bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relationship between immune phenotype and treatment selection of Chemo-IO vs. IO-only in TPS-high NSCLC using hypothetical test-and-control group generation based on survival data extracted from phase III trials.
Authors: Oum Chiyoon , Moon Jimin , Seo Melody SeungHui , Lim Yoojoo , Ahn Chang Ho , Nesmith Ken , Ock Chan-Young 
Published: May 2024

e13569 Background: Immunotherapy has become the treatment of choice for the first-line treatment of unresectable NSCLC with high PD-L1 tumor proportion score (TPS). However, the optimal treatment regimen, particularly when to add chemotherapy, remains unclear. To evaluate whether an immune microenvironment biomarker could help guide the treatment regimen, we have explored computational hypothetical analysis applying historical proportion of inflamed immune phenotype (IIP) to published outcome data of clinical trials. Methods: R package “IPDfromKM” was used to reconstruct raw time-to-event outcomes from Kaplan-Meier (KM) curves. Data from KN-189 and KN-407 for anti-PD1 with chemotherapy (Chemo-IO), and KN-024 and KN-042 for anti-PD1 (IO-only) in the first-line treatment of NSCLC were included for analysis. Overall survival (OS) data were computed from KM of pts with PD-L1 TPS of ≧ 50% from these studies (KN-189 n=132, KN-407 n=73, KN-024 n=154, KN-042 n=299). It was assumed that Lunit SCOPE IO classified pts into IIP or non-IIP at a ratio of 57% vs. 43%, based on previous analysis. One hundred hypothetical sets of Chemo-IO and IO-only groups satisfying hazard ratio (HR) of IIP to non-IIP as 0.81 and 0.61 (± 0.025) while maintaining the proportion of IIP were randomly generated. A total of 10,000 (100 x 100) combinations of cohorts were analyzed to test our hypothesis of superiority of Chemo-IO over IO-only in non-IIP as well as non-superiority of Chemo-IO over IO-only in IIP. Results: Patient-level OS data for a total of 205 pts with Chemo-IO and 453 pts with IO-only were reconstructed. The median OS was favorable in Chemo-IO vs IO-only (HR 0.73, 95% confidence interval [CI] 0.54-0.99, p = 0.045). From 10,000 hypothetical combinations of cohorts, the median HR of Chemo-IO compared to IO-only was 0.61 (interquartile range [IQR] 0.60-0.63) in non-IIP group, whereas that was 0.86 (IQR 0.84-0.89) in IIP group (Table). The upper 95% bound of HR was < 1 for 8,611 combinations of non-IIP groups, but > 1 for all possible combinations of IIP groups. Given a non-superiority margin of 1.37(=1/0.73), 8,306 combinations were positive for the upper 95% bound of HR < 1.37. Overall, 6,917 matched combinations supported our hypothesis. Conclusions: From the computational analysis, 69.17% cases of hypothetical combinations support that Chemo-IO would be superior to IO-only in non-IIP group, whereas Chemo-IO would not be superior to IO-only in IIP group, implying a potential value of confirmatory biomarker analysis in providing evidence for regimen selection. [Table: see text]
Implementation of a virtual tumor board through VA National TeleOncology service.
