bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial intelligence (AI) analysis of tumor-infiltrating lymphocytes (TILs) in hematoxylin and eosin (H&E) slides to explore immune phenotypes in papillary thyroid cancer.
Authors: Nam Myungwoo , Chung Liam Il-Young , Kim Leeseul , Park Gahee , Kim Sukjun , Song Sanghoon , Ock Chan-Young , Chae Young Kwang 
Published: June 2023

6100 Background: Different biomarkers have been discovered to predict response to immunotherapy but the variability of response exists. TILs are a potential biomarker as infiltration of CD8+ T cells in the tumor microenvironment(TME) was related to treatment response to PD-1 blockade. Advanced well-differentiated papillary thyroid cancer(PTC) has limited standard-of-care treatment options and can exhibit a poor prognosis when exhausted. Our study aimed to classify the TME of PTC according to TILs. Methods: Using the TCGA PTC data, samples were classified into three immune phenotypes (immune-’desert’, immune-’excluded’, and ‘inflamed’) based on peri-tumor lymphocyte density from analysis of H&E slides using an AI model(Lunit SCOPE). Immune and stromal cell infiltration was inferred using xCell. Cytolytic scores(CYT) and thyroid differentiation scores(TDS) were calculated using mRNA expression. Results: Total of 383 samples were included. Immune phenotype analysis showed 183(47.7%) ‘desert’, 133(34.7%) ‘excluded’, and 67(17.4%) ‘inflamed’. Median age at diagnosis was the lowest in ‘inflamed’(inflamed 41.5 vs excluded 47 vs desert 50, p = 002). TNM stages were as follows in the order of stages 1 to 4: inflamed 49/4/8/3, excluded 64/13/32/16, and desert 86/28/40/19. BRAF V600E mutation was most prevalent in ‘excluded’(97/133, 72%), followed by ‘inflamed’(32/67, 47.7%) and ‘desert’(51/183, 27.8%). CYT was the highest in ‘inflamed’(median: inflamed 186 vs excluded 53 vs desert 39, p < 0.001). TDS was the highest in ‘desert’(median: inflamed -0.3 vs excluded -0.69 vs desert 0.73, p < 0.001). The 6 cell types with the most statistically significant difference in infiltration were activated dendritic cells(aDCs), DCs, B cells, epithelial cells(EpCs), CD8+ central memory T cells, and CD4+ memory T cells. The ‘inflamed’ showed the highest median value for all of these cells except EpCs. The cells with the highest median value in ‘excluded’ included EpCs, sebocytes, and immature DCs. The cells with the highest median value in ‘desert’ were lymphatic endothelial cells(ECs), ECs, and osteoblasts. Three immune phenotypes were associated with differential outcomes in disease-free survival(log-rank for trend, p-value = 0.01). There was no difference in overall survival. Conclusions: Our results suggest that 15% of PTC can have immune features which were related to a higher rate of response to immunotherapy across different tumor types. A previous study showed that PTC with a low TDS or BRAF V600E mutation is related to increased immune response indices. In our study, TDS or BRAF V600E mutation status could not distinguish the ‘inflamed’ from other phenotypes. This suggests that immune phenotype classification through TILs can provide distinct information about the TME of PTC.
Use of PROMIS to capture patient reported outcomes over time for patients on I-SPY2.
Authors: Jacob Saya , Yu Hongmei , Wolf Denise M. , Chattopadhyay Aheli , Brain Susie , Simmons Carol , Ruddy Kathryn Jean , Kang Irene , Blaes Anne Hudson , Somani Amrita B , Hershman Dawn L. , Esserman Laura , Melisko Michelle E. 
