The purpose of this article is to review the status and limitations of anatomic tumor response metrics including the World Health Organization (WHO) criteria, the Response Evaluation Criteria in Solid Tumors (RECIST), and RECIST 1.1. This article also reviews qualitative and quantitative approaches to metabolic tumor response assessment with 18 F-FDG PET and proposes a draft framework for PET Response Criteria in Solid Tumors (PERCIST), version 1.0. Methods: PubMed searches, including searches for the terms RECIST, positron, WHO, FDG, cancer (in-cluding specific types), treatment response, region of interest, and derivative references, were performed. Abstracts and articles judged most relevant to the goals of this report were reviewed with emphasis on limitations and strengths of the anatomic and PET approaches to treatment response assessment. On the basis of these data and the authors' experience, draft criteria were formulated for PET tumor response to treatment. Results: Approximately 3,000 potentially relevant references were screened. Anatomic imaging alone using standard WHO, RECIST, and RECIST 1.1 criteria is widely applied but still has limitations in response assessments. For example, despite effective treatment, changes in tumor size can be minimal in tumors such as lymphomas, sarcoma, hepatomas, mesothelioma, and gastrointestinal stromal tumor. CT tumor density, contrast enhancement, or MRI characteristics appear more informative than size but are not yet routinely applied. RECIST criteria may show progression of tumor more slowly than WHO criteria. RECIST 1.1 criteria (assessing a maximum of 5 tumor foci, vs. 10 in RECIST) result in a higher complete response rate than the original RECIST criteria, at least in lymph nodes. Variability appears greater in assessing progression than in assessing response. Qualitative and quantitative approaches to 18 F-FDG PET response assessment have been applied and require a consistent PET methodology to allow quantitative assessments. Statistically significant changes in tumor standardized uptake value (SUV) occur in careful test-retest studies of high-SUV tumors, with a change of 20% in SUV of a region 1 cm or larger in diameter; however, medically relevant beneficial changes are often associated with a 30% or greater decline. The more extensive the therapy, the greater the decline in SUV with most effective treatments. Important components of the proposed PERCIST criteria include assessing normal reference tissue values in a 3-cm-diameter region of interest in the liver, using a consistent PET protocol, using a fixed small region of interest about 1 cm 3 in volume (1.2-cm diameter) in the most active region of metabolically active tumors to minimize statistical variability, assessing tumor size, treating SUV lean measurements in the 1 (up to 5 optional) most metabolically active tumor focus as a continuous variable, requiring a 30% decline in SUV for ''response,'' and deferring to RECIST 1.1 in cases that do not have 18 F-FDG avidity or are technically unsuitable. Criteria to define progression of tumor-absent new lesions are uncertain but are proposed. Conclusion: Anatomic imaging alone using standard WHO, RECIST, and RECIST 1.1 criteria have limitations, particularly in assessing the activity of newer cancer therapies that stabilize disease, whereas 18 F-FDG PET appears particularly valuable in such cases. The proposed PERCIST 1.0 criteria should serve as a starting point for use in clinical trials and in structured quantitative clinical reporting. Undoubtedly, subsequent revisions and enhancements will be required as validation studies are undertaken in varying diseases and treatments. Key Words: molecular imaging; oncology; PET/CT; anatomic imaging; RECIST; response criteria; SUV; treatment monitoring Cancer will soon become the most common cause of death worldwide. For many common cancers, treatment of disseminated disease is often noncurative, toxic, and costly. Treatments prolonging survival by a few weeks and causing tumor shrinkage in only about 10%215% of patients are in widespread use. Clearly, we need more effective therapies. With relatively low response rates in individual cancer patients, imaging plays a daily clinical role in determining whether to continue, change, or abandon treatment. Imaging is expected to have a major role not only in the individual patient but in clinical trials designed to help select which new therapies should be advanced to progressively larger and more expensive clinical trials. The ultimate goal of new cancer therapies is cure. This goal, although sometimes achieved in hematologic malignancies, has rarely been achieved in disseminated solid cancers. A good cancer treatment should ideally prolong survival while preserving a high quality of life cost-effectively. To demonstrate prolonged survival in a clinical trial in some more slowly progressing cancers can take 5-10 y or longer. Such trials are expensive, not only in cost but in time. The typical development pathway for cancer therapeutic drugs includes an evolution from phase I to phase II and to phase III clinical trials. In phase I trials, toxicity of the agent is typically assessed to determine what dose is appropriate for subsequent trials. Typically, the statistical power of phase I drug trials is inadequate to assess antitumor efficacy. In phase II trials, evidence of antitumor activity is obtained. Phase II trials can be done in several ways. One approach is to examine tumor response rate versus a historical control population treated with an established drug. New drugs with a low response rate are typically not moved forward to advanced clinical testing under such a paradigm. In such trials, tumor response has nearly always been determined anatomically. An alternative approach is to use a typically larger sample size and have a randomized phase II trial, in which the new treatment is given in one treatment arm and compared with a standard treatment (1-4). Once drug activity is shown-or suggested-in phase II, phase III trials are typically performed. Phase III trials are larger and typically have a control arm treated with a standard therapy. Not all phase III trials are successful, but all are costly. Determining which innovative cancer therapeutics should be advanced to pivotal large phase III trials can be unacceptably delayed if survival is the sole endpoint for efficacy. Survival trials can also be complicated by deaths due to nonmalignant causes, especially in older patients in whom comorbidities are common. Additional complexities can include patients who progress on a clinical trial but who go on to have one of several nonrandomly distributed follow-up therapies-which can confound survival outcomes. There is great interest in surrogate metrics for survival after investigational cancer treatments, such as response rate, time to tumor progression, or progression-free survival (5). Changes in tumor size after treatment are often, but not invariably, related to duration of survival. A variety of approaches to measuring response rate have been developed, beginning with the original reports by Moertel on physical examination in 1976 and continuing to the subsequent World Health Organization (WHO) criteria (1979), Response Evaluation Criteria in Solid Tumors (RECIST) (2000), and RECIST 1.1 (2009) (6-8). Response rate typically refers to how often a tumor shrinks anatomically and has been defined in several ways. Not uncommonly, complete response, partial response, stable disease, and progressive disease are defined as in the WHO and RECIST criteria (Tables 1-3) (8). This type of classification divides intrinsically continuous data (tumor size) into 4 bins, losing statistical power for ease of nomenclature and convenience (9). The time to tumor progression and progression-free survival examine when the disease recurs or progresses (including death for progression-free survival). Because cancers typically grow before they cause death, these markers provide readouts of tumor growth often considerably before the patients die of tumor. These metrics have been shown in some, but not all, cancers to be predictive of survival. Notable exceptions have been identified in several metaanalyses (6-9). Response rates must be viewed with some caution when one is trying to predict outcomes in newer cancer therapies that may be more cytostatic than cytocidal. With such newer treatments, lack of progression may be associated with a good improvement in outcome, even in the absence of major shrinkage of tumors as evidenced by partial response or complete response (2,3). To determine lack of progression by changes in tumor size requires regular and systematic assessments of tumor burden. Newer metrics such as PET may be more informative (10). Surrogate endpoints for survival should provide earlier, hopefully correct, answers about the efficacy of treatment 67 Ga scan midway through or at the end of treatment of patients with diffuse large cell lymphoma predicted a poor outcome in comparison to patients whose scans had normalized, even if residual masses were over 10 cm in size (11). Using planar g-camera imaging and SPECT of 67 Ga citrate, Israel, Front, et al. from Haifa showed the utility of 67 Ga scanning for monitoring response and showed that CT anatomic imaging was insufficient to reliably predict disease-free survival or survival in patients with Hodgkin disease or non-Hodgkin lymphoma after completing therapy (12) (13) (14) . The poor predictive ability of CT was because residual masses on CT commonly were found to represent not viable tumor but rather scarring in both Hodgkin disease and non-Hodgkin lymphoma. 