Authors: Chiba Akiko , McWhirter Gina , Moss Haley , Colonna Sarah Violet , Rodgers Thomas David , Jonna Sushma , Johnson Margaret O , Van Echo David C. , Kelley Michael J. , Passero Vida Almario 
Published: May 2024

e13571 Background: Multidisciplinary tumor boards are critical to optimize treatment of patients with malignancies. The Veterans Affairs (VA) National TeleOncology (NTO) service was created to provide equitable, state of the art oncologic care virtually to centers across the largest integrated health care system in the US. According to American Society of Clinical Oncology report, 66% of rural counties have no oncologist. Fifty-six percent of Veterans who received cancer care through NTO in 2022 lived in rural areas. We describe a virtual multi-institutional, multidisciplinary tumor board across several malignancies. Through NTO, virtual tumor board (VTB) demonstrates a novel approach to leveraging nationwide oncologic expertise to support patients receiving cancer care at VA Medical Centers (VAMC) across the US. Methods: A VTB was conducted with participation of multiple VAMCs throughout the US. There were five separate VTB oncologic sub-specialties: hematologic, breast/gynecologic, CNS, thoracic and cutaneous malignancies. Each case was presented by the treating provider or designated representative. Pathology and pertinent imaging were reviewed and presented by disease-specific expert specialists. Core attendees included medical oncologists, surgical oncologists, radiation oncologists, pathologists, radiologists, genetic counselors, clinical pharmacists, advanced practice providers and nurses. VTB records were analyzed from VTB inception on March 1, 2022 to February 1, 2024. Data collected included attendance and case presentation information. Results: A total of 113 VTB sessions were conducted every 2 weeks to monthly depending on specialty (35 hematologic, 32 beast/gyn, 24 thoracic, 13 CNS, 9 cutaneous malignancies) with a total of 233 patient cases presented from 51 VA facilities in 33 states and Puerto Rico. The total unique attendees were 468 in 41 states, DC and Puerto Rico. The highest case volume per VA location were from Durham, Houston, Pittsburgh, Sioux Falls and Salem. The highest attendee locations included Durham, Boston, Palo Alto, Pittsburgh, and Richmond. Conclusions: This study demonstrates the feasibility of a national multi-institutional, multi-disciplinary VTB. Tumor board sessions provide access to specialized oncologic expertise with the goal of improving cancer care across the entire VA healthcare system. Further growth of national VTB to increase participation at additional sites and incorporated discussion of clinical trials is ongoing. Future research evaluating quality and outcomes for those cases discussed at VTB will be considered.
Financial impact of fixed-dose versus weight-based dosing approaches for pembrolizumab and nivolumab in the Oncology Care Model for The US Oncology Network (The Network).
Authors: Fernandez Jennifer , Indurlal Puneeth , Garey Jody S. , Wilfong Lalan S. 
Published: May 2024

e13573 Background: PD-1/PD-L1 antagonists, pembrolizumab and nivolumab, are being used in an increasing number of cancer types. The FDA initially approved both agents based on a weight-based dosing (WBD) approach, but subsequently included a therapeutically equivalent fixed-dose (FD) approach for most indications. The Oncology Care Model (OCM) was a value-based care (VBC) program from Medicare that required total cost of care (TCOC) accountability from participating practices. We evaluated the impact of using the FD vs WBD on TCOC in the OCM for both pembrolizumab and nivolumab. Methods: Using Medicare claims data for 14 OCM-participating practices in The Network from July 2016 to June 2022, and corresponding weight data from the Electronic Health Record (EHR), we evaluated the distribution of patient weights, use of FD vs WBD, and the financial impact of the use of FD vs WBD vs weight-based dosing capped at the fixed dose (WBDc). Results: Summary statistics are shown (Table). In most cases, the FD was higher than the WBD for both nivolumab and pembrolizumab. FD led to an increase in drug cost of 8.4% for nivolumab and 27.2% for pembrolizumab, corresponding to a 11.3% increase in TCOC for the episodes where FD nivolumab and pembrolizumab were used. Conclusions: Fixed-dosing approaches for commonly used high-cost anti-neoplastic drugs can drive increases in TCOC. The use of WBD could lead to a decrease in drug cost and 11.3% savings of TCOC within OCM-like models, likely without impacting clinical outcomes. These findings encourage practices in risk-bearing models to reflect on the widespread adoption of FD. The withdrawal of varying sizes of vials can limit the use of WBD. Pharmaceutical manufacturers should assist with WBD by providing multi-use vials and varying sizes. The feasibility of using the WBD for pembrolizumab with an every 6-week regimen remains to be explored. [Table: see text]
Workforce evaluation in the field of radiation oncology: National data from a limited-resource setting.
