shown that genetic influences explain between 55 and 89% of the variance in clinical diagnoses of ADHD (Eaves et al. 1997; Sherman et al. 1997). Shared environmental influences were nearly always absent. Likewise, about 70–80% of the variance in CBCL-AP scores is explained by genetic influences. The remaining variance is explained by non-shared environmental influences (Rietveld et al. 2003; Hudziak et al. 2000; Gjone et al. 1996). Kuntsi and Stevenson (2001) used the Conners Rating Scale to assess symptoms of ADHD and reported a heritability of 72%. A review of genetic studies on AP, HI and ADHD suggested the absence of qualitative and quantitative sex differences in the genetic etiology of parent ratings of ADHD (Derks et al. in press).
Interestingly, in parent ratings, but not in teacher ratings, the DZ twin concordances and correlations are lower than would be expected under a purely additive genetic model. For example, in maternal structured interview reports, the concordance rate is .67 in MZ twins, but .00 in DZ twins (Sherman et al. 1997). Similarly, in CBCL ratings, the DZ twin correlations are less than half the MZ correlations (Rietveld et al. 2003). In the literature, two explanations are offered for these low DZ correlations. Firstly, the DZ correlation can be less than half the MZ correlation due to the presence of non-additive genetic effects (i.e., genetic dominance) (Lynch and Walsh 1998). Secondly, the low DZ correlation may be explained by social interaction effects, which may be the result of interaction among siblings (i.e., the behavior of a twin influences the behavior of the other twin) or rater bias (i.e., the behavior of a twin is compared to the behavior of the other twin) (Eaves 1976; Carey 1986; Boomsma 2005). In previous studies, support was found both for the presence of genetic dominance (Rietveld et al. 2003; Martin et al. 2002) and sibling interaction (Simonoff et al. 1998; Kuntsi and Stevenson 2001; Vierikko et al. 2004; Eaves et al. 1997).
A high heritability of attention problems and ADHD has been reported, irrespective of the instrument that is used. However, based on the findings of univariate studies, we cannot conclude that CBCL, Conners Rating Scale, and DSM ratings measure the same construct, or that they are influenced by the same set of genes. To address this question, multivariate analyses are needed. Although a number of studies have focused on the genetic and environmental influences on either AP or ADHD, only the study of Nadder and Silberg (2001) included multivariate analyses. Nadder and Silberg (2001) analyzed data obtained in a sample of 735 male and 819 female same-sex twin pairs, aged 8–16 years. They modelled the genetic influences on nine measures of ADHD symptomatology, including maternal and paternal DSM-III-R interview data (three dimensions: hyperactivity, inattention and impulsivity), maternal questionnaire data (the Rutter Parental Scale, and the CBCL), and a questionnaire completed by the twin’s teacher. The aim of this study was to determine whether overactivity, inattention, and impulsivity reflect the same underlying genetic liability, while taking method (i.e., instrument-specific) variance into account. In males, 23.7–70.1% of the genetic variance was explained by a common factor that loaded on all nine indicators. A second and third factor loaded on the three dimensions of the maternal and paternal interview data, respectively. The remaining variance (0.0–65.7%) was explained by factors that were specific to each measure. In females, there was also one factor common to all indicators (explaining 16.2–60.2% of the variance), and a second and third factor, which loaded on the three dimensions of the interview data. In contrast to the males, a fourth factor loaded on the three behavioral questionnaires. This factor explained 12.3–46.2% of the genetic variance. In total, measurement specific factors explained 0.0–73.0% of the genetic variance.
The purpose of the present paper is to investigate the construct validity of CBCL-AP, CPRS-R:S ADHD-I, and DSM-IV ADHD. Three questions are addressed. First, what are the phenotypic correlations between the three instruments? Second, do the instruments reflect a common underlying factor? Third, what are the genetic and environmental influences on the common and the instrument-specific factors?
Methods
Subjects
This study is part of an ongoing longitudinal twin study in the Netherlands. The subjects were all registered at birth with the Netherlands Twin Register (Boomsma et al. 2002, 2006; Bartels et al. 2007). Mothers of the registered twin pairs receive the CBCL and the CPRS at the ages 7, 10, and 12 years. A subsample of the twins was selected based on their longitudinal CBCL scores. The mothers of these pairs completed a diagnostic interview. The twins, with an age range of 10–13 years (mean age = 11.71; SD = .77) at the time of the interview, were born between 1989 and 1994. The mean time-span between the completion of the interview and the questionnaires was 4.42 (SD = .75), 1.82 (SD = .73), and −.84 (SD = .63) years for the questionnaires completed at age 7, age 10, and age 12, respectively.
Questionnaires were sent to all families that agreed to participate with the research of the Netherlands Twin Registry when the children were born (N = 7,828 families; birth cohorts 1989–1994) at the ages 7, 10, and 12 years. At least one measurement is available for 10,916 twins from 5,458 families, so the response rate is 70%. CBCL ratings were available in 10,018 twins at age 7, 6,565 twins at age 10, and 5,780 twins at age 12. CPRS-R:S ratings were available for 4,887 twins at age 12, and DSM-IV interviews were available for 1,006 twins. Complete data were available in 740 twins. The fact that the number of CPRS-R:S ratings is lower than the number of CBCL ratings, can be explained by the fact that the CPRS-R:S was not included for children born before 1991. The number of available questionnaires decreases over time as a result of the longitudinal character of the study (i.e., a number of children in the study had yet to reach the age of 12).
Zygosity diagnosis was based on DNA in 674 same-sex twin pairs. In the remaining same-sex pairs, zygosity was assessed using a 10–item questionnaire. Zygosity determination using this questionnaire is almost 95% accurate (Rietveld et al. 2000). Of the 5,458 twin pairs, there were 898 monozygotic male (MZM) pairs, 888 dizygotic male (DZM) pairs, 1,005 monozygotic female (MZF) pairs, 844 dizygotic female (DZF) pairs, and 1,823 dizygotic opposite sex (DOS) pairs.
Selection for the diagnostic interview
For the diagnostic interview, subjects were selected on the basis of their standardized maternal CBCL ratings (T-scores; mean = 50, SD = 10) at the ages 7, 10, and 12 years (Derks et al. 2006). Subjects were excluded if maternal ratings were available at only one time-point, or if they suffered from a severe handicap, which disrupted daily functioning. Twin pairs were selected if at least one of the twins scored high on AP (affected pairs), or if both twins scored low on AP (control pairs). A high score was defined as a T-score above 60 at all available time-points (age 7, 10, and 12 years) and a T-score above 65 at least once. A low score was defined as a T-score below 55 at all available time-points. The control pairs were matched with the affected pairs on the basis of sex, cohort, maternal age, and social economic status (SES). T-scores were computed in boys and girls separately. In other words, girls were selected if they scored low or high compared to other girls, and boys were selected if they scored low or high compared to other boys. This procedure resulted in the selection of similar numbers of boys (N = 499) and girls (N = 507).
Measures
The Child Behavior Checklist (CBCL) (Achenbach 1991) is a standardized questionnaire designed for parents to report the frequency and intensity of their children’s behavioral and emotional problems as exhibited in the past 6 months. It consists of 120 items that measure problem behavior. The items are rated on a 3-point scale ranging from “not true = 0”, “somewhat or sometimes true = 1”, to “very true or often true = 2”. The Attention Problem scale contains 11 items. The 2-week test–retest correlation and the internal consistency of this scale are .83 and .67, respectively (Verhulst et al. 1996). In the statistical analyses, we included the CBCL ratings at the ages 7, 10, and 12 years in order to correct for the selection, as explained below.
The Conners’ Parent Rating Scale-Revised is a widely used instrument to assess behavior problems in the past month (CPRS-R; Conners 2001; Conners et al. 1998). The short version contains 28 items. The items are rated on a 4-point scale ranging from “not true at all = 0” to “very much true = 3”. The CPRS-R:S ADHD-I, which was used in the present study, comprises the best 12 items for distinguishing children with ADHD from children without ADHD as assessed by the DSM-IV (American Psychiatric Association 1994; Conners 2001). The internal consistency of this scale at age 12–14 years is .94 in boys and .91 in girls. The 6–8 weeks test–retest correlation is .72. The Diagnostic Interview Schedule for Children (DISC) (Shaffer et al. 1993) is a structured diagnostic interview. It can be used to assess the presence of DSM-IV diagnoses, including ADHD. The Dutch translation is by Ferdinand and van der Ende (1998). The mothers of twins were interviewed by ten experienced research assistants to determine which symptoms of ADHD were displayed by the twins during the last year. Maternal ratings of DISC symptoms in their children were assessed by the same interviewer for each twin in a given pair. We analyzed the total number of symptoms.
