tbuckley/cabot-search huggingface dataset:
Automated survivorship care plan generation using large language models.
Authors: Jathurshan Pradeepkumar, David Liebovitz, Jyoti D. Patel, Jimeng Sun
Journal: Journal of Clinical Oncology (2024)

e13595 Background: Cancer survivorship, particularly from post-treatment to end-of-life, faces challenges such as communication gaps and coordination issues, leading to suboptimal care. A survivorship care plan (SCP) is a summarized record of patient cancer history and treatments, along with follow-up care recommendations. Despite extensive survivorship care guidelines from national cancer organizations, their adoption remains limited. Moreover, creating SCPs is time-intensive and burdensome for healthcare providers due to workforce limitations, hindering efficient personalization of care plans. Our study aims to address these issues by employing large language models (LLMs) to generate personalized SCPs. Methods: We introduce SurvGPT, a customized LLM designed to generate personalized SCPs focusing on its two main sections: the treatment summary and the follow-up care plan. Initially, our method extracts information from the structured and unstructured patient records to generate the treatment summary. Subsequently, it retrieves relevant context from a survivorship knowledge base crafted from existing medical literature and guidelines to generate a customized follow-up care plan. To mitigate LLM-related challenges such as hallucinations, our method ensures that the generated follow-up care plan is grounded on existing medical knowledge with a self-checking mechanism. Results: We created synthetic patient data based upon clinical inputs by a medical oncologist and an internist. We conducted a preliminary study using synthetic lung cancer patient EHR data to evaluate our approach. The evaluation focused on several key dimensions to validate the quality of the generated SCPs, including comprehensiveness, personalization, clarity, and actionable recommendations. The results presented in the table suggest promise for this approach yet also need for additional refinements both in degree of details extracted for ongoing care and then in formatting for effective use. The results also illustrate the complexity of generating synthetic cases for this work whose similarity to real patient care improved with iteration yet remained on occasion perceptibly different from current practice as noted by our clinical oncologist team member. Conclusions: In conclusion, our study demonstrates the potential of SurvGPT for generating personalized SCPs. The results of the preliminary results suggest that this approach is technically feasible with further validation required using real patient data. [Table: see text]
Workforce evaluation in the field of radiation oncology: National data from a limited-resource setting.
Authors: Aren Karapetyan, Nune Karapetyan, Sergo Mkhitaryan, Julieta Hovhannisyan, Narek Manukyan, Lilit Harutyunyan, Gevorg Tamamyan, Lilit Sargsyan, Tatul Saghatelyan
Journal: Journal of Clinical Oncology (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.
Delays in receipt of neoadjuvant chemotherapy compared to upfront surgery in patients with operable breast cancer.
Authors: Margaux Wooster, David DeStephano, Melissa Beauchemin, Shikun Wang, Chin Hur, Jason Dennis Wright, Dawn L. Hershman, Melissa Kate Accordino
Journal: Journal of Clinical Oncology (2024)

e13561 Background: Delays in time from breast cancer (BC) diagnosis to BC treatment initiation (BCTI) including upfront breast surgery (UBS) or neoadjuvant chemotherapy (NACT), can negatively impact BC survival. We evaluated odds of delay in BCTI among patients (pts) who received NACT compared to pts who received UBS. Methods: We conducted a retrospective cohort study using the National Cancer Database. Eligibility included age ≥18 years, diagnosis of non-metastatic BC between 1/1/2010 – 12/31/2020, who were treated with NACT followed by surgery or UBS followed by adjuvant chemotherapy (AC). Pts who did not receive NACT or UBS within 180 days of diagnosis were excluded. The cohort was stratified by first BC treatment (NACT or UBS), and time to NACT (TTNACT) or time to surgery (TTS) was recorded for each patient in days. Pts were categorized into