Authors: Karapetyan Aren , Karapetyan Nune , Mkhitaryan Sergo , Hovhannisyan Julieta , Manukyan Narek , Harutyunyan Lilit , Tamamyan Gevorg , Sargsyan Lilit , Saghatelyan Tatul 
Published: May 2024

e13576 Background: The workforce shortage in the field of radiation oncology is a critical challenge in developing countries. This study's main objective was to evaluate the workload of radiation oncologists (RO) in Armenia. Methods: In this cross-sectional study, the adapted questionnaire from Gunasekera et al. (2021) survey was used. The study setting was the only two RO centers operating in Armenia (public and private). For data collection purposes, census sampling was done and self-administered questionnaires were distributed. The data collection time frame was 15-25 July 2023. Descriptive statistics was done to analyze and present data through portions. Results: In total, data on 16 ROs and residents was collected. The ratio of ROs per million population was 6 in 2023, in Armenia. Primary consultations in median lasted 25 minutes (range: 10-40 mins). Similarly, consults of the patients on active treatment lasted in median of 12.5 minutes (min-60 mins). The median duration of follow-up oncology care consults was 7.5 minutes per day (range: 10- 40 mins). In terms of clinic hours, physicians in public settings reported spending a median of 50 hours per week. According to the respondents, on average radiation therapy specialists allocated 50% of their working time to providing direct patient care, 35% to fulfilling administrative responsibilities, 5% to educational and teaching endeavors, and 10% to conducting scientific research. The median radiation therapy delay was 1 week. Namely, treatment delay in the non-metastatic setting was 1․19, and in the metastatic setting 1 week. By the ROs, the most common barriers to delivering cancer care were treatment high cost (68%, n=11), lack of access to imaging (18.7%, n=3) and pathological (18.7%, n=3) diagnostic procedures, and lack of physicians' time to dedicate to patients (31%, n=5). Conclusions: Our study offers some insight into the radiation oncology workload in Armenia. From this research, we conclude that Armenian healthcare is understaffed in terms of radiation oncologists and medical physicists compared to other upper-middle-income and high-income income countries. Moreover, the median daily workload of the ROs exceeds the upper limit of the amount of consults suggested by the international societies. Hence, further research is required for a detailed evaluation of the RO workforce and specific intervention development that will be able to address this issue in Armenia and other resource-limited settings.
Using an AI trial screening tool to assess language as a barrier for enrollment of underrepresented minorities in cancer clinical trials.
Authors: Corvese Laura , Weiss Christina , Huie Sharon , Zhou Bin , Deng Yanhong , Gong Guannan , Kunz Pamela L. , Fischbach Neal A. 
Published: May 2024

e13586 Background: A major limitation of clinical trials is limited generalizability due to low enrollment of underrepresented minorities (URM). The percentage of patients who are non-English speaking (NES) is higher in URM and language barriers may discourage providers to offer participation in clinical trials. We have developed an artificial intelligence-based system to identify patients eligible for clinical trials, Clinical Trials Patient Matching (CTPM). This platform offers an ideal opportunity to explore bias in enrolling patients on clinical trials (CT). Among patients identified by CTPM as eligible for CT we assessed the impact of NES on trial enrollment. Methods: Individual algorithms were constructed within CTPM to identify patients potentially eligible for two trials open at Yale Cancer Center, DARE (NCT04567420) and EMBER-4 (NCT05514054). Eligibility was confirmed by Clinical Trials Office staff and notification of eligibility was communicated to the treating provider. Retrospectively, we evaluated the impact of NES status on clinical trial enrollment. This analysis occurred between 9/1/2022 and 11/30/2023 and all study-related activities were approved by the Yale IRB. Results: Out of the 13,466 unique patients presenting to the Yale Cancer Center and Smilow Cancer Network breast program within the period of study, 5.1% were NES and 94.9% were English speaking (ES). Of the patients identified by CTPM as eligible for the 2 clinical trials 7.9% were NES and 92.14% were ES. However, out of patients enrolled, only 1.59% were NES and 98.41% were ES. For the DARE trial, enrollment rate was 38.3% for ES vs 0% for NES (p=0.0081). For the EMBER4 trial, enrollment was 13.4% for ES vs 14.3% for NES (p=1.0). The aggregate data for both trials revealed an enrollment rate of 29.4% for ES vs 5.6% for NES (p=0.0285). Conclusions: We found that patients who identify as NES were less likely to be enrolled in cancer clinical trials. CTPM appears to be an effective tool to prospectively identify underrepresented populations in clinical trials and serve as a platform for developing interventions to address disparities in trial participation, including language. [Table: see text]
Natural language processing for automated breast cancer recurrence detection and classification in computed tomography reports.
