bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association of food insecurity with reduced cancer screening rates.
Authors: Shah Mohammed Masoom , Islam Shahidul , Braunstein Marc Justin 
Published: May 2024

1592 Background: Social determinants of health, including access to adequate food and nutrition, influence many aspects of health, including cancer screening. This study explored the associations between food insecurity and cancer screening. Methods: A retrospective analysis was conducted using the National Health Interview Series from 2018-2022 to capture contemporary screening events. Based on scaled questionnaires, food security was stratified into either food secure or insecure, and then subgrouped into high, marginal, or low security. Associations were made with rates of undergoing at least one screening test. Relationships between variables were evaluated using Chi-Square and Cochran-Armitage trend tests. Logistic regression models were applied to find factors associated with cancer screening. Results: Patients included in analyses included: 51,957 for colon, 40,103 for breast, 45,983 for cervical, and 25,781 for prostate cancer screening. Between 2018-2022, those with food insecurity had significantly lower rates of cancer screening than those with food security across all four cancer screens, including colon (68.1% vs. 76.1%, p < 0.0001), cervical (83.3% vs. 87.5%, p < 0.0001), breast (73.3% vs. 79.5%, p < 0.0001) and prostate (41.3% vs. 54.0%, p < 0.0001) (Table). This was similar when comparing low, marginal, and high food security, respectively, for colon (68.1% vs. 68.2% vs. 76.1%, p < 0.0001), cervical (82.8% vs. 84.1% vs. 87.5%; p < 0.0001), breast (72.7% vs. 74.1% vs. 79.5%, p < 0.0001), and PSA screening (40.7% vs. 42.2% vs. 54.0%; p < 0.0001). Other variables were also analyzed, including insurance type, age, race, BMI, and health status. Those with public health insurance had lower screening rates for cervical cancer compared to those with private insurance (p < 0.0001). Individuals with food insecurity reported worse health than those who were food secure (p < 0.0001). Lower food security disproportionately affected Black patients (p < 0.001), and was associated with younger age (p < 0.0001) and higher rates of obesity (p < 0.0001). Conclusions: Individuals with self-reported food insecurity were found to have lower cancer screening rates than those who were food secure. Further analyses revealed associations between low food security status and obesity, public health insurance, younger age, and Black race. These data suggest that addressing food insecurity should be a priority, as these populations appear most vulnerable to lacking appropriate cancer screening. [Table: see text]
Long term impact of COVID-19 pandemic on mammographic breast cancer screening.
Authors: Karam Prasad Dhauna , McGary Alyssa , Kosiorek Heidi E. , Pritchett Joshua , Ellis Richard , Ruddy Kathryn Jean , Haddad Tufia C. , Yousufuddin Mohammed 
Published: May 2024

1597 Background: The impact of COVID19 pandemic on patient health beliefs and healthcare systems has adversely affected mammographic breast cancer screening (MBCS) during the initial stages of the pandemic. The aims of our study were to determine whether the initial decrease in MBCS rates persisted or if there was a rebound in later stages and to quantify the impact on urban and rural populations. Methods: This retrospective observational study analyzed data from March 2017 to March 2023 to assess whether eligible women received annual MBCS during this timeframe. We included women aged 40-75 years, drawn from the integrated Mayo Clinic system, encompassing nearly 60 primary care practices in Arizona, Florida, Iowa, Minnesota, and Wisconsin, representing a broad cross-section of the United States (US) population. US census data was utilized to classify a county as urban or rural. We divided the study period into 4 categories:1) pre-COVID-19 pandemic (March 1, 2017-March 10, 2020), 2) early pandemic (March 11, 2020-March 10, 2021; pre-vaccination period), 3) mid- pandemic (March 11, 2021-March 10, 2022; delta and omicron variant dominant period), and 4) late pandemic (March 11, 2022-March 1, 2023; recovery period). Utilizing the Poisson regression model, we compared the incidence rate ratio (IRR) for rates of MBCS across the 4 predefined time periods. Multivariable modeling was performed to assess the significance of MBCS rates with urbanicity while controlling for the year (2017-2022) and age. Results: By race and ethnicity, the women in this cohort were White (92%), Black (3.1%), Asian (3%) and other (1.9%). The median age was 57 years and the cohort included 239, 804 women aged 40-75 years. MBCS rates was 49.2% in 2017, 47.1% in 2018, 46.4% in 2019, and decreased to 42.1% in 2020 and improved to 44.7% in 2021 and 43.8% in 2022. Compared with the pre-COVID-19 pandemic period, there was a significant decrease in MBCS rate in the early pandemic (IRR= 0.886, 95% CI 0.879, 0.892, p<0.001). Though the MBCS rate improved in the mid-pandemic period, it remained below the pre-pandemic level (IRR=0.941, 95% CI 0.934, 0.948, p<0.001). Similarly, during the late stage of the pandemic, the MBCS rate remained lower than the pre-pandemic level (IRR=0.922, 95% CI 0.915, 0.928, p<0.001). Rural counties had a lower MBCS rate compared with urban counties (IRR = 0.936, 95% CI 0.931, 0.941, p<0.001). Conclusions: This study confirms the negative impact of the early COVID-19 pandemic on MBCS, and it revealed its persistent effect, as the current MBCS rate remains below the pre-pandemic rate. These findings were more evident in rural counties. Factors contributing to the long-lasting impact are being analyzed with the intent to develop and implement targeted solutions to improve the MBCS rate.