Statistical analyses
Transformation to categorical data
The distributions of the CBCL, CPRS-R:S, and DSM symptom data are characterized by excessive skewness and kurtosis. Derks et al. (2004) showed that bias in parameter estimates due to non-normality of the data may be avoided by using categorical data analysis. In this approach, a liability threshold model is applied to the ordinal scores (Lynch and Walsh 1998). It is assumed that a person is “unaffected”, if his or her liability is below a certain threshold, and that he or she is “affected”, if his or her liability is above this threshold. In the present paper, the scores were recoded in such a way that three thresholds divide the latent liability distribution into four categories, of about equal size. The liability threshold model was identified by constraining the variance of the observed variables at 1.
The CBCL AP score was calculated by summing the responses on the 11 items which resulted in a sum score with a possible maximum of 22. The four categories consisted of a score of 0, 1–2, 3–5, and 6 or higher, respectively. The CPRS-R:S ADHD-I score was calculated by summing the responses on the 12 items, which resulted in a sum score with a possible maximum of 36. The four categories consisted of a score of 0–1, 2–5, 6–11, and 12, or higher, respectively. The DISC sumscore with a range of 0 to 18 was transformed into an ordinal variable with four categories. The four categories were: (i) not affected (0 symptoms); (ii) mildly affected (1–2 symptoms); (iii) moderately affected (3–5 symptoms); and (iv) highly affected (more than 6 symptoms). The use of this four category variable provides greater resolution, and so better statistical power than the use of a dichotomous variable (ADHD absent versus ADHD present).
Correcting for the selection
Diagnostic interview data were collected only in a subsample of the twins. The probability of selection for the interview depends on a measured variable, namely the twin’s CBCL scores at age 7, 10, and 12. The data of the complete sample may be partitioned as the observed (selected) and missing (unselected) parts. The data are missing at random (MAR) if the probability of missingness depends only on the observed part of the data, and not on the missing part (Little and Rubin 2002). Given that the data are MAR, unbiased parameter estimates can be obtained by full information (i.e., raw data) maximum likelihood estimation of the parameters in a statistical model that includes the variables that were used for selection. It is essential to include all variables that were used for selection, because the probability of missingness should not depend on the missing part of the data, in which case the data would be missing not at random (MNAR) and parameter estimates would be biased. We therefore included the CBCL ratings obtained at the ages 7, 10, and 12 years in the statistical analyses. All twin pairs in which at least one measure is available are included in the analyses.
Prevalences
To investigate if the prevalences of AP and ADHD depend on the twin’s sex or zygosity, we performed χ2-tests with the five ordinal measures as dependent variables and sex and zygosity as independent variables.
Genetic modeling
Genetic and environmental influences on variance in ADHD scores were estimated using structural equation modeling. All model fitting was performed on raw data with Mx (Neale et al. 2003), a statistical software package well suited for conducting genetic analyses.
The influence of the relative contributions of genetic and environmental factors to individual differences in ADHD can be inferred from the differences in correlations across MZ and DZ twin pairs, as MZ and DZ twins differ in their genetic relatedness (Plomin et al. 2001). Using the twin method, phenotypic variance may be attributed to additive genetic effects (A), dominant genetic effects (D) or shared environmental effects (C), and non-shared environmental (E) effects. The genetic effects (A and D) correlate 1 in MZ twins, as they are genetically identical. In DZ twins, A correlates .5, and D correlates .25. C correlates 1 in both MZ and DZ twins. E or non-shared environmental effects are, by definition, uncorrelated. Uncorrelated measurement error, if present, is absorbed in the E term. Note that estimating C and D at the same time is not possible in a design using only data from MZ and DZ twins reared together. If the correlations of DZ twins are less than half the correlations of MZ twins, which is the case for maternal ratings of attention problems and ADHD, D is included in the genetic model. The proportion of the variance accounted for by heritability or environmental influences is calculated by calculating the ratio of variance due to A, D, or E to the total phenotypic variance. For instance, let a, d, and e denote the regression coefficients in the regression of the phenotype on the standardized latent variables A, D, and E, respectively. The variance due to A is then a2, and the (narrow-sense) heritability is calculated as a2/(a2 + d2 + e2).
Social interactions may be an additional source of variance. Social interaction effects lead to differences in variances in MZ and DZ twins in continuous data (Carey 1986). Using ordinal data, the presence of an interaction component can be tested by equating the prevalences of AP/ADHD between MZ and DZ twins. The absence of significant prevalence differences suggests that the presence of sibling interaction or rater bias is considered implausible.
Three multivariate models were tested: a triangular (Cholesky) decomposition, an independent pathway model, and a common pathway model (Neale and Cardon 1992). The triangular decomposition is the least restrictive model, as no specific hypotheses regarding the covariance matrices of A, D, and E are tested. These matrices are merely assumed to be positive (semi) definite. This is a saturated model that can be used to obtain (otherwise unconstrained) genetic and environmental correlations among traits. In the independent pathway model, common and specific genetic and environmental factors are included. In our data analyses of the five variables, we specified a common factor and five instrument-specific factors for each of the factors A, D, and E, which we denote Ac, Dc, and Ec. An independent pathway model provides a good fit to the data if the covariance between the five variables is due to the common factors Ac, Dc, and Ec. Finally, in the common pathway model, a model that is nested under the independent pathway model, it is assumed that genes and environment explain variance in a latent phenotype. This latent factor, of which the variance is constrained at 1, explains variance in the five variables. In addition, the variance of the five variables is allowed to be influenced by instrument-specific influences of A, D, and E. In other words, the common pathway model would provide a good fit to the data if the covariance between the five variables can be explained by a latent construct.
Because the number of twins for whom interview data are available is relatively small, and sex differences in heritability are usually not found, the data from male and female twins were combined in the analyses. To allow for prevalence differences between boys and girls, sex was included as a covariate on the thresholds. The type-I error rate of all statistical tests was set at .05.
Results
Descriptives
The prevalences for the five measures were compared between MZ and DZ twins and between boys and girls. The first model that was fitted to the data was a fully saturated model. In this model, 90 correlations were estimated, 45 in MZ twins and 45 in DZ twins. In addition, the model included 30 thresholds in each of the following groups: MZ boys, DZ boys, MZ girls, and DZ girls, which results in a total of 120 estimated thresholds. Next, a model was fitted that included a number of constraints on the thresholds. This model included 30 thresholds, 1 sex effect on the thresholds, and 5 zygosity effects on the thresholds (one for each of the five measurements). As this model fitted the data well, it was used as the reference model to test for prevalence differences as a function of zygosity for each of the five measurements. The results of these analyses are summarized in Table 3. Zygosity did not affect the prevalences of the CBCL, CPRS-R:S, and DSM scores. In view of the absence of prevalence differences in MZ and DZ twins, social interaction effects were not included in the genetic model. The model that was used as the reference model to test for sex differences included as free parameters 30 thresholds, 1 zygosity effect on the thresholds, and five sex effects on the thresholds, one for each measurement. The results showed that boys have significantly more problems than girls on all five measurements; therefore, sex was included as a covariate on the thresholds. Because of the use of categorical scores in the present paper, we did not report means and standard deviations of the CBCL, CPRS-R:S and DSM scores. These descriptives can be requested from the corresponding author by interested readers.
Twin correlations
The polychoric correlations between the five measurements are shown in Table 2 for MZ and DZ twins. The MZ (DZ) twin correlations are reported above (below) the diagonal. As expected, the phenotypic correlations (i.e., the correlation between traits within the same individual) are similar in first- and second-born twins and in MZ and DZ twins. The correlations range from .45 to .77, with slightly lower correlations between different assessment methods (e.g., CBCL questionnaire versus clinical interview) than similar assessment methods (e.g., CBCL questionnaire versus CPRS-R:S questionnaire). Equating the correlations of first- and second-born twins at age 12, the phenotypic correlation between CBCL-AP and CPRS-R:S was .75, while the correlations between CBCL-AP and DSM, and CPRS-R:S and DSM were .62. The fact that the cross-twin and the cross-trait cross-twin correlations are higher in MZ than DZ twins indicates that genetic influences contribute to the variance of the three measures and to the covariance between them.