Authors: Lee Jaimie , Zepeda Andres , Arbour Gregory , Isaac Kathryn V. , Ng Raymond T. , Nichol Alan 
Published: May 2024

e13591 Background: Relapse is a major concern for oncologists and breast cancer survivors that necessitates additional treatment and often leads to mortality. Cancer registries routinely track cancer mortality, but few monitor for relapse because of logistical challenges and prohibitive costs. In this context, Natural Language Processing (NLP) is a promising tool. Merging artificial intelligence with linguistics, NLP can rapidly analyze vast volumes of text in electronic health records. This capability of NLP is particularly valuable for Computed Tomography (CT) scans used in breast cancer care. CT scans are routinely used to characterize breast cancer progression and are described in transcribed dictations by radiologists. We aimed to apply NLP to these text reports to identify breast cancer relapses. Objective: To automate breast cancer relapse detection and classification in CT text reports using NLP. Methods: We analyzed 1,445 CT text reports from patients diagnosed with breast cancer between January 1, 2005, and December 31, 2014. These reports underwent manual review by trained human annotators. Text was annotated to identify terminology defining local, regional, and distant breast cancer relapses. Annotated reports were partitioned into a training-validation set (90% cohort) and a test set (10% cohort) for NLP model development. Results: In our dataset of 1,445 CT text reports, 72 (5.0%) were classified as local relapse, 97 (6.7%) as regional relapse, and 743 (51.4%) as distant relapse. The performance of our NLP model using the training-validation dataset can be summarized by the following metrics and 95% confidence intervals: 94% (±3.2) accuracy for detection and 96% (±2.9) accuracy for classification. The performance of our NLP model was confirmed using the test dataset, with 90% (±4.5) accuracy for detection and 91% (±6.3) accuracy for classification. For reference, all metrics are outlined (Table). Conclusions: Our model for identifying regional and distant relapses in CT reports had excellent performance, but had lower sensitivity for local relapses, posing a risk of false negatives. Automating the identification and classification of breast cancer relapses, if used retrospectively, can enhance cancer registry data about patient outcomes and, if used prospectively, holds the potential for enhancing patient care. [Table: see text]
An unsupervised H&E-based machine-learning approach for precise prediction of tumor microenvironment subtypes.
Authors: Sarachakov Alexander , Tyshevich Andrey , Belozerova Anna , Postovalova Ekaterina , Bagaev Alexander , Kushnarev Vladimir , Fowler Nathan Hale 
Published: May 2024

e13593 Background: Precision oncology increasingly relies on accurate classification of the tumor microenvironment (TME), which influences therapeutic responses and prognostication in cancer. While histological observation via H&E staining remains the gold standard due to the detailed spatial context and morphological information provided, its lack of reproducibility limits its use in clinics. Next-generation sequencing (NGS) reliably provides valuable insights into the molecular landscape of tumors. Here we debut an unsupervised machine-learning approach that leverages both the rich detail in H&E slides and the reproducibility of NGS to enhance TME classification. Methods: An ML-based image analysis algorithm was developed using a convolutional neural network (CNN) deep learning model. Using over 600 images from the TCGA COAD and READ datasets and from 139 colorectal cancer (CRC) samples in an internal cohort, we trained the CNN model to curate features from small patches of whole slide images (WSIs). Then, clusters were created from these H&E WSIs without prior labeling. The clusters were validated by experienced pathologists to confirm cluster homogeneity and filter artifacts. We trained a classifier to categorize molecular features from the WSIs into TME subtypes. Performance was measured using the area under the receiver operating characteristic curve (AUC-ROC), based on the model's ability to correctly classify the Fibrotic (F) and Immune Desert (D) TME subtypes (1), as well as MSI status identified by whole exome sequencing. The F subtype corresponds