Published: June 2023

611 Background: Patient reported outcomes (PROs) capture direct feedback from patients in clinical trials. The NIH Patient-Reported Outcomes Measurement Information System (PROMIS) is an open source and validated PRO platform. Data on PROMIS score trajectories over time for breast cancer patients undergoing neoadjuvant chemotherapy (NAC) are limited. Methods: We administered paper surveys to patients enrolled on the I-SPY2 trial, a multicenter, randomized phase 2 trial comparing novel agents to standard NAC in high-risk breast cancer. PRO domains included PROMIS Physical Function, Anxiety, Depression, Applied Cognition and Fatigue. Surveys were administered at baseline (post-consent), C1D1, inter-regimen (~12 wks into NAC), pre-surgery and at 1, 6, 12, and 24 months post-surgery. Responses were scored using the HealthMeasures Scoring Service to generate a standardized T score, with a score of 50 representing the general US population mean. Higher Anxiety, Depression and Fatigue scores represent worse symptoms; higher Physical Function and Applied Cognition scores represent better symptoms. We calculated average scores by time-point and compared changes from baseline to various grouped time points using a paired t-test. Results: From 1/2012 to 7/2020, 1631 patients enrolled in I-SPY 2 with 959 completing the baseline survey. 62% had at least two “on-treatment” surveys, and 52% had at least one follow-up time-point. Average baseline PROMIS scores for Physical Function, Anxiety, Depression, Applied Cognition and Fatigue were 46.7, 55.1, 47.2, 53.3 and 46.0 respectively. Between baseline and the latest on-treatment time point we observed improvement in Anxiety (-5.2) and worsening in Physical Function, Applied Cognition and Fatigue (-5.4, -6.9 and +8.2 respectively). Between baseline and the latest post-surgery time point, we saw improved Anxiety (-5.9) and worsened Physical Function, Applied Cognition and Fatigue (-5.9, -3.6 and +4.3). Depression changed minimally. Conclusions: Using PROMIS, patients on I-SPY2 experienced worse Physical Function, Applied Cognition and Fatigue but improved Anxiety during and after treatment compared to baseline. While changes in Applied Cognition and Fatigue recovered partially after treatment, Physical Function scores did not, suggesting potential for longer term impact on quality of life. Clinical trial information: NCT01042379 . [Table: see text]
Risk of early mortality in patients with cancer experiencing adverse financial events.
Authors: Khan Hiba , Li Li , Yu Kaiyue , Fedorenko Catherine R. , Kreizenbeck Karma L. , Khor Sara , Kestner Shannon , Ramsey Scott David , Shankaran Veena 
Published: June 2023

6503 Background: Patients with cancer are at higher risk for experiencing severe adverse financial events (AFEs) compared to similar individuals without cancer. Among persons with cancer, those experiencing bankruptcy have poorer survival. In this study, we sought to determine whether AFEs less severe than bankruptcy have an increased risk of death among patients with cancer. Methods: Records for all patients diagnosed with a solid tumor between 2013 and 2018 were identified through the Western Washington State SEER cancer registry and were linked to quarterly credit reports from TransUnion. Patients who survived at least 2 years from date of diagnosis were included. Cancer patients experiencing any AFE, defined as third-party collections, charge-offs, delinquent mortgage payments, tax liens, foreclosures, and repossessions within 2 years of diagnosis were compared with cancer cases who did not experience these events. After adjusting for age, sex, race, area deprivation index, rural/urban status, marital status, AFE at diagnosis, cancer type, and stage of cancer, we fit a Cox proportional hazards model to examine the relationship between AFEs and overall survival, and also fit a separate model for each cancer type. Results: A total of 64,637 patients were diagnosed with cancer between 2013 and 2018, and survived 2 years post-diagnosis. The overall population was 84% white, 53% female, and had a median age of 64. 12,698 patients experienced any severe financial event. The adjusted hazard ratio for mortality among patients who did versus did not experience an AFE was 1.22 (95% CI, 1.145-1.299, p <0.001), with age >65, Asian/Pacific Islander race, greater area deprivation index, and higher stage of cancer associated with increased risk for mortality. Among differing cancer types, patients with prostate cancer who experienced an AFE had the highest risk of death compared with patients with prostate cancer who did not experience an AFE (HR 1.703,95% CI, 1.395 to 2.08, p <0.001). Conclusions: Patients with cancer who experience severe AFEs within 2 years of diagnosis are at higher risk for mortality after adjusting for sociodemographic and clinical factors. When comparing within separate cancer groups, this association is strongest among patients with prostate cancer. Current analysis is limited by restricting population to those who survived at least 2 years after diagnosis; future work will expand population to include patients who died at any time point. Further research should investigate mechanisms for this increased mortality risk, to inform potential interventions and policy solutions, especially among the prostate cancer population. [Table: see text]
Health insurance continuity and survival among children, adolescents, and young adults with blood cancer: An analysis based on SEER-Medicaid data.