67 Ga results, qualitatively reported as positive or negative, were significantly predictive of outcome, with a negative 67 Ga scan predicting a favorable outcome (12, 14, 15) . A positive or negative 67 Ga scan after 1 cycle of treatment was also shown to be predictive of eventual response to therapy in both Hodgkin disease and non-Hodgkin lymphoma (12) (13) (14) . Although the prognostic value of 67 Ga in these settings is stronger than that of CT, 67 Ga imaging has now been substantially supplanted by PET using 18 F-FDG. Di Chiro et al. demonstrated that a negative 18 F-FDG PET scan could help distinguish brain tumor necrosis from viable tumor at the end of therapy, despite the overlapping anatomic appearance of brain tumor and necrosis on CT (16, 17) . Planar imaging and SPECT with 18 F-FDG showed that breast cancers and lymphomas had qualitative declines in tracer uptake with effective treatment (18, 19) . Quantitative 18 F-FDG PET was introduced for the early sequential monitoring of tumor response of breast cancer in 1993 (20). Since then, there has been growing interest in using 18 F-FDG PET to quickly assess whether a tumor is-or is not-responding to therapy (20). In the initial report, women with newly diagnosed breast cancer had a rapid and significant decline in standardized uptake value (SUV), influx rate for 18 F-FDG determined by Patlak analysis (influx constant K i ), and estimated phosphorylation rate of 18 F-FDG to FDG-6 phosphate (k 3 ) within 8 d of the start of effective treatment. These parameters continued to decline with each progressive treatment in the responding patients, antedating changes in tumor size. By contrast, the nonresponding patients did not have a significant decline in their SUV. Since that report, there have been many others in a wide range of tumors (21, 22) . Abundant data now exist that PET is a useful tool for response assessment in a variety of diseases, at the end of treatment, at mid treatment, and when performed soon after treatment is initiated. Quantitative nonanatomic imaging approaches can be used as a biomarker of cancer response to predict or assess the efficacy of treatments (23) (24) (25) . PET with 18 F-FDG appears to be one of the most powerful biomarkers introduced to date for clinical trials and for individual patients. An evolving personalized cancer management paradigm is one in which a tumor biopsy is used to produce a genetic or epigenetic profile to help select the initial treatment and enrich for response. A baseline PET scan and a PET scan after 1 or 2 cycles of treatment could then be performed to determine whether the treatment was indeed effective in that specific tumor and patient (26, 27) . Rapid readouts of treatment effect and prompt shifting of patients from ineffective to effective therapies, as well as quick aban- donment of ineffective therapies, is an extremely attractive possibility for personalized health care. Use of these socalled response-adaptive or risk-adaptive treatment approaches is expected to grow (28). Indeed, it is probable that the integration of imaging in which the exact effects of the therapeutic agent on a specific tumor in a specific patient are imaged will be much more potent than are predictions of response based on more traditional established prognostic information (29). In the past 20 years, there has been remarkable growth in the use of 18 F-FDG PET in cancer imaging, with PET now being used increasingly routinely in the diagnosis, staging, restaging, and treatment monitoring of many cancers. Despite the rapid integration of PET with 18 F-FDG into clinical practice in individual patients, there has been relatively little systematic integration of PET into clinical trials of new cancer treatments. Such clinical trials and the regulatory agencies evaluating them rely mainly on anatomic approaches to assess response and progression. Part of the delay in integrating PET into phase I-III clinical trials as a response metric is due to the variability in study performance across centers and the lack of uniformly accepted, or practiced, treatment response metrics for PET. Recently, standardized approaches to the performance of PET and to machine calibrations have been articulated (30, 31) . Further, qualitative dichotomous (positive/negative) 18 F-FDG PET readings at the end of treatment have recently been integrated into lymphoma response assessment in the IWC 1 PET criteria (32, 33) . Given the clinical importance and quantitative nature of PET, it is important to have methods to allow inclusion of PET response criteria into clinical trials, as well. This article attempts to address the status and limitations of currently applied anatomic tumor response metrics, including WHO, RECIST, and the new RECIST 1.1 criteria. It then reviews the qualitative and quantitative approaches used to date in PET treatment response assessment, including the IWC 1 PET criteria for lymphoma and the European Organization for Research and Treatment of Cancer (EORTC) criteria for PET. Finally, it proposes, on the basis of the literature reviewed and the authors' experience, a draft framework for PET Response Criteria in Solid Tumors (PERCIST, version 1.0). These criteria may be useful in future multicenter trials and may serve as a starting point for further refinements of quantitative PET response. They may also provide some guidance for clinical quantitative structured reporting on individual patients. Selected articles obtained using Internet search tools, including PubMed and syllabi from meetings (e.g., Clinical PET and PET/CT syllabus, Radiological Society of North America, 2007), were identified. Publications resulting from database searches and including the main search terms RECIST, positron, FDG, ROI (region of interest), cancer, lymphoma, PET, WHO, and treatment response were included. The search strategy for relevant 18 F-FDG PET studies articulated by Mijnhout et al. was also applied (34,35). These were augmented by key references from those studies, as well as the authors' own experience with PET assessments of treatment response, informal discussions with experts on PET treatment response assessment, and pilot evaluations of clinical data from the authors' clinical practice. Limitations and strengths of the anatomic and functional methods to assess treatment response were evaluated with special attention to studies that had applied qualitative or quantitative imaging metrics, had determined the precision of the method, and had histologic correlate or outcome data available. On the basis of these data, proposed treatment response criteria including PET were formulated, drawing from both prior anatomic models (notably WHO, RECIST, and RECIST 1.1) and the EORTC PET response draft criteria (36). These conclusions were based on a consensus approach among the 4 authors. Thus, a systematic review and a limited Delphilike approach augmented by key data were undertaken to reach consensus in a small group. For demonstration purposes, 18 F-FDG PET scans obtained at our institution on 1 of 2 GE Healthcare PET/CT scanners were analyzed with several tools, including a tool for response assessment. Searching for FDG and SUV produced 1,012 references on January 7, 2009. The abstracts of many were reviewed by the authors, and the seemingly most relevant full articles were examined in detail. Additional references were identified from the reference lists of these articles. Given the large extent of the available literature and the limited time and personnel available to produce this initial review, some major references may not have been identified. RESULTS The