Table 2Polychoric correlations in monozygotic (above diagonal) and dizygotic (below diagonal) twinsFirst-bornSecond-bornCBCL 7 CBCL 10 CBCL 12 CPRS DSM CBCL 7 CBCL 10 CBCL 12 CPRSDSMFirst-bornCBCL age 71.66.62.51.59.76.54.49.45.45CBCL age 10.701.69.61.59.56.77.58.53.48CBCL age 12.63.741.71.57.48.54.75.58.53CPRS-R:S .56.68.771.60.46.55.62.84.51DSM.51.55.59.681.34.41.46.46.64Second-bornCBCL age 7.31.22.18.15.041.66.63.52.46CBCL age 10.22.35.22.21.01.661.71.64.59CBCL age 12.21.28.34.24.13.60.721.75.58CPRS-R:S .22.27.28.38.08.49.64.741.60DSM.11.16.11.07.13.45.63.67.581Note: CBCL = Child Behavior Checklist; CPRS = Conners Parent Rating Scale-Revised:Short version ADHD-index; DSM = Diagnostic Statistical Manual of Mental Disorders
Genetic analyses
A Cholesky decomposition that included additive genetic influences (A), dominant genetic influences (D), and non-shared environmental influences (E) was fitted to the data. The full ADE cholesky decomposition fitted the data well (χ2(50) = 59.03, P = .180); see Table 3 for an overview of the model fitting results. Next, an independent pathway model was fitted to the data. Imposition of the independent pathway model for A, D, and E, resulted in a significant deterioration in fit compared to the fit of a cholesky decomposition (χ2(15) = 42.42, P < .001). Additional analyses showed that the influence of A and D were consistent with the independent pathway model, whereas the influence of E was not. A model that incorporated an independent pathway model for A and D, and a cholesky decomposition for E fitted well compared to the full cholesky decomposition (χ2(10) = 16.45, P = .087). The fit of the common factor model was poor (χ2(23) = 259.12, P < .001). Next, we tested if the instrument-specific influences of A and D could be constrained at zero. The instrument-specific additive genetic factors could not be dropped from the model (χ2(5) = 91.80, P < .001). In contrast, the dominant genetic variance could be explained by one common factor (χ2(5) = 1.06, P = .96). In other words, the covariance structure of D did not include specific variances. This means that this covariance matrix has rank one, and that the correlations (obtained by standardizing the covariance matrix of D) were all one. Figure 1 provides a graphical representation of the genetic part of the best fitting model and includes the unstandardized factor loadings of the additive genetic and dominant genetic factors.
Table 3Multivariate model fitting of maternal ratings on CBCL, CPRS-R:S and DSM-IV ratings on attention problems and ADHD in 7-year-old children−2 log LLN parWith modeld.f.χ2P1. Fully saturated63020.52210––––2. Thresholds MZ/DZ free, thresholds boys/girls equated 63123.54126184103.02.082a. Thresholds CBCL age 7 equated in MZ/DZ63124.66125211.11.292b. Thresholds CBCL age 10 equated in MZ/DZ63123.9812521.43.512c. Thresholds CBCL age 12 equated in MZ/DZ63123.6812521.14.712d. Thresholds Conners age 12 equated in MZ/DZ63123.6012521.06.812e. Thresholds DSM age 12 equated in MZ/DZ63126.34125312.80.093. Thresholds boys/girls free, thresholds MZ/DZ equated 63108.1812618487.66.373a. Thresholds CBCL age 7 equated63423.1912531315.01<.0013b. Thresholds CBCL age 10 equated63395.1812531287.00<.0013c. Thresholds CBCL age 12 equated63321.3212531213.14<.0013d. Thresholds Conners age 12 equated63388.2412531280.06<.0013e. Thresholds DSM age 12 equated63137.031253128.85<.0014. Cholesky decomposition ADE63147.4110215059.03.184a. Independent pathway model D; Cholesky decomposition AE63149.4097451.99.854b. Independent pathway model A; Cholesky decomposition DE63151.8897454.47.484c. Independent pathway model E; Cholesky decomposition AD63170.06974522.65<.0014d. Independent pathway AD; Cholesky decomposition E63163.869241016.45.094e. Independent pathway model ADE63189.838741542.42<.0014f. Independent pathway AD; Cholesky decomposition E, instrument-specific A factors dropped63255.66874d591.80<.0014g. Independent pathway AD; Cholesky decomposition E, instrument-specific D factors dropped63164.92874d51.06.965. Common pathway model63406.5379423259.12<.001Fig. 1A graphical representation of the unstandardized additive genetic (A) and dominant genetic (D) effects on five measurements of Attention Problems and ADHD. In this figure, a graphical representation of the best-fitting model and the estimated factor loadings is provided for one individual twin. Additive genetic effects correlate 1 in MZ twins and .5 in DZ twins. Dominant genetic effects correlate 1 in MZ twins and .25 in DZ twins. To identify the model, the variances of the five categorical measurements are constrained at 1. CBCL7 = Child Behavior Checklist at age 7; CBCL10 = Child Behavior Checklist at age 10; CBCL12 = Child Behavior Checklist at age 12; CPRS-R:S = Conners Parental Rating Scale-Revised:Short version at age 12; DSM = DISC-IV ADHD at a mean age of 12 years
Although the influence of the nonshared environment was not included in Fig. 1, the fact that the total variances of the five measurements are constrained at 1 in order to identify the model allows a calculation of the additive and dominant genetic variance based on the unstandardized factor loadings. For example, 41% (i.e., .442 + .462) of the variance in the CBCL rating at age 7 is attributable to additive genetic effects, 36% (.602) is attributable to dominant genetic effects. The remaining variance is explained by nonshared environmental effects. The additive genetic variance on the five measurements can be decomposed into variance due to the common factor and variance due to instrument-specific factors. For the CBCL rating at age 7, 19% (.442) of the total variance is attributable to common additive genetic effects, and 22% (.462) is attributable to instrument-specific genetic effects. The influence of common additive genetic effects on the total variance accounts for 36%, 55%, 56%, and 32% for the CBCL at age 10, the CBCL at age 12, the CPRS-R:S, and the DSM, respectively. Likewise, the influences of instrument-specific effects account for 17, 13, 23, and 24% of the variance, respectively.
Table 4 shows an overview of the standardized influences of A, D, and E on the variance and covariance of the five measurements. The three diagonals of the five by five tables of A, D, and E contained the standardized variance components. The results indicate a high heritability, irrespective of measurement instrument or age. On the off-diagonals in Table 4, one can find the standardized influences of A, D, and E on the covariance between the measurements. For example, the covariance between CBCL7 and DSM is for 51% explained by A, 25% by D, and 24% by E. To obtain the unstandardized amount of variances explained, the standardized influences should be multiplied with the phenotypic covariance between the measures, which is .51 for CBCL7 and DSM. The most interesting comparison is between the data that were collected at approximately the same time. The covariance between the CBCL at age 12 and the DSM is explained largely by genetic effects (68% A, 9% D, and 23% E). Similar results were found for the covariance between CPRS-R:S and the DSM (67% A and 7% D) and for the covariance between the CBCL age 12 and CPRS-R:S (74% A and 8% D).
Table 4Standardized genetic and environmental influences on the variances and covariances of five ratings of ADHD and attention problemsADECBCL 7CBCL 10CBCL 12CPRS-R:SDSMCBCL 7CBCL 10CBCL 12CPRS-R:SDSMCBCL 7CBCL 10CBCL 12CPRS-R:SDSMCBCL age 7.41.36.23CBCL age 10.39.53 .45.25.16.22CBCL age 12.53.62.68 .26.19.07.21.19.25CPRS-R:S.62.69.74.79 .26.17.08.05.13.14.18.16DSM.51.57.68.67.56.25.17.09.07.04.24.26.23.25.40Note: CBCL = Child Behavior Checklist; CPRS-R:S = Conners Parent Rating Scale-Revised:Short version ADHD-index; DSM = Diagnostic Statistical Manual of Mental Disorders; A = additive genetic effects; D = dominant genetic effects; E = non-shared environmental effects
Table 5 includes the genetic and environmental correlation matrices in the best-fitting model, which represent the overlap between the genetic and environmental influences on the five measurement instruments. The additive genetic correlations range between .52 and .76. All dominant genetic correlations are 1, which is a result of the absence of specifics in the one-factor model used to model the dominant genetic covariance structure. The non-shared environmental correlations range from .34 to .68.
Table 5Genetic and environmental correlations of five ratings of ADHD and attention problemsADECBCL 7CBCL 10CBCL 12CPRS-R:SDSMCBCL 7CBCL 10CBCL 12CPRS-R:SDSMCBCL 7CBCL 10CBCL 12CPRS-R:SDSMCBCL age 71.01.01.0CBCL age 10.571.01.01.0.501.0CBCL age 12.62.741.01.01.01.0.56.601.0CPRS-R:S.58.69.761.01.01.01.01.0.34.49.681.0DSM.52.62.68.631.01.01.01.01.01.0.39.52.45.621.0Note: CBCL = Child Behavior Checklist; CPRS-R:S = Conners Parent Rating Scale-Revised:Short version ADHD-index; DSM = Diagnostic Statistical Manual of Mental Disorders; A = additive genetic effects; D = dominant genetic effects; E = non-shared environmental effects
Discussion
The aim of this study was to determine the extent to which three different instruments, which are commonly used to assess ADHD, attention problems, and hyperactivity, measure a common construct. The instruments considered are two scales based on items from questionnaires (CBCL-AP, and CPRS-R:S ADHD-I), and a DSM-IV ADHD interview. First, we considered the phenotypic correlations. Second, we tested if the variance in the different instruments reflects one common underlying factor. Third, we estimated the genetic and environmental influences on individual differences in ADHD. This is the first study that includes multivariate genetic analyses of behavior rating scales and DSM-IV interview data collected in a large sample of twins of approximately the same age. The CBCL scores collected at age 7 and 10 years were included only to correct for the selection. In the discussion, we focus mainly on the CBCL, CPRS-R:S and DSM interview data, which were collected at a mean age of 12 years.