sub-cohorts by receptor status and clinical stage: 1) Hormone Receptor positive/HER-2 negative (HR+/HER2-) lymph node (LN)+; 2) HER-2 positive (HER2+) cT2+ or LN+; and 3) triple negative BC (TNBC) cT2+ or LN+. Logistic regression adjusted for demographic, socioeconomic, and tumor characteristics was used to identify delays in BCTI &gt; 60 days. Multiple imputation was used for missing data. Inverse probability treatment weighting (IPTW) was done as a sensitivity analysis. Results: We identified 218,049 eligible pts, 75% were White, 18% were Black, and 8.6% were Hispanic. Median age was 53 years (range 18-90). There were 145,697 (67%) pts who received NACT with median TTNACT of 32 days (range 0-180), and 9.8% had a delay &gt; 60 days (Table). There were 72,353 (33.0%) pts who received UBS followed by AC, with a median TTS of 33 days (range 0-180), and 13% with a delay &gt; 60 days. In multivariable analysis, pts who received UBS had higher odds of BCTI delays compared to NACT, with OR 1.50 (95%CI 1.42-1.57) [HR+/HER2-], OR 1.49 (95%CI 1.41-1.58) [HER2+], and OR 1.26 (95%CI 1.18-1.34) [TNBC]. Results from IPTW analysis were similar, groups were well balanced, and pts who received UBS had higher odds of BCTI OR 1.50 (95%CI 1.44-1.57) [HR+/HER2-], OR 1.75 (95%CI 1.67-1.83) [HER2+], and OR 1.46 (95%CI 1.38-1.53) [TNBC]. Conclusions: Among high clinical risk BC patients who received surgery and chemotherapy, those who underwent NACT were less likely to experience delays in BCTI compared to pts who received UBS first. Among eligible pts, use of NACT may reduce delays in BCTI. [Table: see text]
A digital pathology AI model to predict immune-oncology biomarker status and pembrolizumab response in a real-world cohort of patients with colorectal and breast cancer.
Authors: Ming Chen, Norsang Lama, Yating Cheng, Eghbal Amidi, Joanne Xiu, Jennifer Ribeiro, Matthew James Oberley, David Spetzler, George W. Sledge
Journal: Journal of Clinical Oncology (2024)

e13617 Background: Deep learning (DL) algorithms have garnered significant attention for prediction of biomarker status in digitized tissue slides. Here, we trained and validated a digital pathology algorithm to predict microsatellite instability (MSI)/mismatch repair deficiency (MMRd) in colorectal cancer (CRC) and PD-L1 status in breast cancer (BC) using a large real-world patient cohort. Methods: H&amp;E and immunohistochemistry (IHC) slides of CRC and BC from Caris Life Sciences (Phoenix, AZ) with known biomarker status were digitally scanned (CRC: N=27,494, 6.6% MSI-high by next-generation sequencing [NGS]; N=25,788, 6.4% MMRd by IHC; BC: N=16,069, 15.8% PD-L1+ by IHC). A transformer-based DL model (Wagner, Sophia J., et al., 2023) was trained to predict biomarker status, with 5-fold cross-validation. Accuracy, F1 score, area under the curve (AUC), and other metrics were calculated comparing the model to NGS/IHC “truth”. Risk stratification was performed on holdout datasets of pembrolizumab-treated CRC (N=422-454) and triple negative breast cancer (TNBC; N=513) pts. Post-treatment overall survival (OS) was calculated from start of pembrolizumab to last contact, per insurance claims data. Hazard ratios (HR) and 95% confidence intervals (CI) were calculated by Cox-Proportional hazards and compared between model and “truth”. Results: When applied to holdout datasets for biomarker prediction, the validated model achieved AUC of 0.78-0.95. In CRC and BC, performance was better among primary specimens than metastatic. For MSI-H/MMRd risk stratification in pembrolizumab-treated CRC, HR for OS were comparable between model and “truth” for primary and metastatic cohorts. When applied to the TNBC holdout dataset, the model performed better than “truth” for PD-L1 risk stratification in pembrolizumab-treated pts. Conclusions: A digital pathology DL model predicts biomarker status with high accuracy and is non-inferior to NGS/IHC for risk stratification of pembrolizumab-treated CRC and TNBC pts. These results support further investigation of AI algorithms developed from large datasets as possible rapid screening tools for biomarker detection in molecular testing programs. [Table: see text]
Artificial intelligence–aided lung cancer screening in routine clinical practice: A pilot of LungFlag at Geisinger.