Authors: Ji Xu , Zhang Elyse Xinyue , Bai Shasha , Stock Wendy , Yabroff Robin , Mertens Ann C. , Castellino Sharon M. 
Published: June 2023

6504 Background: Many previously uninsured patients do not receive Medicaid coverage until the point of, or after, cancer diagnosis, which may delay access to life-saving therapies. We examined whether continuous Medicaid coverage prior to diagnosis (vs. gaining Medicaid at, or after, diagnosis) provides a survival benefit in children and adolescents/young adults (AYAs) newly diagnosed with blood cancer. Methods: Using the linked SEER cancer registry-Medicaid enrollment data, we assessed coverage during the six months prior to, the month of, and the six months after cancer diagnosis (13-month window) in 29,873 children and AYAs (aged 0-39 years) diagnosed with blood cancer in 2006-2013. To measure insurance continuity, we categorized patients who had: (1) continuous Medicaid (enrolled for ≥6 months prior to through diagnosis), (2) newly gained Medicaid at/after diagnosis (enrollment only at or ≤6 months after diagnosis), (3) other patterns of noncontinuous Medicaid, (4) private insurance, (5) other insurance, or (6) uninsured/unknown insurance. The last three groups contain patients in SEER not linked to Medicaid enrollment data; these patients’ insurance at diagnosis was available in SEER. Kaplan-Meier (KM) survival curve of 5-year overall survival was stratified by insurance continuity category. Associations between insurance continuity and survival were examined using Cox proportional hazard models, for all blood cancers combined and by cancer type (leukemia, lymphoma, myeloma). Models also adjusted for age, sex, race/ethnicity, rurality, neighborhood socioeconomic status, and diagnosis year. Results: Of our sample, 44.1% had private insurance, followed by Medicaid (28.3%), other insurance (9.6%), and no insurance (3.9%) or unknown insurance (14.1%). Of Medicaid-insured patients, 44.9% had continuous Medicaid, 38.9% gained Medicaid at/after diagnosis, and 16.2% experienced other noncontinuous patterns. The KM-estimated 5-year survival probability was 89.3% for privately insured patients, followed by other insurance (88.1%), continuous Medicaid (78.2%), other patterns of noncontinuous Medicaid (74.3%), and newly gained Medicaid at/after diagnosis (70.6%; p<0.001). In multivariable analysis, patients who newly gained Medicaid at/after diagnosis had a hazard ratio (HR) for death of 2.63 (95%CI=2.42-2.87), whereas patients with continuous Medicaid and those with other noncontinuous patterns had HRs of 2.08 (95%CI=1.90-2.28) and 2.31 (95%CI=2.04-2.60), respectively, compared to privately insured patients. This finding persisted across blood cancer types. Conclusions: Less than half of Medicaid-insured children and AYAs with blood cancer had continuous insurance coverage prior to and following cancer diagnosis. Lacking continuous Medicaid coverage was associated with inferior survival.
Association of medical debt and cancer mortality in the US.
Authors: Hu Xin , Zheng Zhiyuan , Shi Kewei Sylvia , Yabroff Robin , Han Xuesong 
Published: June 2023

6505 Background: Medical debt is associated with non-medical and medical financial hardship, such as food and housing insecurity, as well as delaying or forgoing recommended health care. This study examines county level associations between medical debt and cancer mortality in the US. Methods: We conducted a county level ecological study, using 2018 medical debt data from the Urban Institute linked with 2015-2019 mortality data from the National Centers for Health Statistics. All US counties with non-missing information on medical debt and other covariates were included. Primary outcomes were age-adjusted mortality rates per 100,000 person-years for all malignant cancers and by cancer type. Multivariable linear mixed models were used to estimate the association of percent of population with medical debt with cancer mortality, adjusting for county level sociodemographic characteristics. Additional analyses examined the association between median amount of medical debt per person and cancer mortality for the subset of counties with these data. Models were weighted by county level population size, with standard errors clustered at the state level. Results: Among 2,943 counties, an average of 19.8% of the population had medical debt, ranging from 0% to 53.6% across counties. Higher percentages of the population with medical debt were observed in non-metro areas, counties with higher percentages of non-Hispanic Black people, adults without high school education, living below poverty level, uninsured, or unemployed. One percentage point increase in the population with medical debt was associated with a 1.12 (95% CI=1.02-1.22) increase in age-adjusted mortality rate (per 100,000 person-years) for all malignant cancers, a 0.47 (95% CI=0.43-0.52) increase for lung cancer, a 0.09 (95% CI=0.07-0.11) increase for colorectal cancer, and a 0.06 (95% CI=0.04-0.07) increase for female breast cancer. Among 1,949 counties with the amount of medical debt available, each $100 increase in median medical debt was associated with a 0.86 (95% CI=0.52-1.21) increase in age-adjusted mortality rate for all malignant cancers. Associations between increases in median medical debt and increased mortality rates were statistically significant for major cancer types. Conclusions: Medical debt is associated with significantly higher cancer mortality at the county level. Policies increasing access to affordable health care may improve cancer outcomes. [Table: see text]
Practice- and provider-level inequities in next-generation sequencing (NGS) testing by race/ethnicity for patients (pts) with advanced non-small cell lung cancer (aNSCLC) treated in the community setting.