The phenotypic correlation between CBCL-AP and the CPRS-R:S ADHD-I was high (r = .75). The correlations between the CBCL and the DSM and between the CPRS-R:S and the DSM were slightly lower (r = .62). These lower correlations can both be the result of the different time-points at which the behavior checklists and the DSM interview data were collected (the mean time-span between measurement occasions was 10 months), the differences in the time frame for the assessment of the items (e.g., 1 month for the CPRS-R:S, 6 months for the CBCL, and 1 year for the DSM), and of instrument or method variance (e.g., interview versus behavior checklists). The genetic analyses show that the covariance between CBCL and CPRS is for 82% explained by genetic factors while the covariance between CBCL and DSM was for 75% explained by genetic factors. Therefore, the higher phenotypic correlation between CBCL and CPRS is not caused by a relatively higher genetic covariance.
As noted, the AP scale of the CBCL questions relate to both inattention and hyperactivity/impulsivity. The fact that the correlation between the CPRS-R:S ADHD-I and DSM-IV ADHD is identical to the correlation between CBCL-AP and DSM-IV ADHD implies that the CPRS-R:S and the CBCL measure ADHD equally well. The description of the eleven item CBCL scale as an inattention scale seems to be too limited, because both the item content and the current results suggest that the CBCL also signals problems related to hyperactivity/impulsivity.
Although the phenotypic correlations provide an interesting insight regarding the similarities and dissimilarities of the quantitative and qualitative approaches towards child psychopathology, an important question concerns the etiological influences on the variances and covariances. In agreement with previous studies (Eaves et al. 1997; Sherman et al. 1997; Rietveld et al. 2003; Hudziak et al. 2000), individual differences in AP and ADHD are mainly explained by genetic factors. An independent pathway model provided a better fit than a common factor model. A common factor model implies a similar structure for the additive genetic, dominant genetic and nonshared environmental influences so the poor fit is probably due to the fact that there are instrument-specific additive genetic factors while these are absent for the dominant genetic factors. As referees of earlier drafts of this paper noted, alternative models might be fit to our data; a model including three common factors (one loading on all ratings, a second loading on CBCL ratings, and a third loading on age 12 ratings, might offer a good solution, but because the structure of A, D, and E differ (with no rating-specific influences for D), we did not fit this model to our data.
An independent pathway model allows for the inclusion of common and instrument-specific genetic and environmental factors. The model that provided the best fit to the data included common additive and dominant genetic effects, instrument-specific additive genetic effects, and nonshared environmental effects. The relative influence of common and instrument-specific genetic effects varies by rating. Two third of the additive genetic variance of the CBCL ratings at age 10, age 12, and the CPRS-R:S rating at age 12, was explained by common effects. More specifically, Instrument-specific effects played a more important role in the CBCL ratings at age 7, and in the DSM ratings. For these ratings, the ratio of common and instrument-specific effects was about 50:50. Apparently, the overlapping genes explain less of the variance in these ratings compared to the other ratings, probably as a result of developmental changes in behavior and of method variance (i.e., questionnaire versus interview). The dominant genetic effects overlapped completely between ratings, as the instrument-specific effects could be dropped from the model. Our results show some agreement with the findings of Nadder and Silberg (2001), who fit an independent pathway model to ADHD symptomatology based on maternal and paternal questionnaire and interview data, and to teacher reports. Although their best-fitting model included contrast effects instead of genetic dominance, our finding of both common and specific genetic influences on the questionnaire and interview data on ADHD is supported by their results.
The poor fit of the common factor model suggests that the construct validity of the instruments is not perfect. However, it is interesting to consider the implications of the overlap between the sets of genes that explained variance in the three instruments. High genetic correlations imply that the detection of the specific genes that play a role for ADHD, does not depend much on the instrument that is used. At age 12, the additive genetic correlations of the CBCL, CPRS-R:S, and DSM varied between .63 and .76, while the dominant genetic correlations could be constrained at 1. The non-shared environmental correlations are also quite high, and vary between .45 and .68. The dominant genetic correlations of 1 suggest that there is a subset of genes whose effect is not instrument or age dependent. In contrast, the correlations of the additive genetic effects are high but less than perfect. This suggests that the influence of most genes with an additive effect are not sensitive to the particular instrument that is used, although there are some genes that explain variance only in a particular measurement (e.g., CBCL), but not in another (e.g., DSM).
What are the implications of the present findings for gene finding studies? Thus far, five groups have conducted genome-wide linkage scans in an attempt to find genomic regions which are involved in ADHD, and a number of regions that may be of interest have been identified. Linkage peaks with a LOD score above 2 (P < ∼.002) were reported at chromosomes 16p13 and 17p11 (Ogdie et al. 2003), chromosomes 7p and 15q (Bakker et al. 2003), chromosomes 4q, 8q, and 11q (Arcos-Burgos et al. 2004), chromosomes 5p and chromosome 12q (Hebebrand et al. 2006), and chromosomes 14q32 and 20q11 (Gayan et al. 2005). All these studies based diagnosis on DSM-IV (Ogdie et al. 2003; Bakker et al. 2003; Arcos-Burgos et al. 2004; Hebebrand et al. 2006) or DSM-III (Gayan et al. 2005) criteria. The discrepancy in the results of these five studies could be due to low statistical power. The present study showed that the genetic overlap between behavior checklist scores and the DSM-IV diagnosis of ADHD is high. This implies that the detection of genes, which play a role for ADHD, can be based on questionnaire scores, rather than diagnostic interviews. This will reduce the costs of collecting phenotypic data. Resources may then be reallocated to the collection of genotypic data. An increased number of subjects can be genotyped and the statistical power to detect a QTL will be increased.
Limitations
The results of this study should be interpreted bearing in mind the following limitations. First, further study is required to investigate if the results of the current study, which was based on a Dutch population sample, generalize to population samples outside the Netherlands. Second, clinical diagnoses were based on structured diagnostic interviews with the mother. The results may be different when the assessment of ADHD is based on expert clinical diagnoses. Third, no distinction was made between problems related to inattention and problems related to hyperactivity. Since the CBCL does not distinguish between inattention and hyperactivity (and probably the number of items is too small to reliably measure these two factors) we did not distinguish between the subscales. Fourth, we did not allow for sex differences in the genetic and environmental influences based on the results of univariate studies. Because of the increased statistical power in the multivariate model, it is possible that sex differences do exist. However, due to the categorical nature of the data, and the fact that some of the cells in the contingency tables contain few individuals in a two-group analysis, statistical problems will arise in a four-group analysis. Fifth, as a result of the categorical nature of the data, computational limitations prohibited inclusion of confidence intervals.
Clinical implications
Two general approaches towards the measurement of ADHD can be distinguished. In the DSM-IV framework, ADHD is viewed as a categorical trait. Using behavior checklists, children can show variation in a continuum from not affected at all to severely affected. The current study shows that variance in DSM-IV symptoms, the CBCL-AP scale, and the CPRS-R:S ADHD-I is explained mostly by genetic effects. The correlations between the genetic influences on variance in these three measurements of ADHD are high. This implies that different measurements tap the same genetic liability.

genetics
attention problems
adhd
measurement
twins
multivariate analysis


MRI compared to conventional diagnostic work-up in the detection and evaluation of invasive lobular carcinoma of the breast: a review of existing literature

Purpose The clinical diagnosis and management of invasive lobular carcinoma (ILC) of the breast presents difficulties. Magnetic resonance imaging (MRI) has been proposed as the imaging modality of choice for the evaluation of ILC. Small studies addressing different aspects of MRI in ILC have been presented but no large series to date. To address the usefulness of MRI in the work-up of ILC, we performed a review of the currently published literature.
Introduction
Invasive lobular carcinoma (ILC) is the second most common histologic type of breast carcinoma after invasive ductal carcinoma (IDC). In most series ILC constitutes between 5 and 15% of all breast cancers, whereas IDC constitutes between 70 and 90% of all breast cancers [1–5]. Probably due to the use of complete hormone replacement therapy the lobular breast cancer component has continuously increased over the past decade from 9.5% in 1987 to 15.6% in 1999 [3].
Patients are, according to most series, a little older than patients presenting with IDC, especially the fraction of patients presenting with ILC younger than 40 is smaller [1, 5, 6]. Furthermore, the mean tumor size of ILC is slightly larger than in patients with IDC and patient presentation with a tumor larger than 5 cm occurs more often in cases of ILC [1, 5, 7].
Histopathologically, ILC are clearly defined: ILCs are constituted from small, relatively uniform cells, very similar to normal endothelial cells. Characteristically, these cells are only loosely cohesive and infiltrate the stroma in single cell file strands along ductuli. This growth pattern, present in 30–77% of cases [8], is also known as “Indian filing.” It is probably caused by a typical loss of the adhesion molecule E-cadherin. Often there is very little desmoplastic stromal reaction [8, 9]. The biological characteristics of ILC are usually less alarming than those of IDC: more tumors contain estrogen receptors and progesterone receptors, while expression of Her2/Neu and p53 are more often normal and axillary lymph nodes are not more often positive, even though ILC are overall larger in size than IDC [1, 7].