Authors: Matthew A. Facktor, Yue Jin, Rebecca Maff, Elliot Mitchell, Thanh G. N. Ton, Nicolò Olghi, Milan Obradovic
Journal: Journal of Clinical Oncology (2024)

e13604 Background: Use of low-dose computed tomography (LDCT) in lung cancer screening is recommended for early detection and can reduce mortality. LungFlag (Medial-EarlySign) is a machine-learning risk prediction model that identifies individuals at high risk of non-small cell lung cancer so their care team may recommend LDCT screening. The LungFlag model was developed using data from Kaiser Permanente Southern California and is being implemented in a pilot at Geisinger. Here we provide an overview of the clinical workflow for the LungFlag pilot. Methods: Geisinger has implemented LungFlag into routine clinical practice via integration with the electronic medical record system. This pilot is examining the performance and clinical impact of LungFlag in the first year. LungFlag runs every month to provide risk evaluation for individuals 50 to 80 years old who are eligible for annual lung cancer screening according to US Preventive Services Task Force recommendations. Eligible individuals have a 20 pack-year smoking history, no lung cancer history, and currently smoke or quit within the past 15 years. Patients with cancer history in the past 5 years and those screened or scheduled for screening in the past year are excluded. LungFlag identifies the top 30 individuals with the highest LungFlag scores per month (high-risk patients) and refers them for targeted outreach from a lung cancer screening program nurse navigator. A positive LungFlag result prompts nurse navigator chart review, patient outreach, and coordination with the primary care provider. Flagged individuals are contacted (up to 3 phone calls) and encouraged to receive lung cancer screening. Individuals not flagged by the model (non–high-risk patients) receive usual care consisting of reminders for lung cancer screening during primary care visits without additional targeted outreach and high-risk prioritization. Results: The LungFlag pilot was initiated in August 2023 and will continue until August 2024. As of January 2024, risk profiles of &gt;12,000 patients were analyzed and 180 high-risk patients were identified. Conclusions: This pilot is the first evaluation of the implementation process for the LungFlag model in routine clinical practice. After the pilot, a retrospective observational study is planned to further assess the impact of LungFlag and the associated clinical workflow in identifying high-risk individuals and increasing compliance with LDCT screening among high-risk individuals.
Multi-institutional validation survey on Belong.life's conversational artificial intelligence (AI) oncology mentor, "Dave.
Authors: Talia Golan, Ofer Purim, Danny Rosin, Eli Sapir, Moshe Gatt, Tomer Charas, Ilya Iofin, Ido Wolf, Bozhena Haikin, Daniel A. Vorobiof
Journal: Journal of Clinical Oncology (2024)

e13596 Background: Belong.life, a global oncology social network for patients and caregivers, recently launched “Dave” the first conversational AI oncology mentor and companion. “Dave’s” objectives are to provide uninterrupted support, clarify relevant clinical issues and guide patients and caregivers with relevant and empathetic information and education in all aspects of cancer, from diagnosis to treatment protocols and side effects management. “Dave” underwent training on Belong’s unique and large datasets of patients to physicians, and patients to patients’ interactions, as well as incorporating high quality information from the latest international cancer guidelines, providing it with a robust up-to-date supportive data and a wide understanding of the patients’ cancer journey. Methods: “Dave’s” responses to inquiries from Belong members were subjected to a validation survey conducted by eight oncologists, each specializing in various solid and haematological cancers and affiliated with several medical institutions. From the social network datasets, 471 questions from patients and caregivers were randomly selected and categorized into groups, including Breast, Gastrointestinal and Pancreatic, Genito-urinary, Bone, Haematological cancers, and Radiation therapy. The oncologists assessed the AI mentor's replies for their relevance and helpfulness, aligning them with evidence-based medicine, current recommendations, and guidelines. Results: Results were categorized into positive or negative assessments. Impressively, 432 out of 471 “Dave’s” responses (91.8%) received positive validation for providing suitable and relevant recommendations. Conversely, 39/471 responses (8.2%) received a negative validation, of which only 5/471 (1%) were graded as not at all helpful or relevant. Minor variations were observed within diagnostic groups, with positive validation rates as follows: Genito-urinary 100%, Breast 98.4%, Musculoskeletal 97%, Radiation therapy 88%, Gastrointestinal and Pancreatic 86%, and Haematological cancers 84%. Conclusions: The documented results on 471 patients’ posts are very promising, indicating that “Dave” provides valuable and reliable recommendations, achieving a positive validation rate of nearly 92%, with only 1% graded as not at all helpful or relevant. The findings also offer insights into the AI mentor's performance across distinct medical domains. This validation study provides a solid foundation and adds confirmation that the addition of an AI oncology mentor and companion, like “Dave”, improves patients’ knowledge and coping mechanisms and provides helpful and relevant guidance during their cancer journey, while supporting physicians in the daily management of their cancer patients.