Assessment of endocrine therapy adherence in a nurse-led cancer survivorship clinic: Results from the Linking You to Support and Advice (LYSA) trial.
Authors: Ghassemi Rad Mohammad Javad , O'Connell Kate , Raigal Laia Aran , Johnston Katie E , Flynn Naoimh Marie , O'Reilly Seamus , Noonan Sinead , Medved Katarina , McInerney Veronica B , Lowery Aoife J , Palmer Brendan Anthony , Dahly Darren L , Hegarty Josephine , Connolly Roisin M. 
Published: May 2024

1627 Background: The care of those living with and beyond a cancer diagnosis requires dedicated cancer survivorship services to ensure optimal symptom control and quality of life. Supporting adherence to adjuvant endocrine therapy is also an essential component of care. We examined endocrine therapy adherence rates, and factors associated with non-adherence, in patients with early-stage breast cancer enrolling in a survivorship trial (LYSA). Methods: Women with early-stage hormone positive breast (n=168) cancers within 12 months of completing primary therapy, were enrolled in a randomized control trial with parallel arms (PMID 36357934). Experimental arm had access to nurse-led symptom management, dietetic consultation, and undertook bimonthly electronic patient reported outcome (ePRO). Active comparator group had access to conventional care and ePRO was completed at beginning and end of study. Endocrine therapy adherence was reported as the ability to take medication as prescribed over prior 4 weeks (Michigan Oncology Quality Consortium Adherence Tool), with response quantified as Excellent (adherence close to 100%), Very Good (80%), Good (60%), Fair (40%) and Poor (20%). Adherence Objective (Secondary) was adherence rate at 12 months after study entry in both arms, and factors associated with non-adherence. Results: A total of 148/168 women with breast cancer who completed the LYSA study reported adjuvant endocrine therapy use (74 experimental arm, 74 active comparator) during study conduct. Therapies included aromatase inhibitors (53%), tamoxifen (39%) and ovarian suppression combinations (8%). At study entry ≥ 80% adherence was reported in 74% of overall cohort (75% experimental, 72% comparator, p=0.7), with 1.6% reporting not taking any medication. By end of study (12 months), ≥ 80% adherence was reported in 74% overall (77% experimental, 71% comparator, p=0.4), while non-adherence rates were 2.9%. Among the pre-specified reasons for non-adherence at study end, “simply missing it” ranked highest (10%), and 5% “experienced side effects”. Post-menopausal patients were more likely than pre-menopausal to report excellent (versus sub-excellent) adherence at study end (OR 4.60, 95%CI 1.45 – 16.13, p = 0.012, adjusted for arm, age, and ECOG). Additional exploration of reasons for non-adherence will be presented. Conclusions: Adjuvant endocrine therapy adherence rates in both arms at 12 months after study entry collected via ePRO were good (≥ 90% reporting excellent or very good adherence). Additional factors potentially associated with non-adherence are being analyzed including self-care agency, symptom burden and quality of life. Clinical trial information: NCT05035173 .
The use of telehealth in a large community oncology practice prior to and during the COVID-19 pandemic.
Authors: Nickleach Dana , Saravanan Priya , Meyer Gary , Mehring Kiana , Mumuney Ganiat , Gordan Lucio N. 