results of this review are presented in 3 main areas: anatomic response criteria, PET metabolic response criteria, and rationale for the proposed PERCIST criteria. A scientific approach to assessing cancer treatment response was notably applied by Moertel and Hanley (6). They evaluated the consistency of assessment of tumor size by palpation among 16 experienced oncologists using 12 simulated masses and routine clinical examination skills. Two pairs of the 12 masses were identical in size. When a 50% reduction in tumor dimensions (perpendicular diam- eters) was taken as a significant reduction in size, the frequency of detecting a tumor response was about 7%28% because of chance differences in measurement values. If a 25% reduction in the product of the perpendicular diameters of the tumors was considered a response, an unacceptably high false tumor reduction occurred 19%225% of the time because of variability in the measurement technique. This study quantified for the first time the variability in determinations of tumor size by experts due to measurement error using metrics available at that time. Moertel and Hanley thus recommended that a true tumor response would need to be greater than 50% so as to avoid these random responses due to measurement variance. As measurement tools are developed, a key question is their intrinsic variability from study to study. Lower variability (i.e., higher precision) means that smaller treatmentinduced effects in tumor characteristics can be identified. This does not necessarily mean, however, that the treatmentinduced changes identified are medically relevant. Moertel and Hanley's work and the development of a variety of promising anticancer therapies, mainly cytotoxics, in the 1960s and 1970s brought about a clear need for standardization of response criteria. Because CT of the body was not in widespread use until the early 1980s, most tumor measurements were obtained by palpation or chest radiographs. In 1979, WHO attempted to standardize treatment response assessment by publishing a handbook of criteria for solid tumor response (7). The proposed WHO methods included determining the product of the bidimensional measurement of tumors (i.e., greatest perpendicular dimensions), summing these dimensions over all tumors, and then categorizing changes in these summed products as follows: complete response-tumor has disappeared for at least 4 wk; partial response-50% or greater reduction in sum of tumor size products from baseline confirmed at 4 wk; no change-neither partial response nor complete response nor progressive disease; and progressive disease-at least a 25% increase in tumor size in one or more lesions, with no complete response, partial response, or stable disease documented before increase in size, or development of new tumor sites. Reviewing the data of Moertel and Hanley, one would be concerned that the progressive disease category in WHO might be easy to achieve by chance changes in measurement (i.e., a 25% increase in the product of 2 measurements could occur with an approximately 11% increase in each dimension). In addition, the WHO criteria were not explicit on such factors as how many tumor foci should be measured, how small a lesion could be measured, and how progression should be defined. Thus, despite efforts at standardization, the WHO criteria did not fully standardize response assessment. The WHO criteria are still in use in some trials and are the criteria used to define clinical response rates in many trials from the past 2 decadeswhich are important reference studies. Although not as commonly used at present, familiarity with the WHO response criteria is essential for comparison with more recent studies using RECIST, especially as relates to the issue of when tumors progress. The WHO criteria are summarized in Table 3 . The RESIST criteria were published in 2000 and resulted from the recognition of some limitations of the WHO criteria (8). The criteria were developed as a primary endpoint for trials assessing tumor response. In addition, between the time of development of the WHO criteria and development of RECIST, cross-sectional imaging with CT and MRI entered the practice of oncology. RECIST specified the number of target lesions to assess (up to 10), though it did not give substantial guidance on how they were to be selected, except that there should not be more than 5 per organ. RECIST assumed that transaxial imaging would be performed, most commonly with CT, and specified that only the single longest dimension of the tumor should be mentioned. Thus, RECIST implemented a unidimensional measurement of the long axis of tumors. RECIST also clearly stated that the sum of these unidimensional measurements was to be used as the metric for determining response. RECIST also specified the minimum size of the lesions to be assessed, typically 1 cm using modern CT with 5-mm or thinner slices. Lesions of adequate size for measurement are described as ''measurable.'' There are also designations of ''target'' and ''nontarget'' lesions (Tables 1-3 ). All target lesions are measurable. Some nontarget lesions are measurable. Both can contribute to disease progression and to complete response (Tables 1-3) . The RECIST categories for response include complete response-disappearance of all tumor foci for at least 4 wk; partial response-a decline of at least 30% in tumor diameters for at least 4 wk; stable disease-neither partial response nor progressive disease; and progressive disease-at least a 20% increase in the sum of all tumor diameters from the lowest tumor size. A 20% increase in tumor dimensions results in a 44% increase in the bidimensional product, substantially greater than the WHO progression criterion of 25%. One would predict progression to be later, and possibly less frequent, using RECIST than using WHO. This has been the case, and earlier progression is seen in about 7% of patients using WHO versus RECIST (8). Thus, time to disease progression can be shorter with WHO than with RECIST (for the identical patient data). When progression is due to new tumor foci (which occurs about half the time in some reports), the 2 methods would be expected to be concordant in indicating progression of disease (8). Overall, quite good concordance was seen with the 2 methods. The RECIST and WHO criteria are contrasted in Table 3. RECIST TO PERCIST: PET TUMOR RESPONSE • Wahl et al. 127S Another consideration for anatomic and functional imaging is that many of the changes in response, from partial response to complete response, or from stable disease to partial response, are at the border zones between response groups (i.e., 48% vs. 52% change in tumor size in WHO, or 28%232% change in RECIST (nonresponse vs. partial response, for example). These border zones are frankly quite artificial, as changes in tumor size occur on a continuum. This is why continuous, so-called waterfall, plots of fractional shrinkage or growth of tumors are becoming increasingly popular as a means of graphically displaying tumor response data (1,2,10). It is to avoid such problems that PERCIST includes providing a specific percentage reduction in the SUV (SUV lean, or SUL) from baseline, as well as noting when the information is available-the number of weeks from the start of treatment. Therasse, Verweij, et al. recently reviewed the use of RECIST in about 60 papers and American Society of Clinical Oncology meeting abstracts (37,38). The expected delay in progression detection versus WHO was observed. In addition, recognition of challenges in certain pediatric tumors, unusually shaped tumors such as mesotheliomas, and tumors with a great deal of central necrosis or cystic changes, such as gastrointestinal stromal tumor (GIST), were noted. Overall, however, the authors believed that RECIST had been highly successful but that some improvements were needed. The RECIST group, which included representatives from, among others, the EORTC, the National Cancer Institute (NCI), the National Cancer Research Network, and industry, recently reported new response criteria for solid tumors, RECIST 1.1 (39). This version of RECIST, reported in January 2009, includes several updates and modifications to refine the prior RECIST criteria. Notably, RECIST 1.1 made use of a data warehouse of images and outcomes provided from a variety of clinical trials, allowing assessment of changes in tumor size based on several formulae. Although the original RECIST included size measurements of up to 10 lesions, with a maximum of 5 for any single organ; simulations in RECIST 1.1 assessed the use of 1, 2, 3, or 5 target lesions, versus the original 10. They