Authors: Vidal Gregory A. , Jain Neha , Fisher Aaron , Sheinson Daniel , Lofgren Katherine T , Ma Esprit , Yu Elaine , Comment Leah , Miksad Rebecca A. , Daniel Davey B. 
Published: June 2023

6508 Background: Inequities in NGS testing in pts with aNSCLC are well documented. Here, we examined elements of total inequity in NGS testing for pts with aNSCLC treated in the US community setting, in terms of within and between inequities at the practice and provider level. Methods: Using the nationwide, deidentified, electronic health record–derived Flatiron Health database, we studied pts with aNSCLC diagnosed in April 2018 or later and treated in the community setting. Testing quality was defined as an NGS result reported ≤60 days after advanced diagnosis. Total-practice inequity was the average percentage-point (pp) difference in the rate of testing quality for non-Latinx Black or Latinx pts compared with non-Latinx white pts. Within-practice inequity was the average pp difference in testing rates at a practice weighted by overall practice enrollment. Between-practice inequity was the covariance between a practice's overall testing rate and the extent of practice-level racial/ethnic underrepresentation. We estimated these inequities at the practice and provider levels using a Bayesian approach. Results: Our study included a total of 12,045 pts (9,981 non-Latinx white, 1,528 non-Latinx Black, and 536 Latinx pts). Both within- and between-practice inequities contributed to total inequity in NGS testing for non-Latinx Black and Latinx pts compared with non-Latinx white pts (Table). For non-Latinx Black pts, between-practice inequities were estimated as 57% (3.77 pp/6.56 pp) of the total inequity in NGS testing; for Latinx pts, the contribution of between-practice inequity was 39% (3.13 pp/8.01 pp). At the provider level, between-provider inequities were the dominant contributor to total inequity in NGS testing for both non-Latinx Black and Latinx pts, accounting for 86% (5.88 pp/6.87 pp) of total provider-level inequity for non-Latinx Black pts and 64% (5.36 pp/8.43 pp) for Latinx pts. Conclusions: Inequities within and between practices, as well as between providers, are meaningful contributors to total inequity in NGS testing. Additional healthcare equity initiatives are needed to identify potential causes for lagging practices/providers to address inequities in NGS testing. [Table: see text]
Mediators of racial/ethnic inequities in clinical trial participation among US patients with cancer, 2011-2022.