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: Chiyoon Oum, Jimin Moon, Melody SeungHui Seo, Yoojoo Lim, Chang Ho Ahn, Ken Nesmith, Chan-Young Ock
Journal: Journal of Clinical Oncology (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 &lt; 1 for 8,611 combinations of non-IIP groups, but &gt; 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 &lt; 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]
Optimizing clinical research in a regional cancer center network: A survey-based approach.
Authors: Isabel Ruth Preeshagul, Angemael Syldor, Stephanie Lucia Terzulli, Ruth Ann Gordon, Lauren A Evans, Solange Moran, Juliana Eng, Robert Michael Daly, Devika Rao, Parisa Momtaz, Julia P Brockway, Smita Suhas Joshi, Collette Houston, Gregory J. Riely, Diane Lauren Reidy
Journal: Journal of Clinical Oncology (2024)

e13560 Background: Conducting clinical research (CR) within regional care networks (RCNs) of comprehensive cancer centers presents unique challenges. This study aimed to identify and address such barriers at Memorial Sloan Kettering Cancer Center's (MSKCC) RCN, encompassing six sites across New York and New Jersey. Methods: A survey was conducted among medical oncologists at the RCN sites (February 2022-February 2023) to assess their perspectives on research-related challenges. The 23-question survey included multiple-choice, open-ended, and ranking questions regarding protocol availability, consenting processes, staffing support, and more. Results: 92/108 (85%) medical oncologists completed the survey, revealing three key challenges: 1. Limited protocol availability: Only 47% of the time was there a suitable trial available in the RCN for patients interested in clinical trials. Notably, 50% of attendings referred over 5 patients out of the RCN to MSKCC Main Campus for trial enrollment. Identified barriers to opening desired protocols (n=23 in total) included closed to accrual, pending amendments, and sponsor approval issues. 2. Inadequate investigator representation: 278 protocols lacked RCN physicians listed as consenting providers hindering potential patient participation. 3. Insufficient research staff including clinical trial nurses and administrative staff. Only 33% were satisfied with the research support, and 67% of physicians identified lack of clinical research support staff as the biggest barrier to accrual. 58% reported delays of more than one week in screening eligible patients due to insufficient support. Following these observations, interventions were implemented. Thirteen of the 23 studies were opened to accrual, all RCN physicians were added to the 278 protocols, and an analysis of staffing needs increased clinical trial nursing and administrative staff. Therapeutic and non-therapeutic accruals in the Department of Medicine rose by 10.5%, and total research visits increased by 21.4% compared to the previous year. Conclusions: This survey-based approach identified and addressed key barriers to CR within MSKCC's RCN, and in turn, increased research activity was noted. These findings offer a potential roadmap for optimizing research in similar networks by enhancing protocol accessibility, addressing staffing needs, and fostering collaboration. Further investigations are ongoing to address challenges in other clinical departments.