found strong agreement in response classifications using fewer than 10 lesions, even using just 1 lesion, but even better concordance when 5 lesions were used. In randomized studies in which tumor progression is the major concern, RECIST 1.1 suggests that just 3 lesions may be used, not 5. Thus, there are potentially 50%270% fewer tumor measurements with RECIST 1.1 than with RECIST. RECIST 1.1 also suggests that the largest lesions be used for response, as long as they are distinctly capable of being measured. RECIST 1.1 also dealt with lymph nodes differently than did the original RECIST criteria. In the original RECIST, the longest axis of lymph nodes was to be measured and the lymph nodes had to disappear completely to secure a complete response. In RECIST 1.1, nonnodal lesions had to be 1 cm in size or larger (long axis) to be considered measurable. By contrast, in RECIST 1.1, the short axis of lymph nodes is measured; short-axis lengths greater than 1.5 cm are considered suitable for measurement, and nodes with short axes under 1 cm are considered normal. If a node disappears nearly completely and cannot be precisely measured, it is assigned a value of 5 mm. If totally absent, it becomes 0 mm. The difference between RECIST and RECIST 1.1 in lymph nodes is that the lymph node size can decline to greater than 0 and still be considered a complete response. Thus, with RECIST 1.1, especially in diseases in which lymph nodes represent a significant fraction of the total tumor burden, criteria for a complete response are less stringent than with the original RECIST. In the simulation data used in the RECIST 1.1 study, if nodal disease predominated, 23% of cases would move from partial response to complete response, whereas about 10% would move from partial response to stable disease. It should be noted that short-axis nodal diameter is added to long axis of other tumors to result in an overall tumor burden assessment in measurable lesions. This reclassification to an increased complete response rate for node-dominant disease is a major change and may be controversial as regards comparing RECIST with RECIST 1.1. The overall definition of progressive disease also changed in RECIST 1.1 by requiring an absolute increase in the sum of the tumor dimensions of at least 5 mm. This requirement prevents a minimal (,5-mm sum of tumor long axes) 20% increase from being categorized as progressive disease. The new RECIST 1.1 criteria offer guidance on what constitutes unequivocal progression of nonmeasurable or nontarget disease. There is also a brief discussion in RECIST 1.1 of the implications of a newly positive PET scan with 18 F-FDG in disease otherwise not considered to be progressing-the PET scan must be taken seriously as recurrence (39-41). Methods for classifying anatomic response in RECIST and RECIST 1.1 are detailed in Tables 1-3. Although these anatomic criteria may appear to be arcane, the RECIST criteria and now, quite likely, the RECIST 1.1 criteria are or will be used in virtually every clinical trial of new solid tumor therapeutics, as response is essentially always measured. Further, regulatory agencies have accepted RECIST as the de facto standard in response assessment for clinical trials in many countries. Familiarity with the implications of trials in which response is measured using the WHO, RECIST, and RECIST 1.1 criteria is essential, as they are not identical and do not produce identical results. Although RECIST has been used quite extensively for the past 8 y, some concerns about the method have not been fully addressed, even in RECIST 1.1. One issue is the fundamental statistical issue of reducing intrinsically continuous data on tumor size and tumor response to a series of 4 bins of response (i.e., complete response, partial response, THE JOURNAL OF NUCLEAR MEDICINE • Vol. 50 • No. 5 (Suppl) Limitations of RECIST in predicting response are noted clearly in the SHARP trial, in which sorafenib, an inhibitor of vascular endothelial growth factor receptor, plateletderived growth factor receptor, and Raf, was used in a randomized placebo-controlled trial in patients with hepatoma. In this trial of over 602 hepatoma patients who had not received previous therapy, only about 2% of the treated group and 1% of the control group had a partial response by RECIST, a figure that might lead one to conclude the drug to be inactive. However, the main endpoints of the trial were not tumor response but rather survival and progression-free survival. Because hepatomas have a bad prognosis and there is a high death rate, survival studies are feasible. At the time the study was ended, median overall survival was 10.7 mo in the sorafenib group and 7.9 mo in the placebo group (P , 0.001). The median time to radiologic progression was 5.5 mo in the sorafenib group and 2.8 mo in the placebo group (P , 0.001). Thus, clearly prolonged survival of about 3 mo was seen in this group of patients with advanced hepatocellular carcinoma treated with sorafenib, in comparison to patients treated with placebo. This substantial improvement in survival was associated with stable (not shrinking) anatomic disease (45). In hepatomas, alternative criteria to RECIST have been developed, referred to as the EASL (European Association for the Study of the Liver) criteria (44,50). These criteria rely on contrast enhancement patterns after vascular interventional therapies and appear superior to RECIST in this limited setting. Similarly, in mesotheliomas and pediatric tumors, modifications of RECIST dealing with the peculiarities of these tumors are in place (51-53,53A). An additional consideration for RECIST is that the most precise estimates are achieved when the same reader assesses the baseline and follow-up studies. More misclassifications and variance in response are seen when a different reader assesses the baseline and follow-up studies (54). Tumor size is a clearly important parameter, and there is some evidence that the more rapidly a tumor shrinks, the more likely it is that the response will be durable. For example, in lymphomas, patients whose tumors shrink the most rapidly are most likely to do well, and they may need less treatment (55). Estimates of tumor volume may prove more useful than 1-dimensional methods of tumor assessment in evaluating tumor response. Caution, however, is needed even with volumes; in neoadjuvant therapy of lung cancer, early changes in lung cancer volume were shown not to be predictive of histologic response (56). Tumor histologic status was well associated with changes in tumor volumes in neoadjuvant therapy of colorectal cancer, however (57). The use of continuous as opposed to discrete sets of response has been suggested. Such continuous assessments may then lend themselves well to randomized phase II trials in which the response metrics can be compared using more standard statistical testing than concordance or k-statistics (4). Lymphomas have had a somewhat different approach to response assessment than solid tumors. Briefly, residual or even bulky masses after therapy completion are frequent in both Hodgkin disease and non-Hodgkin lymphoma but correlate poorly with survival (58). Masses often do not regress completely after adequate (curative) treatment because of residual fibrosis and necrotic debris. The anatomic response categories of ''complete remission unconfirmed'' or ''clinical complete remission'' were created in recognition of the problem that, particularly in patients with lymphoma, anatomic response criteria often underestimate the chemotherapeutic effect (59). Patients with stable disease by conventional anatomic criteria may be cured. It has been demonstrated that adding PET to the posttherapy CT is especially useful in identifying which of these patients have achieved a satisfactory functional remission (60,61). The reader should be aware that there are well-established anatomic metrics of response in lymphoma (59). These metrics have recently been updated and modified to include PET at the end of therapy because of the limitations of anatomic imaging (Tables 4 and 5 ) (32, 33) . Although limited in their early assessment of treatment response, and somewhat variable in terms of outcome prediction, WHO, RECIST, and RECIST 1.1 are the standard anatomic response assessments currently accepted by most regulatory agencies, and RECIST, in particular, is in widespread use in clinical trials. By contrast, it is infrequent for these response criteria to be used in routine clinical practice. Although the criteria are quite detailed, variance in response occurs because of measurement errors and the inability of anatomic processes