with high stromal content, low immune infiltration, and the worst prognosis. The D subtype corresponds with a cold immune TME and an intermediate prognosis. Progression-free survival (PFS) data from TCGA were used to examine prognostic significance. Results: We obtained 0.86, 0.79, 0.72, and 0.71 AUC scores for predicting the ID and F TMEs, MSI status, and KRAS mutation status, respectively. PFS data analysis showed our H&E-based approach to yield similar or better prognostic significance compared to NGS-based TME classification (ID vs others, p=0.05 (H&E) and 0.2 (NGS); F vs others, p=0.07 (H&E) and 0.09 (NGS)). Our findings showed a notable correlation with RNA- and DNA-based predictions of ID and F TMEs and MSI, with enhanced precision and more robust subtype identification while achieving high embedding consistency. Conclusions: By harnessing the inherently rich detail in histological images, our H&E-based approach complements NGS-based TME analysis. Our findings advocate for the integration of advanced image analysis into the standard TME characterization workflow to refine prognostic and therapeutic strategies for CRC. We will expand this algorithm for use with other cancer types to realize its potential as a promising adjunct to NGS for precise TME classification. 1. Bagaev et al., 2021.
Impact of Exigent Research Network’s partnership with the Tempus AI TIME Program on the screening and matching of patients for clinical trials.
Authors: Gajra Ajeet , Mallahan Samantha , Blau Sibel , Peguero Julio Antonio , Shoukier Mahran , Kanaan Mohamad , Huynh Chanh Trung , Efiom-Ekaha Daniel N. , Vankayala Hema M. , Kitchen Kyle , Kendall Stephan Disean , Orsini James Michael , Babu Sunil , Hellyer Jessica , Weart Thomas , Meyer Janelle Marie , Clifford Christian , Skelly Danielle , Patnaude Emily , Cooney Matthew M. 
Published: May 2024

e13599 Background: Exigent Research is comprised of independent community oncology practices with a central patient data repository to support clinical research. The Tempus AI TIME program provides clinical trials solutions including a diverse trial portfolio, TApp an AI powered patient matching platform, patient tracking LINK portal, and a rapid trial activation process. This partnership facilitated Tempus AI to continuously search the data repository and enabled Exigent to increase trial enrollment. Methods: TApp is a clinical trial matching platform that utilizes patient data, trial eligibility criteria, and NLP models to execute AI powered matching of subjects to trials. TApp searches the entire patient population for TIME trial matches. New patient clinical information or changes to trial eligibility criteria triggers a new TApp search and matches are reviewed by Tempus AI nurses. Trials were opened in an accelerated “just in time” manner if a subject was ready immediately or prospectively prior to a first patient being identified. Data collected included trial activations, number of patients, available studies, patient and trial updates, TApp searches, nursing effort, matches, and consents. Interventional Phase I to III trials and observational studies were included. Results: From 2022 - 2023, there were 16 Exigent sites in the TIME network. The patient population screened was 244,986 matched to 189 clinical trials. There were 3,179,079 changes to individual patient records and 2,152 trial updates. This triggered 216,712,093 unique TApp searches and 33,083 matches reviewed by nurses. In total, 1,312 patients matched to observational studies and 6,040 patients for interventional trials. Cumulatively, nurses spent 6,617 hours on patient screening resulting in a $330,830 cost. There were 71 trial activations resulting in 312 patient consents (169 observational, 143 interventional). The average duration for study activation was 12.3 days for a just in time activation and 33.8 days for a prospective activation. Conclusions: Exigent Research leveraged a central data repository with the Tempus AI TIME program to continuously screen a large patient population for 185+ trials. During a two-year period, 215M+ unique TApp searches occurred which resulted in over 310 patient consents with rapid activation times. Future clinical trial strategies should partner AI screening technologies with robust trial infrastructure solutions to maximize enrollment success. [Table: see text]