Published: May 2024

1631 Background:The COVID-19 pandemic prompted implementation of telehealth services in oncology following CMS approval for reimbursement. Using data from a single large US-based community oncology practice, we characterized the adoption of telehealth, evaluated telehealth’s impact on patient transit to the site of care, and the frequency of ER visits and hospitalizations. Methods: A retrospective cohort analysis was conducted using data from January 2019 through June 2022 from the FCS EMR database, IQSS’s RefineIQ database, which captures and standardizes patient treatments for consistent analysis, InfoDive, a reimbursement platform with data on clinic visits, and Oncology Care Model data. The target population included all patients with cancer aged 18 or older under active management at FCS during the study window with at least 60 days of follow-up. Patient disease and demographic characteristics and ER/hospital care visits were compared between telehealth groups, as well as impact of telehealth use on transit time and distance traveled for patients was summarized using descriptive statistics. Results:178,531 patients were analyzed. Median age was 68, with 64% female and 47% white. 97% of patients lived in a metropolitan area, 97% in Florida, and 77% in areas with less than $78,000 median income. Median duration of disease was 41 months, 24% of patients were stage IV and 93% ECOG 0-1. 20% of patients used telehealth, with 81% having only one telehealth use per year. Patients using telehealth, compared to those that did not, were more likely to be white (50% vs 46%), have commercial insurance (30% vs 27%), live in areas where annual median income was > $78,000 (25% vs 22%) and ≥95% of the population graduated high school (31% vs 26%). Telehealth saved a median of 5 miles driven per user annually. Users tended to have more advanced disease than non-users, with a higher proportion of metastatic disease (27% vs 23%), high CMS risk score (16% vs 15%), and line of therapy 2 or greater (10% vs 7%). Users had higher median annual oncology clinic visits both pre- (5 vs 3) and post-telehealth approval (6 vs 5). Hospital admissions after ER visits were more frequent among users both pre- (14% vs 13%) and post-telehealth approval (29% vs 25%). Conclusions: Telehealth was somewhat more likely to be used by patients with cancer in more affluent communities, white patients, patients who had more advanced disease and more frequent clinic and hospital visits. Most patients who used telehealth used it only once, and the low utilization of telehealth had limited impact on patient transit effort to the clinic. This study is limited by its concentration at a single large practice in Florida, the relatively low use of telehealth, retrospective design, and selection bias, as the telehealth population had more advanced disease and likely needed more frequent intervention.
Associations of predictive biomarkers, MHC-I and MHC-II, with clinical and molecular features in a diverse breast cancer cohort.
Authors: Reid Sonya A. , Sun Xiaopeng , Kennedy Laura Carpin , Gonzalez-Ericsson Paula , Sanchez Violeta , Sanders Melinda , Perou Charles M. , Troester Melissa A. , Balko Justin M 
Published: May 2024

517 Background: The addition of immunotherapy (IO) to chemotherapy in triple negative breast cancer (TNBC) has improved survival outcomes and recent studies have also shown improvements in pCR rates in early high-risk HR+/HER2- breast cancer. However, these improvements are not without toxicities and thus there is a critical need for biomarkers to select patients that are most likely to benefit from immunotherapy. MHC-I and tumor-specific MHC-II (tsMHC-II) expression are candidate biomarkers for predicting PD-(L)1 checkpoint inhibition benefit and have previously shown potential for predicting treatment response in multiple tumor types, including breast cancer. However, the existing data were generated from clinical trials with limited racial/ethnic diversity. Here we report associations between MHC-I and MHC-II expression and clinicopathological features in a large, racially and ethnically diverse breast cancer cohort. Methods: We performed multiplexed immunofluorescent tissue-based analysis of participants in the Carolina Breast Cancer Study (CBCS). Intrinsic subtype and P53 mutant-like status were identified using RNA-based multigene assays. To evaluate MHC-I we ranked all participants based on tumoral MHC-I intensity, with the top 33% categorized as "MHC-IHigh"; MHC-II+ expression on tumor cells was defined as ≥5% of tumor cells. Association between MHC-I/II and clinicopathological features by race was tested using Chi-square test or fisher-exact test and relative frequency difference (RFD) was calculated. Results: This study included 1630 participants (48% Black and 52% non-Black) with Stage I-III breast cancer. Black participants had a higher incidence of TNBC (25% vs. 12.5%, p=<0.001) and Basal-like (29% vs. 14%, p=<0.001) tumors. Basal-like and TP53 mutant tumors were more frequently observed among Black participants with HR+/HER2- tumors (11% vs. 5.5%, p=0.006 for Basal-like; 26% vs. 17%, p=0.003 for p53 mutant-like). While there was a similar distribution of tsMHC-II+ tumors between Black and non-Black participants, the relative frequency of tsMHC-II+ tumors was significantly more common in Black participants with HR+/HER2- breast cancer compared to non-Black participants (7.9% vs. 5%, p=0.04). Moreover, Black participants also were significantly more likely to have MHC-Ihigh tumors (38.9% vs. 28.4%, p=0.01), which, after IHC subtype stratification, was only significant among participants with HR+/HER2- breast cancer (28.2% vs. 22.3%, p=0.02). Conclusions: In this diverse breast cancer cohort, MHC-I and MHC-II expression, candidate predictive biomarkers of IO benefit, were more prevalent in tumors from Black women with HR+/HER2- breast cancer. These results underscore the importance of diverse and equitable clinical trial enrollment in future IO trials, especially in HR+/HER2- breast cancer.