to quickly detect functional changes in tumors resulting from early effective treatment. The delayed readouts from anatomic imaging mean that it RECIST TO PERCIST: PET TUMOR RESPONSE • Wahl et al. 129S is difficult to quickly use anatomic imaging to modify treatments in individual patients. Functional imaging with PET offers major advantages. This entire supplement to The Journal of Nuclear Medicine is devoted to treatment response assessment using PET, mainly with 18 F-FDG, though other tracers have shown promise. The general principles for assessing treatment response with 18 F-FDG PET have been articulated elsewhere for several different disease types. Although a range of factors has been associated with 18 F-FDG uptake, there appears to be a rather strong relationship between 18 F-FDG uptake and cancer cell number in a substantial number of studies (62, 63) . Consequently, it is reasonable to expect that declines in tumor 18 F-FDG uptake would be seen with a loss of viable cancer cells and that increases in tumor glucose use and volume of tumor cells would be Figure 1 . PET is capable of detecting cancers that are smaller than depicted on CT. In addition, as a quantitative technique, the binary readings typically applied in clinical diagnosis do not need to be applied. As we have previously discussed in The Journal of Nuclear Medicine, cancers are usually not diagnosed until they reach a size of 10-100 g, or 10 10 210 11 cells. In the idealized setting, standard cancer therapies kill cancer cells by first-order kinetics; a given dose will kill the same fraction, not the same number, of cancer cells regardless of the size of the tumor. Thus, a dose of therapy that produces a 90% (1 log) reduction in tumor mass will have to be repeated 11 times to eliminate a newly diagnosed cancer comprising 10 11 cells (26, 27) . With current PET systems, the limit of resolution for detecting typical cancers by 18 F-FDG PET generally ranges between a 0.4-and 1.0-cm diameter (67,68), which translates into a tumor size roughly of 0.1-0.5 to 1.0 g or 10 8 210 9 cells. It follows that PET likely can measure only the first 2 logs of tumor cell kill, depending on the initial size of the tumor. Thus, a negative PET scan at the end of therapy can mean there are no cancer cells present or that there are as many as 10 7 cells. Although a completely negative PET scan at the end of therapy typically suggests a good prognosis, it does not necessarily correspond to an absence of cancer cells. Several studies have demonstrated the inability of 18 F-FDG PET to detect minimal tumor burden versus no tumor burden (64-66). On the contrary, in the absence of inflammation, a positive 18 F-FDG PET scan after several cycles of treatment is usually a harbinger of residual tumor. Because it is not possible for PET in its current form to detect microscopic burden, efforts to read to Partial remission: see Table 3 Progressive metabolic disease: ''increase in intensity or extent of tumor metabolic activity or new sites'' Progressive disease: see Table 3 FIGURE 1. Kinetics of tumor cell kill and relation to PET. Line A represents brisk tumor response that would produce cure after only 4 cycles of chemotherapy. Line B represents minimum rate of tumor cell kill that will lead to cure in 6 cycles of treatment. Both lines would be associated with negative PET scan after 2 cycles of chemotherapy. In contrast, line C represents rate of tumor cell kill that would be associated with negative PET scan after 4-6 cycles but would not produce cure. Importantly, PET scan for line C would likely be positive after 3 cycles (27). a high sensitivity, although well-intentioned, may yield excessive false-positive rates. Thus, it would probably be important to maintain the specificity of the technique in readings and in response assessments, in order to maximize the utility of the method. As is apparent in Figure 1 , the time to normalization of the PET scan is also important, as this time should reflect the rate of cell kill and, therefore, predict the likelihood of cure, per our simple model. Because a true-positive PET scan at the end of 2 cycles suggests that fewer than 1 or 2 logs of tumor cells have been eliminated, it is unlikely that the 10 or 11 logs needed for cure will be eradicated by standard-duration 8-cycle treatments. A true-negative scan after 1 or 2 cycles implies the opposite; that is, the rate of tumor cell kill for this tumor is sufficient to produce cure-or at least a valuable remission (Fig. 1) . In the earliest studies of cancer treatment response with PET, sequentially evaluating 18 F-FDG uptake in breast cancers before and at varying times after treatment, declines in 18 F-FDG uptake were seen with each successive treatment cycle in patients who were responding well (20). By contrast, lesser or no decline in 18 F-FDG uptake was seen in the nonresponders. Those patients with a continuing decline in 18 F-FDG uptake over time were the most likely to have complete pathologic responses by histology at the end of therapy. Tumor 18 F-FDG uptake also declined more rapidly than did tumor size with effective treatment. A large body of evidence supports these general principles in a wide range of human cancers evaluated with PET, including esophageal, lung, head and neck, and breast cancers and lymphoma (21, (69) (70) (71) . Patients whose PET scans convert from positive to negative after treatment more commonly have complete pathologic responses and typically better disease-free survival and overall survival than patients whose scans remain positive. Quite striking is that prognostic stratification between high and low 18 F-FDG uptake after (or during) treatment is typically preserved across disease types regardless of whether the changes in 18 F-FDG uptake are assessed qualitatively (often visually) or quantitatively, using a variety of cut-point thresholds for percentage decline in SUV or a cutoff value in absolute SUV. Readers are referred to several references for further examples of risk stratification with PET (63, [72] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] [85] . Because a growing body of data suggests that patients whose scans rapidly normalize are those most likely to have a favorable outcome, a disease-assessment scan performed soon after the beginning of treatment provides much information predictive of subsequent outcomes (85). Often, early changes in 18 F-FDG uptake are not complete and may be difficult to visualize. In this setting, quantitation of 18 F-FDG uptake may provide a better assessment than does qualitative analysis (57, 86) . It is also clear that for certain noncytotoxic agents, such as imatinib mesylate (Gleevec; Novartis), PET scans normalize much more quickly than anatomic changes, thus providing a better early prediction of outcome (43, 87) . Two basic approaches can be considered for assessing the metabolic changes of treatment: qualitative and quantitative. Another issue is whether a response scale should be binary (yes/no for response) or continuous (giving varying degrees of response). An additional and not fully resolved issue is whether the most metabolically active region of the tumor should be assessed or whether the entire tumor burden glycolysis and volume should be assessed. Not fully resolved, as well, is what constitutes a negative scan, a problem not unique to 18 F-FDG PET (88). Qualitative. PET scans for diagnosis and cancer staging in clinical practice are typically interpreted using qualitative methods in which the distribution and intensity of 18 F-FDG uptake in potential tumor foci are compared with tracer uptake in normal structures such as the blood pool, muscle, brain, and liver. Qualitative interpretations include a great deal of information, such as clinical experience, expectations of disease patterns for specific diseases, and knowledge of normal variants and artifacts. It might be expected that conversion of a markedly positive PET scan to a totally negative scan at the end of therapy could be done quite well with qualitative methods. Indeed, this has commonly been the method used in PET studies performed at the conclusion of therapy. The IWC 1 PET criteria developed through the efforts of Juweid and Cheson dichotomize PET results into positive and negative relative to the intensity of tracer uptake, as compared with the blood pool or nearby normal structures (Table 4) . Such an approach is attractive, and this dichotomous reporting has been used by many investigators in lymphoma, as reviewed by Kasamon et al. (27). However, there are pitfalls to this approach, because intermediate patterns of tracer uptake with