Probably due to the diffuse infiltrative growth pattern, ILC is frequently missed on mammography [5]. Detection is also compromised because ILC often has a density less than or equal to normal fibroglandular breast tissue on mammography [5, 10].
For correct treatment of ILC, adequate staging is important. Both mammography and ultrasound tend to underestimate lesion size and are therefore not optimal for staging purposes [5, 11]. This may in part be the reason that higher failure rates of breast-conserving therapy (BCT) in ILC than in IDC are reported [2, 11, 12]. Various authors therefore propose magnetic resonance imaging (MRI) as the modality of choice for the evaluation of ILC. Several small studies addressing the different aspects of the use of MRI in ILC have been presented, but no large series to date. Therefore many questions regarding the use of MRI in ILC remain unanswered.The sensitivity of MRI for breast lesions is approximately 95–98%, however, whether this holds true for ILC as well is not clear [13].The morphologic aspects of ILC are not yet well defined, nor is the dynamic behavior of contrast agents in these tumors clearly documented.Moreover, whether the MRI findings are similar to pathologic findings and can thus be used for accurate staging still needs to be established.Finally, the impact of MRI on surgical treatment of ILC should be evaluated.To answer these questions we performed a thorough review of the existing literature regarding the use of MRI in case of ILC and performed meta-analysis whenever possible. We subsequently reviewed the literature on other imaging modalities for this indication in order to evaluate the use of MRI from a clinical perspective.
Materials and methods
Search strategy
We performed a literature search for articles that specifically dealt with the use of MRI in patients with histologic proof of ILC published before 1 April 2006. The Cochrane Library, MEDLINE and the in-progress citations as provided by PubMed were searched using the query: “lobular AND (MR OR MRI OR MRT OR magnetic).” These databases were further searched using the “Related Articles” function in PubMed. The same query was used to browse the web using scholar.google.com.
Furthermore, the references of all retrieved articles were manually searched for relevant cross-references. Articles in all languages were accepted. All retrieved articles were then compared and from overlapping series of patients only the most recent publication was accepted.
Many different search terms were used for literature review of other imaging modalities. However, only PubMed was used as search engine.
Endpoints
The study was thus undertaken to answer the following four questions.What is the sensitivity of MRI for ILC?What are the visual characteristics of ILC on MRI?Are the findings on MRI equal to the findings at pathology?What is the impact of MRI on surgical management of ILC?Whenever studies allowed direct comparison between MRI and other imaging modalities, these modalities were also analyzed. Sensitivity was defined as the number of lesions visible on MRI divided by the total number of ILC detected at pathology. We regarded morphology, dynamic curve analysis of contrast behavior, and quantitative dynamic analysis of contrast behavior as three different aspects of tumor appearance and these were thus analyzed separately. A principal distinction between mass-like and non-mass-like lesions was made in the analysis of morphology. Based on the BI-RADS lexicon [14], we defined architectural distortion, regional, segmental, ductal, multifocal, or diffuse enhancement, and multiple enhancing foci as descriptors of non-mass lesions. Nodular or focal enhancement, well-defined, round, irregular or spiculated masses, and dominant masses with small enhancing foci were defined as descriptors of mass-like lesions. Correlation between the findings on MRI and pathology was evaluated for relative tumor size (unifocal versus multifocal disease and single quadrant versus multicentric disease) and absolute tumor size. The impact on surgical management was derived from all changes implemented, based solely on MRI findings. The numbers of correct and incorrect changes were tabulated.
Eligibility criteria
All studies that presented a series of at least ten patients with histologic proof of pure ILC, with or without concurrent DCIS and/or LCIS, were considered eligible. A quality analysis of the study had to be possible, otherwise no abstracts were accepted. Patients with mixed carcinomas of ILC and IDC were excluded. Studies that presented data on both ILC and mixed carcinomas had to allow extraction of the relevant data for ILC only. Every study considered eligible according to these eligibility criteria was then evaluated for all the study endpoints. Specific eligibility criteria for the various considered endpoints are described below.Detection: Studies had to be based on a pathology database and all subsequent patients with ILC who underwent a MRI had to be included. The total number of ILC confirmed at pathology had to be clearly stated as well as the number of lesions found with MRI.Morphology: Studies describing the appearance of ILC visible on MRI were eligible. Separation between mass and non-mass-like lesions had to be possible.Dynamic curve analysis of contrast behavior: Studies that described the enhancement versus time curve were eligible. However, as time to peak and shape of the final phase of the enhancement curve were our main endpoints, these had to be described.Quantitative analysis of contrast behavior: Studies performing quantitative analysis of the contrast-enhancement parameters were eligible.Relative correlation with pathology: Studies presenting data on the unifocal versus multifocal correlation or single quadrant involvement versus multicentric involvement were eligible.Absolute correlation with pathology: Studies comparing sizes measured on MRI with those measured at pathology and presenting a correlation coefficient or sufficient raw data to calculate such a value were eligible.Detection of additional lesions: Any study describing additional lesions apart from the index lesion detected by MRI only with subsequent acquisition of histologic proof of malignancy was considered eligible. Lesions in the ipsilateral breast and the contralateral breast were evaluated separately.Impact on surgical treatment: Studies mentioning all changes in surgical strategy based on MRI findings were eligible.
Statistics
The quality of all included studies was assessed using the QUADAS tool [15]. The latter is a list of 14 items created for quality assessment of studies to diagnostic accuracy. Although not all the included studies specifically evaluate diagnostic accuracy, this tool was judged to be the most appropriate available. Data of all the studies were collected according to the inclusion and exclusion criteria. When at least five studies presented the same type of data or at least 100 patients were included in a smaller series of studies with similar data, we considered meta-analysis and heterogeneity analysis was performed. Dichotomous data with a binomial distribution (e.g., sensitivity) were transformed to the log odds scale because this scale has a normal distribution and is a good approximation to the exact binomial distribution. A disadvantage of this transformation, however, is that the confidence intervals are a little wider and values in the middle of the distribution (e.g., sensitivity closer to 50%) are more heavily weighted in meta-analysis than values close to the upper or lower level. Pearson’s correlation coefficient was transformed to Fisher’s Z for the same reason [16].
We calculated Cochran’s Q coefficient and the I2-statistic to assess heterogeneity. Cochran’s Q is a form of the χ2-test and provides information about the applicability of pooling the data. The I2-statistic provides a quantitative measure of the amount of heterogeneity and has an upper limit of 100%. Values of the I2-statistic of 25, 50, and 75% can be interpreted as low, moderate, and high heterogeneity, respectively [17]. Meta-analysis of the data using a random effects model was performed when the Q-coefficient showed no significant heterogeneity (p > 0.05).
In cases where meta-analysis was feasible, the estimate and the 95% CL are expressed. When meta-analysis was not feasible due to severe heterogeneity, only the range of values found in the different studies is mentioned. All calculations were performed using R version 2.3.1 (The R Project for Statistical Computing, www.r-project.org) and the meta package (G. Schwarzer, cran.r-project.org).
Results
Studies
We identified 21 separate studies that dealt with MRI and ILC [18–38]. We further identified four studies that did not deal specifically with ILC and MRI. However, they did present their data in such a fashion that relevant information for ILC only could be extracted for at least ten patients [39–42]. Four studies were case-reports and were dropped from the cohort [20, 21, 29, 37]. The study by Bazzocchi et al. [18] was excluded because only eight patients underwent MRI. Leung et al. [27] and Newstead et al. [42] only published their findings in abstract form and were consequently excluded. Table 1 gives an overview of the included studies and their characteristics, including the QUADAS score.