Breast Cancer Index re-stratifies 21-gene assay risk groups for late distant recurrence and extended endocrine therapy benefit: Results from the BCI Registry Study.
Authors: Chuy Valerie , Siuliukina Natalia , O'Neal Brandon , Anderson Amanda Kristine Laust , Hoskins Kent , Zhang Yi , Treuner Kai , Gadi VK 
Published: May 2024

529 Background: Patients with hormone receptor-positive (HR+) breast cancer face a prolonged risk for late distant recurrence (DR), even after 5 years of adjuvant endocrine therapy. The BCI (Breast Cancer Index, Biotheranostics) is a genomic assay that provides the risk of late DR and predicts the likelihood of benefit from extended endocrine therapy (EET) based on the H/I (HOXB13/IL-17BR) ratio. The 21-gene RS (Recurrence Score; Oncotype DX, Exact Biosciences) test provides the 9-year risk of DR and the benefit from adjuvant chemotherapy (CT) in HR+/HER2− breast cancer. However, the RxPONDER trial showed that RS was not predictive of CT benefit in premenopausal women with N1 disease. Here, we investigated the concordance between BCI prognostic score and H/I versus RS in HR+/HER2- patients from the BCI Registry study. Methods: The BCI Registry study is a prospective, multi-institutional study to evaluate the long-term clinical outcome, decision impact and medication adherence in early-stage HR+ breast cancer patients. Based on TAILORx and RxPONDER results, RS stratified patients into four risk groups: Age ≤ 50, No CT benefit (RS ≤ 15); Age ≤ 50, CT benefit (RS>15); Age > 50, No CT benefit (RS ≤ 25); and Age > 50, CT benefit (RS > 25). Kendall’s correlation coefficient (R) was used to estimate the correlation between BCI prognostic score, BCI (H/I) and RS as continuous variables. Results: BCI and RS results were reported for 847 patients (76% T1; 59% grade II; 85% N0). H/I classified 559 patients (66%) as H/I-Low and 288 (34%) as H/I-High, respectively, while BCI prognostic risk scores classified 434 patients (51%) as low-risk and 413 (49%) as high-risk for late DR. RS classified 30 patients (4%) as Age ≤ 50, No CT benefit, 38 (5%) as Age ≤ 50, CT benefit, 665 (78%) as Age > 50, No CT benefit and 114 (13%) as Age > 50, CT benefit. When analyzed as continuous variables, BCI H/I and prognostic score showed a weak correlation with RS with R=0.18 and R=0.2, respectively. Based on categorical groups, H/I had low concordance with RS for younger women (Age ≤ 50) but showed some correlation with RS for older women (Age > 50). Among those older than Age 50, H/I identified 31% of those with no CT benefit as likely to benefit from EET while 39% of those with CT benefit as not likely to benefit from EET. Conclusions: BCI H/I and prognostic risk scores exhibited low concordance with RS risk categories. H/I consistently re-stratified RS risk categories into distinct H/I-Low and -High groups, further substantiating that prediction of CT benefit does not equate to prediction of EET benefit. [Table: see text]
Predicting epirubicin response in Danish patients with breast cancer.