Authors: Guadamuz Jenny , Wang Xiaoliang , Altomare Ivy , Calip Gregory Sampang 
Published: June 2023

6511 Background: In 2024, the US Food & Drug Administration will require drug sponsors to implement plans to improve racial/ethnic diversity in clinical trials. While minoritized populations are less likely to participate in cancer trials, it is unknown whether social determinants of health (SDOH) explain these inequities. Here we identified SDOH factors that contribute to racial/ethnic inequities in clinical trial participation among patients with 22 solid and blood cancers. Methods: This retrospective study used the nationwide Flatiron Health electronic health record-derived de-identified database of adult patients with cancer (2011-2022). Area-level SDOH ( e.g., predominant race/ethnicity [proxy for segregation]) contextualizing where patients live were defined using the American Community Survey tract data. Patients were followed from cancer-specific diagnosis to clinical study drug receipt (proxy for trial participation), death, or last recorded activity. Associations of race/ethnicity and SDOH factors on trial participation were assessed using Cox proportional hazards models adjusted for clinical factors (diagnosis year, age, sex, performance status, stage, and cancer type). To elucidate which SDOH explain racial/ethnic inequities, mediation analysis was performed using nonlinear multiple additive regression trees models. Results: This study included 233,556 patients (median age: 68 years, 50.0% women; 65.6% non-Latinx [NL] White, 8.5% NL Black, 5.0% Latinx). Black and Latinx patients were more likely to live in marginalized areas ( i.e., disproportionately minoritized, low socioeconomic status, limited English proficiency, and low vehicle ownership) than White patients. Overall, 4.9% of patients participated in trials. Fewer patients from community practices or in marginalized neighborhoods participated in trials. Black (3.4%; hazard ratio [HR] 0.55, 95% confidence interval [CI]: 0.51- 0.60) and Latinx patients (3.6%; HR 0.57, CI: 0.52-0.63) were less likely to participate in trials than White patients (5.8%). Black-White trial inequities were substantially mediated (58%, CI: 47%-68%) by practice type ( e.g., community vs. academic center, 13%, CI: 10%-15%) and area-level SDOH ( e.g., segregation [33%, CI: 25%-44%] and vehicle ownership [12%, CI: 8%-15%]). Mediators explained 55% (CI: 38%-71%) of the Latinx-White trial participation inequity; area-level SDOH—including segregation (29%, CI: 13%-37%), limited English proficiency (15%, CI: 9%-25%), and vehicle ownership (8%, CI: 5%-12%)—were the most important mediators. Conclusions: SDOH factors, especially manifestations of structural racism such as segregation, explained a substantial proportion of racial/ethnic inequities in trial participation among patients with cancer. These findings can inform targeted efforts to increase racial/ethnic diversity in cancer trials.
Enhancement in line of therapy (LoT) derivation from real-world data (RWD) from electronic health records (EHR) via integration of medical claims data.
Authors: Agrawal Smita , P B Avinash , George Rohini , Singh Neeraj , Soni Megha , Chakkrapani Sangavai , Priya Vandana , Vaidya Vivek Prabhakar 
Published: June 2023

6514 Background: Clinical RWD derived from EHRs is becoming increasingly important for clinical research, trial design, regulatory decisions etc. These applications require identification of lines of therapy (LoT) which are typically not captured in EHR and must be abstracted from other clinical and medication data. EHR data has significant missingness which can be complemented with other data sources such as medical claims data. In this study, we demonstrate how our proprietary line of therapy algorithms for solid cancers show significant improvements when built using integrated EHR and claims data when compared to EHR data alone. Methods: For this analysis, ConcertAI’s RWD360 dataset integrated with a large administrative open-claims dataset (>90% overlap) for 14 solid cancer indications (Breast, Bladder, Lung, Prostate, Pancreas, Melanoma, Liver, Head & Neck, Renal, Colorectal, Melanoma, Ovarian, Thyroid, Endometrial) was used. The date of advanced/metastatic diagnosis used as the index date for LoTs was derived from the EHR data and medications from both EHR and claims data were used. We ran our LoT algorithms on EHR data with and without claims data and evaluated the impact of integrating claims data on the quantity and quality of LoT output. Results: The inclusion of medication data from claims significantly increased (7-22%) the number of patients for which LoTs could be extracted from the EHR data. Furthermore, we observed increases in number of lines per patient, length of lines and medications per line across cohorts. The distance between index date and 1st line start date was shortened in a subset (2-12%) of patients as a result. In a small fraction of cases, we even observed removal of false lines as some of the lines moved to adjuvant/neoadjuvant setting by filling in missing medication from claims. Overall, 7-39% patients in the LoT cohorts were impacted by addition of claims. Results for a few cancer types are presented in Table 1. We also compared the top LoTs derived from the integrated dataset against the standard of care for that cancer and observed very good concordance. Conclusions: Deriving LoTs by integrating data from multiple data sources such as EHR and claims can significantly improve its accuracy. [Table: see text]