Table 1Characteristics of the included studiesAuthorsPub. YearaStudy typebNcAge meandAge min.eAge max.fFieldgScan seq.hUni/bilatiCompressionjMean sizekQUADAS scorelRodenko et al. [32]19961206038842110X11Sittek et al. [34]1998123XXX1220X11Weinstein et al. [36]200111753326922111,712Kim et al. [41]200111254m24m88m22102,1m12Trecate et al. [35]2001128X32812220X9Francis et al. [24]2001222XXX22203,712Qayyum et al. [30]20021135546842110X11Munot et al. [28]20021206139782320X11Yeh et al. [38]200311959427922204,111Kneeshaw et al. [26]20031215743722210X11Quan et al. [31]200316253XX2231X10Bedrosian et al. [39]200312453mXX2001X10Schelfout et al. [33]20041265741743220X11Diekmann et al. [22]2004117XXX0000X10Boetes et al. [19]200413455357822204,910Berg et al. [40]2004229XXX3220X13Kepple et al. [25]20051296251672130X9Fabre Demard et al. [23]2005134XXX2220X11aYear of publication of the original articleb1 indicates retrospective cohort study, 2 indicates prospective cohort studycNumber of patients includeddMean age of all included patients, X denotes not mentionedeAge of respective youngest patient included in the studyfAge of respective eldest patient included in the studygStrength of magnetic field—0 denotes unknown, 1 denotes 1 T, 2 denotes 1.5 T, 3 denotes both 1 T and 1.5 ThType of scan sequence used—0 denotes unknown, 1 denotes RODEO, 2 denotes FLASH 3D, 3 denotes otheriUnilateral or bilateral imaging of the breast—0 denotes unknown, 1 denotes unilateral, 2 denotes bilateral, 3 denotes both unilateral and bilateral depending on the patientjCompression applied to the breast—0 denotes no, 1 denotes yeskMean size of the lesions in centimeters, X denotes not mentionedlNumber of items valid on QUADAS scorings listmValid for whole study population only, not for subpopulation of patients with ILC
The applied scan protocols in the included studies are diverse. In general, most studies presented herein used a 1.5-T MRI scanner, although some authors had at least some of their included patients scanned using 1.0 T machines [33, 34, 40]. Most protocols were based on T1 weighted images made with either a normal FLASH 3D sequence or a FLASH 3D sequence with fat-suppression [19, 20, 23, 24, 26, 31, 33–36, 38, 40, 41] or a RODEO sequence with water selective excitation [25, 30, 32]. A number of authors also used T2 weighted sequences [22, 23, 31, 38, 40, 41]. Other differences in scan protocols involve the voxel sizes and temporal resolution. Some authors emphasize high spatial resolution [32, 39] while others prefer high temporal resolution [26] and yet again others performed both types of sequences in succession [30, 38]. Furthermore, single breast coils [26, 30, 32, 36, 41, 43] and double breast coils (all others) were used and sometimes compression was applied to the imaged breast [31, 36, 39]. In most reported studies the scanning protocols evolved over time and are thus not identical for all imaged patients.
Lesion detection
Eight studies provided sufficient data to calculate sensitivity of MRI for ILC [19, 23, 24, 26, 28, 33, 34, 40]. Sensitivity ranged from 83 to 100%. Cochran’s Q was 6.48 (p = 0.49), I2 was 0%, indicating homogeneous studies and hence data pooling could be performed. Mean sensitivity was 93.3% (95% CI 88–96%). Only the studies by Francis et al. [24] and Berg et al. [40] provided prospective data and are therefore able to show sensitivity in clinical practice. They showed a sensitivity of 95 and 97%, respectively, and were statistically not different from the retrospective studies (two-sided T-test, p = 0.78). Seven of these studies also provided data on mammography [Q 31.79 (p < 0.001), I2 = 81%], six on ultrasound [Q 10.92 (p = 0.05), I2 = 54%], and five on clinical examination [Q 29.63 (p < 0.001), I2 = 87%]. Sensitivity of ultrasound could also be computed through meta-analysis and was 83% (95% CI 71–91%), although moderate heterogeneity was present. The provided data for mammography and clinical examination were too heterogeneous for meta-analysis and ranged from 34 to 91 and 28 to 94%, respectively. Figure 1 shows the results of each independent study and the overall results.
Fig. 1Forestplot of the sensitivity of the respective modalities for ILC (MMG mammography, US ultrasound, CE clinical examination), the horizontal lines represent 95% confidence intervals. Modalities presented on the right of the authors name have not been tested in the appropriate study. The diamonds at the bottom represent the pooled estimates and their 95% confidence intervals for MRI and US, respectively. Because mammography and clinical examination were too heterogeneous for meta-analysis no pooled estimate is presented for these modalities
Morphology
Seven studies described lesion morphology on static MRI images [23, 30, 32, 33, 36, 38, 41]. However, Kim et al. [41] studied morphologic appearances of masses only and therefore did not include non-mass-like lesions. Information provided by their study is therefore only used to evaluate the appearance of masses and not for the principal distinction between mass and non-mass lesions. The terminology used in the literature to describe the lesions is highly variable. Only Yeh et al. [38] consistently used the terminology of the BI-RADS lexicon [14]. The six eligible studies that presented data on morphologic appearance described a total of 133 tumors. However, results are highly variable. The incidence of a mass-like lesion ranged from 31 to 95% [Q 16.44 (p < 0.01), I2 = 70%]. Table 2 shows the appearance of ILC on MRI for all individual studies.
Table 2Morphologic appearance of ILC on MRIAuthorsNumber of tumorsNon-mass-likeMass-likeRodenko et al. [32]201 (5)19 (95)Weinstein et al. [36]188 (44)10 (56)Qayyum et al. [30]139 (69)4 (31)Yeh et al. [38]2011 (55)9 (45)Schelfout et al. [33]276 (22)21 (78)Fabre Demard et al. [23]3511 (31)24 (69)Numbers between parenthesis represent percentages
Fabre Demard et al. [23] did not specify the lesions beyond the description “mass-like.” Other authors used many different terms to further describe lesions. In the study presented by Rodenko et al. [32], five pre-defined shapes were used, but they described all 19 mass-like lesions as spicular enhancing masses. In the other studies most lesions are described as spiculated masses as well. Schelfout et al. [33] recognized a dominant mass with multiple enhancing foci in eight cases and Yeh et al. [38] described even a round focal mass. In the 12 mass-like cases described by Kim et al. [41], 10 had an irregular shape and 8 were spiculated. Therefore, among the 76 masses, a total of 65 tumors were described as an irregular or spiculated mass. This appears to be the most common type of mass-like presentation in ILC.
Kinetics
Only two studies reported on the dynamic curve appearance of ILC [34, 35]. The most apparent similarity between findings was that maximum enhancement is often delayed and wash-out is present in only a minority of lesions. Sittek et al. [34] reported that maximum enhancement was not reached before 2 min after contrast administration. Trecate et al. [35] noted that a classic pattern of rapid signal increase was only present in 4 of 12 pure ILC, whereas a delayed pattern was observed in the other 8 cases.
Two other studies reported on quantitative contrast behavior analysis in ILC [30, 38]. Qayyum et al. [30] reported on a parameter called K21, analogue to the Ktrans parameter as described by Tofts et al. [44]. Yeh et al. [38] evaluated the extraction flow product (EFP), which is a similar analogue but respects the possibility that contrast leakage from the vessels is limited by flow instead of being limited by the permeability surface area product. Both studies did not, however, include sufficient patients to produce meaningful results, other than a high variability in the values of these parameters and the presence in some tumors of enhancement very much like enhancement in normal breast tissue. It was noted that K21 values appeared to be an order of magnitude less in ILC than in IDC lesions.
Correlation
Several authors evaluated correlation of the MRI findings with pathology [19, 24–26, 28, 32, 33, 40]. Three studies compared unifocality and multifocality between MRI and pathology [26, 32, 33] (Table 3). Overall 5 of 67 cases (7%) were regarded as multifocal, whereas they appeared unifocal at pathology and, vice versa, 2 cases (3%) in one study appeared unifocal at MRI, but were multifocal according to pathology.
Table 3Relative correlation of unifocality versus multifocality for MRI versus pathologyAuthorsNumber of patientsUF MRIUF PATHMF MRIMF PATHOverestimatedaUnderestimatedbRodenko et al. [32]2091111921Kneeshaw et al. [26]21910121110Schelfout et al. [33]261417121021Total6752UF unifocal, MF multifocal, PATH pathologyaDisease was classified as multifocal on MRI, but was unifocal on pathologybDisease was classified as unifocal on MRI, but was multifocal on pathology
Overestimation of multifocality based on mammography in 63 patients from these studies occurred in 2 patients (3%), whereas underestimation occurred 25 times (40%) and the lesion was not visible on mammography in another 4 patients (6%).
Two of these studies further analyzed single quadrant versus multicentric involvement of the affected breast [32, 33] (Table 4). In the study by Rodenko et al. [32], two cases of single quadrant disease were erroneously classified as multicentric on MRI.
Table 4Relative correlation of single quadrant versus multicentric involvement for MRI versus pathologyAuthorsNumber of patientsSQ MRISQ PATHMC MRIMC PATHOverestimatedaUnderestimatedbRodenko et al. [32]2091111920Schelfout et al. [33]2621215500Total4620SQ single quadrant, MC multicentric, PATH pathologyaMulticentric involvement was seen on MRI, but involvement of only one quadrant was shown on pathologybInvolvement of only one quadrant was seen on MRI, but on pathology multicentric involvement was shown
Mammography in 42 of these patients resulted in overestimation of disease extent in 1 patient and underestimation in 15. Again, no lesion was visible in four patients.
Berg et al. [40] further showed a series of 12 patients that underwent MRI. Correct size estimation was performed in seven patients. In one patient an additional focus was missed and in four patients overestimation occurred due to foci of LCIS.
Absolute correlation of MRI and pathologic size measurement was performed by six authors [19, 24–26, 28, 32]. Rodenko et al. [32] found a Kappa coefficient of 0.77, which represents substantial agreement. The other authors presented Pearson’s correlation coefficients ranging from 0.81 to 0.97 [Q 10.90 (p = 0.03), I2 = 63%]. Correlation coefficients for other modalities were substantially more variable. Presented correlation coefficients in Table 5 are optimized by excluding cases where no abnormalities were seen from the calculations.