Authors: Buhl Ida Kappel , Nart Jan , Papadovasilakis Zacharias , Sørensen Simon , Jensen Peter Buhl , Ejlertsen Bent , Buhl Ulla Hald , Tsamardinos Ioannis 
Published: May 2024

539 Background: Anthracyclines are highly effective chemotherapy drugs. However, they are toxic and feared for their cardiotoxicity. There is a need for improved personalized prediction of treatment benefit. For example, state-of-the-art guidelines (NCCN Guidelines Version 1.2024 and meta-analysis PMID: 37061269) mark anthracyclines as optional adjuvant treatment of estrogen receptor (ER) positive, HER2 negative early high-risk breast cancer. A biomarker to predict whether a patient will benefit from anthracycline treatment could potentially lead to increased cure rate and overall reduced toxicity. However, no such test exists. Here we introduce a clinically validated, predictive test based on clinical tumor transcriptome data and the cell line panel NCI60 that can forecast epirubicin response in breast cancer patients with high accuracy. Methods: Microarray gene expression levels and growth inhibition levels (GI50) from the NCI60 cell line panel were acquired from NCBI to build a prediction algorithm. A tumor-specific variance-based filter based on >6.000 patient tumors was applied to select the most relevant genes. AUC (Area Under Curve) was used to evaluate the performance of numerous configurations and combinations of parameters that led to different gene-signature sets. Response scores based on the gene expression level of all genes in each signature were generated for each sample in a published cohort (PMID: 23340299) and a final signature with 141 genes was the best-performing signature (Progressive Disease versus Response AUC 0.81 and ANOVA-test p-value < 0.01). The model was validated on a cohort of Danish breast cancer patients treated with epirubicin in the metastatic setting (n=153), with time-to-progression (TTP) as endpoint (PMID: 31654283, PMID: 30099635). Finally, we used an established method (PMID: 30289602) to test if the gene signature was correlated between microarray- and RNA sequencing-based datasets. Results: The hazard ratio (HR) for the best-performing signature was 0.31 when comparing patients with a difference of at least 50 in response score (two-sided p-value = 0.012, 95% confidence interval 0.12-0.77, endpoint TTP, continuous scoring). This was consistent in a multivariate model including ER and HER2. Using the data and method provided by Pedersen et al (PMID: 30289602), we also saw a high correlation between matching microarray and RNA sequencing datasets for the genes in our model (mean Pearson’s r = 0.87). Conclusions: Here is presented a development and clinical validation of an accurate epirubicin response signature for breast cancer patients. The expression of the genes in the signature is highly correlated between microarray- and RNA sequencing data. Moving forward, we will seek to validate the model independently on RNA sequencing data. Our aim is that these models will be accepted for prospective clinical tests at local hospitals in Denmark in early 2025.
Artificial intelligence algorithms for recurrence risk score prediction in early-stage breast cancer: A multicenter study of 437 cases.
Authors: Bayram Ertugrul , Basaran Gul , Gokmen Erhan , Mandel Nil Molinas , Oyan Basak , Uskent Necdet , Karagoz Bulent , Yildirim Yesim , Karaaslan Sinan , Guler Emine Nilufer , Eralp Yesim , ER ÖZLEM , Karaoglu Aziz , Komurcu Seref , Mete Burak , Gemci Fahriye , Ibrikci Turgay , Paydas Semra 
Published: May 2024

545 Background: In non-developed and developing countries, the 21 gene assay (Oncotype DX) test is expensive and not covered by private and social health insurance, which limits its use. However, clinicians need this test for treatment prediction in early stage breast cancer. Therefore, we aimed to predict the risk of recurrence in patients using artificial intelligence (AI) algorithms. Methods: This study was conducted with 437 early-stage breast cancer patients whose recurrence risk scores were determined by 21 Oncotype DX and were followed in 14 oncology centers in Turkey between 2012 and 2022. The optimum cut-off for the 21 gene assay risk score (RS) was found by ROC analysis using the patients' recurrence information (scale of RS ≥17: high risk, <17: low risk). In artificial intelligence prediction models, patients with a 21 gene assay risk score ≥17 were considered high risk. Additionally, algorithm predictions were made within other cut-offs recommended in the literature (<10, 11-25, >25). Python programming language was used in the analyses. The Random Forest (RF), Logistic regression (LG) and K-Nearest Neighbors (KNN) were used as AI prediction algorithms. Results: The study analyzed 437 women with an average age of 49.49±10.30. During the follow-up period from 2012 to 2022, 7.1% of the patients experienced a recurrence. AI prediction algorithms which were RF, LG, and KNN generated confusion matrices for two classes, with a threshold of 17 and 25. The algorithms were then applied to the dataset with risk score thresholds of 1-10, 11-25, and greater than 25, resulting in three classes. The dataset was analyzed using recall, precision, and F1 score values. The results showed that RF had the most accurate predictions for RS = 17 (Table). Conclusions: The use of artificial intelligence algorithms in predicting the risk of recurrence in early stage breast cancer is promising in determining treatment decisions for clinicians who cannot access genetic results. [Table: see text]