intermediate prognostic significance have been described. One of these patterns was described by Mikhaeel et al. and termed minimal residual uptake. In a retrospective study of 102 patients evaluated with 18 F-FDG PET at mid treatment for aggressive lymphoma, 19 patients had scans with minimal residual uptake and had an estimated 5-y progression-free survival of 59.3%, closer to the 88.8% for the PET-negative group (n 5 50) than to the 16.2% for the PET-positive group (n 5 52), but seemingly different (89). Kaplan-Meier analyses showed strong associations between the mid-therapy 18 F-FDG PET results and progression-free survival (P , 0.0001) and overall survival (P , 0.01). In clinical practice, classification of minimal residual uptake seems to be the most challenging. Other approaches to lymphoma PET scoring using a 5-point visual scale have also been implemented in risk-adaptive clinical trials (90). Investigators in Melbourne have used the visual qualitative analysis criteria noted in Table 5 to predict outcomes at the end of therapy for non-small cell lung, colon, esophageal, and metastatic breast cancers (82, 84, (91) (92) (93) (94) , with excellent risk stratification capability between positive and negative scans. Hicks has argued for qualitative assess- THE ments and has emphasized the considerable value of the reader's perception in excluding treatment-induced alterations from actual disease progression. Other investigators have found qualitative imaging to be more accurate than quantitative imaging, such as in lung cancer nodal assessment (72). In studies of neoadjuvant therapy of colorectal cancer, we have found that multipoint qualitative assessments of treatment response on 18 F-FDG PET perform somewhat less well than quantitative assessments such as maximal SUV (SUVmax) or total lesion glycolysis (57). Given these results and those reviewed for lymphoma and by Weber and others, it is clear that qualitative assessments of tumor response carry with them considerable prognostic information. There are, however, surprisingly few data on the reproducibility of qualitative readings of PET for diagnosis or for treatment response. Reproducibility is important for clinical practice and clinical trials. In addition, there are not nearly as many data qualitatively evaluating PET response to treatment soon after treatment has been started as there are at the conclusion of treatment. The likely reason is that the changes in PET findings at the conclusion of treatment are far more substantial than those observed early after treatment has begun, and that early clinical trials with PET (and reimbursement for PET) focused, at least in the United States, on the restaging scenario at the conclusion of a course of treatment. The performance of PET diagnostic readers has been compared, to a limited extent. Moderate concordance in diagnostic accuracy was found for interpretations of PET scans of the axilla in women with untreated breast cancer. Three experienced readers had a comparable accuracy of 0.7-0.76 (area under the curve) (95) in over 300 patients evaluated independently by each reader. In lung cancer, moderate agreement in mediastinal staging by PET, especially of trained readers, has been reported, with k-values of 0.65 (96). After radiotherapy of head and neck cancer, variability in reporting has been seen by qualitative methods, with an intraclass k of 0.55. In 17% of cases, indeterminate readings were rendered (i.e., neither positive nor negative), indicating the difficulty of dichotomizing the inherently continuously variable PET uptake patterns (97). This is possibly similar to the ''minimal residual uptake'' category reported in treated lymphomas by Mikhaeel's group (89,98). In lymphoma, in which a dichotomous, positive/negative PET scoring system has been applied (Table 4) , some variability in reporting has been observed among readers. In one report, false-positive PET readings were not uncommon, occurring in about 50% of PET-negative cases of nonHodgkin lymphoma when read by less experienced readers. Indeed, only a 56% concurrence rate was seen between less experienced readers and experts (99) in assessments of nonHodgkin lymphoma disease activity. These figures may be reflective of inexperienced readers without benefit of PET/CT but suggest that some level of discordance qualitatively is to be expected. Although mainly qualitative readings have been used at the end of therapy in lymphoma treatment response, in mid-treatment monitoring both qualitative and quantitative readings have been used. We have used a 5-point visual assessment scale in our patients with non-Hodgkin lymphoma during therapy, and a 4-point scale in colorectal cancer after treatment, recognizing that response does likely represent a continuum of intensities of uptake (57,90). These approaches have not been fully studied for reproducibility among readers but likely have been made more consistent by limiting the number of readers of the study. For earlier subtle changes in tumor uptake before treatment effect is complete, quantitation may be more desirable and perhaps essential for consistent reporting among readers. Certainly, more information is needed on the reproducibility of qualitative reporting of treatment response in the therapy-monitoring setting. Quantitative. Because PET is intrinsically a quantitative imaging method, quantitative measurement of early treatment-induced changes is an attractive potential tool for measuring subclinical response and more complete changes. The feasibility of detecting small changes in tumor glucose metabolism quantitatively was demonstrated over 15 years ago in studies of neoadjuvant treatment of primary breast cancer, for which declines in SUV of 20%250% were seen, depending on the time from the start of treatment. These declines were evident using Ki, SUV, and the k 3 rate constant (20). More than 30 different ways to monitor tumor response have been discussed, but the SUV appears to be the most widely applied, generally correlating well with more complex analytic approaches (100,101). The SUV is a widely used metric for assessing tissue accumulation of tracers. SUV can be normalized to body mass, lean body mass (SUL), or body surface area. Body surface area and SUL are less dependent on body habitus across populations than is SUV based on total body mass. In a single patient of stable weight, all 3 SUV normalization approaches will give comparable percentage changes with treatment, as the normalization terms cancel out mathematically. However, the absolute change in SUV with effective treatment and the absolute amount of change in SUV to be significantly different from a prior scan will differ on the basis of the metric used. The determination of SUV is dependent on identical patient preparation and adequate scan quality that is similar between the baseline and follow-up studies. Ideally, the scans should be performed on the same scanner with comparable injected doses of 18 F-FDG and comparable uptake times before scanning. Absolute and rigorous standardization of the protocol for PET is required to achieve reproducible SUVs. Standardization has been well summarized in a consensus document from the National Institutes of Health and a recent report from The Netherlands (30,31). SUL is preferred by many over SUV normalized by body surface area, as the SUL values are relatively close to (though RECIST TO PERCIST: PET TUMOR RESPONSE • Wahl et al. 133S usually somewhat less than) SUVs normalized on the basis of total body mass (30, 102, 103) . SUL is typically more consistent from patient to patient than is total-body-mass SUV, as patients with high body mass indices have high normal organ SUVs because 18 F-FDG does not significantly accumulate in white fat in the fasting state (102,103). ROI selection is a key aspect of determining tumor SUV, tumor Ki, or any quantitative PET parameter. A wide variety of SUV ROI selection metrics has been used: manually defined ROIs; irregular isocontour ROIs based on a fixed percentage of the maximal pixel in the tumor (e.g., 41%, 50%, 70%, 75%, or 90% of the maximum); irregular isocontour ROIs based on a fixed SUV threshold (e.g., SUV 5 2.5); irregular isocontour ROIs based on a background-level threshold (e.g., relevant background 1 2-3 SDs); and small fixed-dimension ROIs centered over the highest-uptake part of the tumor (e.g., 15-mm-diameter circles or spheres or 12 · 12 mm squares, giving rise to a parameter sometimes called SUV peak). In addition, SUV is frequently obtained from the pixel with the SUVmax and, although not usually determined in this way, it could be considered to be a