Table 5Correlation of tumor size measured by various modalities compared to pathologyAuthorsMRIMMGUSCENPCCΚNPCCKNPCCKNPCCKRodenko et al. [32]200.77315−0.081Munot et al. [28]200.97100.66140.67Kneeshaw et al. [26]210.86210.93a210.93a210.47Francis et al. [24]220.87160.79200.56190.89Boetes et al. [19]360.81360.34360.24Kepple et al. [25]330.8890.71MMG mammography, US ultrasound, CE clinical examination, N number of lesions visible on the appropriate modality, PCC Pearson’s correlation coefficient, K Kappa valueaKneeshaw et al. did not provide a correlation coefficient for either MMG or US, but only one for the combined modalities
Boetes et al. [19] applied a correctness measure of 1.0 cm to their data and found that MRI underestimated disease extent in 5 of 36 tumors and overestimated extent in 4 cases by more than 1.0 cm. The data provided by Francis et al. [24] allow a similar calculation. Underestimation occurred in 6 of 22 cases and overestimation occurred in 1.
Additional lesions
Five studies focused on the detection of concurrent additional lesions in the affected breast apart from the index lesion only visible by MRI [22, 23, 31, 33, 36]. In 44 of 146 patients, additional malignant lesions were found [Q 7.20 (p = 0.13), I2 = 44%]. Additional malignant findings only visible on MRI were present in 32% of cases (95% CI 22–44%). The results of the individual studies are presented in Table 6.
Table 6Additional malignant findings in the ipsilateral breast by MRIAuthorsNumber of patientsNumber of additional findingsWeinstein et al. [36]187Quan et al. [31]5111Schelfout et al. [33]269Diekmann et al. [22]179Fabre Demard et al. [23]348Total14644Meta-analysis (%)10032
Eight studies, presented in Table 7, reported on findings in the contralateral breast [19, 22–25, 28, 31, 40]. In 12 of 206 patients, unexpected contralateral cancer was discovered exclusively by MRI [Q 2.28 (p = 0.94), I2 = 0%]. Cases where contralateral cancer was also visible on mammography and/or ultrasound are excluded. Contralateral carcinoma only visible by MRI was present in 7% of patients (95% CI 4–12%).
Table 7Additional findings in the contralateral breast by MRIAuthorsNumber of patientsNumber of contralateral findingsFrancis et al. [24]220Munot et al. [28]202Quan et al. [31]535Diekmann et al. [22]171Boetes et al. [19]342Berg et al. [40]150Kepple et al. [25]140Fabre Demard et al. [23]342Total20612Meta-analysis (%)1007
Effect on surgical treatment
Six studies explicitly stated the effect of MRI on the surgical treatment of their patients [23, 26, 28, 31, 32, 39]. In 160 patients with ILC, a total of 44 changes in surgical management occurred [Q 7.90 (p = 0.16), I2 = 37%]. Overall, MRI changed the surgical management in 28.3% of cases (95% CI 20–39%). In 24 cases BCT was changed to mastectomy. In nine cases a wider local excision was performed. In the remaining 11 cases the type of change was not further described. Forty-one of 44 changes in surgical management were retrospectively judged necessary based on pathologic findings [Q 1.24 (p = 0.94), I2 = 0%]. Therefore, 88% of all changes were correct (95% CI 75–95%). In three cases the change in management was retrospectively judged unnecessary based on pathology. The data of the individual studies are presented in Table 8.
Table 8Changes in surgical management based solely on MRI findingsAuthorsNumber of patientsNumber of changesCorrect changesIncorrect changesCorrect wider excisionIncorrect wider excisionCorrect mastectomyIncorrect mastectomyRodenko et al. [32]2087171Munot et al. [28]20333Kneeshaw et al. [26]215514Quan et al. [31]51111156Bedrosian et al. [39]241192NANANANAFabre Demard et al. [23]246633Total160444139231Number of changes (%)10028.3Correct changes (%)10088Number of changes and correct changes show the result of meta-analysesNA not available
Rodenko et al. [32] and Kneeshaw et al. [26] both reported one further unnecessary mastectomy based on MRI outcomes. However, these mastectomies would also have been performed based on the mammography findings and are therefore not only due to the MRI. Berg et al. [40] also reported that findings on MRI in 12 patients with ILC would have resulted in two unnecessary mastectomies. However, mastectomies were also indicated according to the ultrasound report. Nonetheless they based their treatment on the mammograms only and therefore these mastectomies were not performed.
Discussion
Studies and quality analysis
We included 18 studies, but the highest number of studies that could be used to answer a specific endpoint was 8 (sensitivity and contralateral findings). Strong evidence is therefore lacking and this review is thus a clear call for more substantial research in this area. The overall study quality of all studies is, according to the QUADAS score, reasonably high (lowest score = 9/14). However, this tool does not include the study size in the analysis, which was generally low. The tool places a strong emphasis on the relation of the test to the reference standard (typical for observational studies). In all studies, the reference standard was pathology and therefore always acceptable as gold standard. However, the test results (in this case the MRI reports), were never shielded from the pathologist who performed the pathologic evaluation. In studies that were performed to evaluate the visual characteristics of ILC on MRI a thorough description of the pathological examination was, deservedly so, not included [23, 30, 32, 33, 36, 38, 41]. These studies thus scored a little lower. There are some other drawbacks that must be considered and that are not included in the QUADAS score. Firstly, all but 2 of the included 18 studies were retrospective in nature, and secondly, the applied MRI protocols were largely heterogeneous (see Table 1). However, the presented data were extracted from studies that made use of the various standards in MRI of the breast of the last decade and therefore give a reasonable overview of the overall capability of MRI in ILC imaging in this period.
Sensitivity
The sensitivity of physical examination and conventional imaging for ILC of the breast is not optimal. The sensitivity of physical examination for ILC ranges between 65 and 98% [10, 45–47], with usually over 50% of patients presenting with palpable abnormalities.
The sensitivity of mammography for ILC (BI-RADS 3 or higher) ranges between 81 and 92% in literature [10, 45–51]. In a recent study that evaluated intra- and interobserver variability, sensitivity even ranged from 88 to 98% [52], which could be regarded as sufficient. However, ILC often do not appear as a malignant lesion on mammography; approximately 30% is classified as equivocal and sensitivity is then approximately 57–59% [51].
The overall sensitivity of mammography in the current analysis appears lower than findings in the literature on mammography in ILC. However, equivocal findings may have been classified as undetected lesions in some studies resulting in the overall lower results. Nevertheless, the sensitivities of only 34% found by Berg et al. [40], and 50% found by Munot et al. [28] are on the lower end of the spectrum. Munot et al. [28] did not state which views constituted their mammograms, while Berg et al. [40] made craniocaudal, mediolateral and spot-compression views on a standard mammography machine, which we regard as common practice. A possible explanation for the poor results in the study by Berg et al. [40] may be that they defined an ILC as a focus of tumor, thereby allowing more tumors to be present in one breast, whereas other authors defined this as multifocal or multicentric tumors and thus as detected when at least one lesion was visible on mammography.
In literature, the reported sensitivity of ultrasound for ILC ranges between 68 and 98% [47, 53–58]. As this range is comparable to the range found in the present evaluation, we are of the opinion that an overall sensitivity of 83% is accurate. However, application of newer high-frequency ultrasound transducers may improve sensitivity. Initial series using 7.5 MHz transducers show sensitivities of 68% [47] and 78% [56], whereas series that used 10–13 MHz transducers report sensitivities up to 98% [57, 59].
Contrast-enhanced MRI is nowadays widely accepted as the most sensitive modality for detection of malignancy of the breast. Early reports on overall sensitivity of MRI for breast lesions range from 93 to 100% [13, 60–63]. Thus, the sensitivity of MRI found for ILC in the studies presented herein and the overall sensitivity of 93.3% calculated from these studies are not different from those known for malignancy in the breast in general. The relatively low heterogeneity of all studies describing lesion detection as well as detection of additional lesions in the ipsi- and contralateral breast show that the applied MRI technique only has a minor impact on the ability of MRI to detect lesions.
The overall sensitivity could even be increased to 96% (95% CI 92–98%) if an early study is excluded from the analysis [34]. This study reported a sensitivity for ILC of only 83%, a discrepancy that may well be explained by the fact that the slice thickness in this study was 4.2 mm, thicker than in any of the other presented studies, which could have had a negative impact on sensitivity. Moreover, 15 of 23 patients in their series were scanned with a FLASH 3D sequence with TR 8.4/TE 3.0, resulting in image acquisition with a phase-shift of water and fat, which might have further decreased their sensitivity, although this was not apparent from their data.
It must be taken into account that the acquired sensitivity in all studies was achieved in cases where prior knowledge of the existence of ILC was present. Mostly because of the retrospective nature of the presented studies, but also because the two prospective studies both included their patients on the basis of histological proof of invasive (lobular) carcinoma by core biopsy. It is therefore not possible to formulate conclusions on the sensitivity of MRI for ILC prior to biopsy. In a large multicenter trial by Bluemke et al. [64] overall sensitivity for invasive cancer prior to biopsy was 91%, thus it might be expected that sensitivity for ILC prior to biopsy is also slightly lower. However, in most cases the indication for MRI is assessment of disease extent because of inconclusive findings at mammography or ultrasound. In conclusion, the sensitivity of MRI for ILC is higher than that achieved by any other modality, in direct comparison and validated by literature, and is equal to the overall sensitivity of MRI for malignant lesions of the breast. Only modern ultrasound examinations seem to have the ability to approach the performance of MRI in the detection of ILC [57].
Morphology
The morphologic appearance of ILC on MRI ranged from 69% non-mass-like lesions to 95% mass-like lesions, thereby raising questions concerning the amount of heterogeneity in the description of morphology of lesions by radiologists. In fact, the general agreement on the description of lesion type according to the BI-RADS lexicon is only moderate [14, 65]. In the current analysis, this is even further complicated because most authors did not specifically use the BI-RADS lexicon. Additionally, differences in scan techniques may have further affected the appearance of the lesion. However, in keeping with the above, the classification of lesion type is also highly variable on mammography, where the incidence of mass lesions ranges from 32 to 78% [10, 45, 46, 48, 50, 51, 55].
The vast majority of the mass-like lesions described on MRI are irregular or spicular lesions. The eight patients with a dominant mass surrounded by multiple enhancing foci, as described by Schelfout et al. [33], may present noncontiguous foci of disease without visible spiculae due to the absence of desmoplastic reaction, which is a well-known histopathological presentation [8]. In all series only one round mass was described [38], suggesting this to be a very rare presentation for ILC. This is consistent with findings in mammography by Le Gal et al. [10], who described a round mass in only 2% of all patients where a mass was present (4/174) while the remainder was either classified as a spicular mass (54%) or poorly defined mass (44%).
Mammographic findings would therefore appear to correlate well with MRI findings. However, only one study allows direct comparison [33]: of all lesions visible in this study on both mammography and MRI, 78% (18/23) were classified as mass-like by MRI, while only 48% (11/23) were classified as mass-like by mammography. Six masses on MRI were visible as architectural distortion on mammography and two as asymmetric density. In one case a lesion described as spicular mass on mammography was visible on MRI as multiple enhancing foci with interconnecting enhancing strands.
Non-mass-like ILC in mammography are typically described as architectural distortion or asymmetric density. In some cases microcalcifications are present, although these are often related to concurrent surrounding DCIS, sclerosing adenosis or fibrotic changes and might thus not be related to the presence of ILC [45, 51, 55]. The descriptors currently used for non-mass-like lesions on MRI are diverse and include various types of abnormal enhancement, such as regional, ductal, segmental, and diffuse enhancement. According to Qayyum et al. [30] the morphologic description of ILC on MRI has a good correlation to histopathologic findings. The non-mass-like presentation might specifically occur in cases where ILC grow in the classic pattern with cells arranged in a linear fashion along the ductuli.
It may thus be concluded that the appearance of most ILC on MRI and mammography is similar: most ILC are mass-lesions that have clear malignant properties. However, the more diffuse growing tumors are characterized by areas of unexpected enhancement and are more difficult to recognize. In a number of cases where no clear mass is visible on mammography, a mass-like lesion may be found on MRI [33].
Kinetics
The relatively late contrast enhancement of ILC apparent in all studies presented here and mirrored by the relatively low values of K21 and EFP in the studies by Qayyum et al. [30] and Yeh et al. [38] must be taken into account when evaluating ILC. Standard subtraction images, generated from the pre-contrast and the first or second post-contrast acquisitions may be inconclusive as maximum enhancement is not achieved at this point in time and the lesion is thus not yet clearly visible. In fact, false-negative MRI in cases of ILC is usually contributed to inadequate enhancement of the tumor [26, 35, 66]. The diffuse and often slow tumor growth, not requiring extensive neovascularization, may partly cause this difficult visualization [1, 67, 68]. This is also clear from the relatively lower amount of vascular endothelial growth factor found in tumors with a lobular histology, which might also indicate a different signaling pathway in the formation of neovascular vessels in ILC, resulting in more mature and thus less leaky capillaries [69], with consequently diminished or absent contrast enhancement.
Correlation
In the herein presented studies overestimation of lesion extent by mammography is rare, yet underestimation is more rule than exception. This is also confirmed by studies that specifically deal with mammography in cases of ILC. Yeatman et al. [5] showed that mammography underestimated ILC by a mean of 12 mm. Uchiyama et al. [51] reported 56% of all visible ILC on mammography to be underestimated and Veltman et al. [52] showed 35–37% of all ILC to be mammographically understaged.
Ultrasound also tends to underestimate tumor size in the studies presented here. This finding is underlined by Tresserra et al. [70] and more recently by Watermann et al. [71], who documented a structural underestimation of 5.4 ± 12.2 mm in cases of ILC versus 1.4 ± 12.0 mm for cases of IDC. This might be partly due to the observation that US tends to underestimate larger tumors more than smaller tumors and low grade tumors more than high grade [70], consistent with the finding that ILC usually presents with slightly larger and less aggressive tumors [1, 5, 67, 72]. The current analysis shows that there is good correlation of tumor size measured on MRI compared to pathology. The various studies presented only moderately heterogeneous results.
In most cases MRI outperforms mammography and ultrasound in the assessment of disease extent. Most tumors are correctly classified as uni- or multifocal and multicentric disease is only seldom overestimated [19, 32].
Additional lesions and effect on surgical treatment
Especially important in this analysis is the detection of additional lesions apart from the index lesion in patients with ILC. The co-existence of other invasive malignant lesions apart from the index lesion in the ipsilateral breast in 32% of patients only visualized by MRI is high. Moreover, the detection of contralateral cancer in another 7% of patients by MRI only, seems to make MRI indispensable. These findings are confirmed by the rate of change in treatment of the ipsilateral breast based on MRI. The fact that change in treatment was considered correct, as verified by pathologic findings in the specimen, in 88% of cases shows that ILC is often more extensive than appreciated on conventional imaging.
However, various authors have shown that there is no significant difference in disease free survival (DFS) or overall survival (OS) after BCT or mastectomy in patients with breast cancer. Although some authors report more local recurrence in patients with ILC after BCT [2, 73], most authors showed that there is no difference in DFS or OS after BCT in ILC versus IDC [74, 75]. On the other hand, Yeatman et al. [5] reported a higher rate of conversion from lumpectomy to mastectomy in ILC compared to IDC (17.5% versus 6.9%). More recently, Molland et al. [68] reported similar findings (37.2% versus 22.4%). Hussien et al. [2] even reported failure of BCT in patients with ILC in 63% (34/54) of patients, resulting in conversion to mastectomy in 76% of failures (26/34). However, a very recent study by Morrow et al. [76] showed that BCT did not fail more often in patients with ILC when corrected for age and tumor size, although they still observed a trend of more excisions in patients with ILC [OR 1.58 (0.89–2.79), p = 0.12].
To date, there is no evidence suggesting increase in survival for patients with ILC due to the performance of MRI. What is then the added value of MRI? The rate of recurrence 10 years after BCT followed by radiotherapy is between 7 and 18% and is not significantly different from the rate of recurrence in case of IDC [77, 78]. However, in view of the MRI findings (additional malignant lesions in 32% of patients), we can only conclude that in a large number of patients with ILC, surgery is not curative but merely debulking. As recurrence rates are fortunately much lower, we must assume that curative treatment is to be expected from adjuvant therapy. Unfortunately, because there is no possibility to determine which additional findings will respond to adjuvant therapy, the detection of additional lesions on MRI currently still requires a change of treatment when malignancy has been proven by core biopsy. This may further reduce the rate of recurrence in patients with ILC and may even improve survival. However, this requires confirmation in future studies.
Conclusion
Magnetic resonance imaging has a high sensitivity for ILC, not achieved by other imaging modalities. Therefore MRI is helpful in cases where conventional imaging is inconclusive. Morphology is often mass-like and a typical ILC presents as an irregular or spiculated mass. However, asymmetric enhancement that can be ductal, segmental, regional, or diffuse in nature may be the only sign of tumor. MRI measures disease extent with a high reliability. Although underestimation and overestimation of lesion size by MRI still occurs, it is more accurate than size determination by other modalities, indicating often more extensive tumor burden than expected.
The underestimation by other imaging modalities results in more failure of BCT, more re-excisions and more conversion to mastectomy in series where MRI is not used. MRI has an effect on surgical management in that when used to assess disease extent, surgical management was changed in 28.3% of which 88% were judged necessary based on pathology. Larger series of patients are required to confirm the findings of this review; especially evaluation of tumor morphology and dynamic profile seems feasible.