single-pixel ROI. As part of this special contribution, we have ascertained the methods for ROI selection in determining SUV in cancer studies in over 1,000 reports. The use of varying regions of interest to determine SUV over the past decade is shown in Figure 2 . It is apparent that SUVmax is growing in use and is the de facto standard, given its widespread use. A close examination of the graph shows a growing use of SUV peak, as well. The isocontour and manual ROIs have also been applied in some studies. Given that the use of SUVmax is so commonly reported, it might seem to be the ''best'' method. However, the wide use of SUVmax may also be due its being easily measured using current commercial workstations. To simply recommend SUVmax as the preferred treatment response parameter would be easy, as it should also be most resistant to partial-volume issues in small tumors. However, this recommendation must be taken with some trepidation as SUVmax is highly dependent on the statistical quality of the images and the size of the maximal pixel (104). For SUVmax to be used routinely, its performance characteristics should be well understood, including its reproducibility versus other approaches. A fundamental biologic question underlying choices of regions of interest is whether the total tumor volume or the maximally metabolically active portion of the tumor is most important. Intuitively, both would seem important and desirable to determine. However, concepts of stem cell biology suggest that the most critically important parts of tumors are the most aggressive portions, which may not be the entire tumor. This controversial concept is under study for many cancers (105-108). In practice, much of the early development of PET for treatment response was in the setting of a single tumor, as neoadjuvant therapy or as palliative treatment. Most papers focus on a single or a few tumor foci in ROI selection. However, the total lesion volume and its metabolic activity, known as the total lesion glycolysis, effective glycolytic volume, or total glycolytic volume (calculated in similar manners-mean SUV of the total tumor times · total tumor volume, in mL), are potentially important parameters for studying the behavior of the total tumor (109-112). For the purposes of this article, although the terms represent similar indices, we will refer to total lesion glycolysis in discussions of response based on total lesion volume and its metabolic activity. To use quantitative metrics to assess treatment response, one must know their performance characteristics. We are aware of 5 reports on the test-retest reproducibility of PET with 18 F-FDG in cancer, and the major methods and protocols of these studies are summarized in Table 6 (100,113-115). Overall, the reproducibility of quantitative PET parameters in the test-retest setting has varied depending on lesion size and the methods for image acquisition, reconstruction, and analysis. The lowest variability in PET quantitative parameters is in the 6%210% range, but up to 42% variability has been reported. In the test-retest setting, ROI and lesion size seem to be important for SUV reproducibility whereas reproducibility appears less dependent on glucose correction factors (113,114) and the reconstruction method used (filtered backprojection vs. ordered-subset expectation maximization) (100). Minn et al. (116) first demonstrated that although kinetic modeling with nonlinear regression is conceptually more attractive than SUV, it is not as reproducible in the test-FIGURE 2. Number of papers that included use of tumor ROIs, as function of year of publication. Papers were identified by Medline search that queried for FDG AND SUV OR ''standard uptake value'' OR ''standardized uptake value'' OR ''standardised uptake value''). Only human 18 F-FDG oncology studies were included. ROI max refers to maximal pixel in tumor. ROI peak refers to small (typically 15 · 15 mm) fixed-size ROI centered on most metabolically active part of tumor. ROI isocontour refers to irregular ROI defined by isocontour set at, for example, some percentage of maximal pixel. ROI manual refers to manually drawn ROI. Only a subset of these papers describes response assessment studies. THE retest setting as is the simpler Patlak-derived Ki or the SUV. Because both Ki and SUV (or SUL or body-surface-area SUV) correlate well with kinetic modeling results, full kinetic modeling approaches are not typically undertaken in treatment response monitoring with 18 F-FDG. Ki is an attractive parameter and may be helpful when the SUV after treatment is low (117). However, Ki requires a period of dynamic scanning, a process typically more time consuming and restricted in the spatial location evaluated than whole-body PET. Further, only limited standard software is available for generation of Ki values. The size of the ROI affects the reproducibility of SUV. SUVs obtained from larger, fixed ROIs are more reproducible than single-pixel SUVs (110, 115, 118) . Comparing the test-retest studies in Table 6 , one can see that the ROI used by Minn in 1995 (113) was 39-fold larger in volume than that used by Nahmias and Wahl (115) in 2008 for singlevoxel SUVmax (438 mm 3 vs. 12.5 mm 3 ). For equal sensitivity, there would be 39-fold fewer counts in the maximal pixel using modern PET scanners, versus the volume applied originally in determining the statistical precision of PET in the test-retest setting using older equipment with thicker slices and smaller matrices. The assessment of Nakamoto et al. (110) Reproducibility data from individual patients are likely of greatest practical interest in evaluating the degree of change required to determine that a change is significant between 2 studies. Weber et al. (114), using a larger ROI, reported that 0.9 SUV unit was needed for a significant change. Concordantly, Nahmias and Wahl (115) showed in test-retest studies that absolute differences in mean SUV obtained from a large ROI did not exceed 0.5 SUV unit and that the absolute differences in mean SUV decreased as mean SUV increased. In contrast, the absolute difference between SUVmax increased to over 1.5 SUV units in a substantial number of cases in which the SUVmax was over 7.5 (i.e., the hotter tumors). Thus, there are differences in the behaviors of SUVmax and mean SUV in terms of reproducibility that likely will have a direct impact on the fractional and absolute changes required to have a significant difference between a baseline and a follow-up scan. The large ROI of Nahmias (115) showed superb test-retest performance; however, the size of their circular ROI was both manually determined and manually positioned, and thus it may be difficult to routinely achieve such low variability at other centers. Larger ROIs may be too big for small tumors such as nodes to be optimally assessed, as well. These human data are augmented by phantom and modeling data. Boellaard et al. also showed that SUVmax variability increases as the lesion matrix size is increased from 128 · 128 to 256 · 256. They also showed that the variability increases with lower counts as the patient size increased (and the statistical quality decreased) (104). The appeal of the single maximal pixel value is undeniable, but it is clear that with modern scanners and many small voxels, it is not as reproducible as larger ROIs and that larger changes in SUVmax between studies are needed for significance (104). This is mainly because of noise effects on SUV, which induce a positive bias in the recovery coefficient for SUVmax. As lesions get larger and hotter, there is also a statistical bias to higher single-pixel SUVmax simply because of the number of counts available. This raises concern, especially given the widespread and growing use of this parameter in clinical studies with PET, and caution must be applied in the use of single-pixel SUVmax for assessing small changes induced by treatment. For these reasons, it is probably important to have a minimum ROI for PET metrics of maximal tumor activity to ensure adequate statistical quality and intrastudy comparability. Methods for determining total lesion glycolysis are still evolving. Choosing a threshold based on a single maximal pixel value in the tumor carries with it the variability inherent in determining a single-pixel value and is driven by that value (104, 109, 112, 119, 120) . Investigators have also found poor reproducibility for tumor volume estimates (also applied to calculate total lesion glycolysis) using thresholding methods based on the maximal pixel value. After treatment, thresholding methods for tumor volume determination may extend to include too much normal tissue (118). The use of thresholds such as ''anything 3 SDs or greater above background is tumor'' is one approach that has been applied to defining lung cancer volumes on PET, avoiding the uncertainty of SUVmax (121). A background threshold approach has been developed as a tool for defining metabolic tumor volumes for mesotheliomas with good initial success, choosing 3 SDs above background levels for segmentation (111). Other approaches include determining the lesion volume not from PET but from the CT of the PET/CT (122). These methods hold great promise for providing the tumor burden, which may be quite important as a complement and addition to SUV. One other approach, akin to total lesion glycolysis, is the multiplication of SUVmax · tumor width to provide a combined glycolysis · size parameter. Such approaches may be useful in response assessment but have not been extensively assessed. They could suffer from the variance intrinsic in the metabolic and anatomic methods, potentially reducing the precision of the methods, but initial results are encouraging in esophageal cancer treatment assessment (123). Comparing tumor activity to background is an attractive way to minimize variability and to potentially ensure the RECIST TO PERCIST: PET TUMOR RESPONSE • Wahl et al. 135S 20, 113) . These values were slightly higher than mean blood-pool values. Krak et al. recommended the use of SUL for monitoring treatment response, as well, although they favored glucose correction (100). A variety of methods has been used to determine the change in SUV with treatment. SUVmax in a single pixel, background-corrected values, larger or smaller ROIs, and total lesion glycolysis have been used, among others. The prospective data of Weber et al. are among the most compelling (125). Based on the differences seen in testretest studies, they evaluated changes in SUV in cases that met the following characteristics: tumor clearly visible, large enough, and hot enough (2 · blood-pool background). Using a 1.5-cm ROI, they showed in lung, gastric, and esophageal cancers that declines in 18 F-FDG uptake of 20%235% after 1-2 doses of therapy are predictive of outcomes, with the larger the drop, the greater being the beneficial effect. In esophageal cancer, for example, Weber et al. found a drop of greater than 35% in SUV to be a good predictor of response (125). In neoadjuvant gastric cancer therapy, in which tumors with an SUV of more than 1.35 times the mean liver SUV 1 2 SDs were assessed, the mean decline in SUV was about 50% in responders and 18% in nonresponders (126). Weber has argued that any drop of more than 20% is significant and should be called a response on the basis of reproducibility considerations (Radiological Society of North America syllabus). However, in most studies, larger drops in SUV of more than 30%235% are seen and associated with a good outcome. In lymphomas, at mid therapy, a drop in SUV of 65.7% was best at separating favorable from unfavorable responses and appeared superior to visual examination (accuracy visual, 65.2%; SUV reduction, 76%; tumor-to-background ratio, 74%; and SUV floor, 74%) in a study by Lin et al. (86). Although quantitative analysis appeared superior to visual analyses (though it must be cautioned that this was using a retrospective cutoff value and there was considerable overlap in the best responding and less well responding groups quantitatively-as well as a fine continuous scale for quantitation but a coarser approach for visual analyses), the several quantitative approaches appeared quite comparable. The authors favored the percentage decline in SUV. It appears that many methods of quantification can produce valuable prognostic information on treatment response using PET. Another issue in PET treatment response is whether an absolute SUV floor or threshold (such as blood-pool background in the non-Hodgkin lymphoma PET criteria) or a percentage decline in SUV is most important. The advantages to a percentage drop in SUV versus a floor are that the percentage drop is likely easier to calculate than the absolute SUV; many measurement issues become less important when test-retest studies are done, because the technical issues are constant across studies. Modeling studies have shown that the ratios of SUV are less dependent on ROI choice than are absolute SUV determinations (104). An SUV floor carries the advantage of allowing a baseline PET scan to be obtained at another center to verify the 18 F-FDG avidity of the tumor, but such a baseline study is not required for quantitation. The data of Lin et al. (86) show nearly comparable results for floor SUV versus percentage decline in terms of ability to separate those with a good response from those with a less good response to treatment for non-Hodgkin lymphoma. However, several papers have shown that in lung cancer, for example, a decline in a tumor SUV to below 4-6 after treatment separates groups of patients with longer and shorter survival reasonably well (72,127). The differing cutoffs suggest possible differences in SUV calculation approaches. Reproducing absolute SUV across centers can be difficult, however, and although such absolute cutoffs may be valuable for determining prognosis, they are viewed as more suitable in single-center studies or in well-controlled multicenter approaches using careful standardization methods (31). It may be possible to determine a simple floor for PET through the use of normalization to structures such as the normal liver or blood pool, for example, as has been done qualitatively in the IWC 1 PET criteria (33). SUVs in normal tissues are not stable with time, because blood-pool and liver uptake fall with increasing delays from injection, whereas uptake in tumor typically rises (20, 128) . Thus, normalization is difficult if scan uptake times vary. However, a threshold for posttreatment PET is an attractive concept and may be more important in the future as standardization for PET performance improves. Methods of assessing response to treatment with total lesion glycolysis are still evolving. It appears that percentage declines in total lesion glycolysis are sometimes greater than declines in SUV and that total lesion glycolysis gives a larger range of changes after treatment than does SUV (111). This would suggest that larger changes in total lesion glycolysis would be required to have a meaningful response than are required for SUV alone. Francis has found total lesion glycolysis to be superior to SUVmax in mesothelioma response RECIST TO PERCIST: PET TUMOR RESPONSE • Wahl et al. 137S assessment. However, SUVmax is also a potent predictor of outcomes in other studies of mesothelioma (52, 129) and is quite strong in the data of Francis et al., as well (111). In studies of colorectal cancer neoadjuvant response, SUVmax appeared to perform somewhat better than total lesion glycolysis, though it depended on the specific task involved (57). Total lesion glycolysis has performed well in studies of colorectal cancer and brain tumor response (109, 112, 119, 120) . In studies of sarcoma response, total lesion glycolysis performed less well than SUV peak (122). Thus, the total lesion glycolysis parameter appears promising in some, though not all, cancers. The method by which it is calculated can be quite variable, however. The EORTC PET response criteria were proposed in 1999 (36). Given the limited data available on treatment response at that time, the criteria were useful and prescient. They recognized that the subclinical metabolic response seen early after treatment on PET, but not seen anatomically, was likely to be important. The group made several important points in its report regarding the 18 F-FDG PET response: careful methods and patient preparation are essential; early declines in SUV with effective therapy will be smaller than later ones; with ineffective treatment, tumors can progress not only by increasing their SUV but also by physically growing; accurate and reproducible methods are essential for accurate reporting; and as the literature matures, updates will be needed (36). Drawing from their work and the maturing literature on treatment response assessment over the intervening decade, some additional suggestions regarding treatment response criteria are in order.
For this purpose, simple quantitative image features like SUVmax, SUVpeak REF , metabolic tumor volume (MTV), or total lesion glycolysis (TLG) have been proposed, out of which SUVmax is most widely adopted.
From RECIST to PERCIST: Evolving Considerations for PET response criteria in solid tumors.
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Medicine