tbuckley/cabot-search huggingface dataset:
Utilization of circulating tumor DNA (ctDNA) testing by race and ethnicity in more than 135,000 patients.
Authors: Andrew A. Davis, Leslie A Bucheit, Nicole Zhang, Christopher Pretz, John H Strickler, Abraham Chachoua, David R Wise, Kwok-Kin Wong, Emeline Mariam Aviki, Tian Zhang, Qian Qin, Keelia M. Clemens
Journal: Journal of Clinical Oncology (2024)

e13702 Background: Studies have shown low rates of tumor genomic profiling in the metastatic population and disparities in cancer care have been observed across races, ethnicities, and ages. We aimed to better understand how ctDNA is being utilized across race, ethnicity, and age in common cancer types using a real-world (RWE) clinicogenomic database. Methods: GuardantINFORM is an RWE database that aggregates de-identified records from ctDNA testing (Guardant360), and aggregated payer claims from which race, ethnicity, age, cancer type and setting (academic/community) were extracted from 2021-2023 using the patients’ first test. Common cancers with biomarker/targeted therapy opportunities were included. Rates of ctDNA testing by race were assessed for non-Hispanic (NH) White, Black, and Asian/pacific islander (PI) and separately for Hispanic ethnicity. ctDNA testing rates were compared to recent CDC Wonder incidence using previously published methods (PMID: 36658153) and to tissue-based testing rates (PMID: 33106634) using t-test and Fisher’s Exact Test (significance: p &lt; 0.05). Results: There were no significant differences in sex, setting (academic/community), or age ( &gt; 65, 50-65 years) for each group compared to the overall cohort (p &gt; 0.2). ctDNA testing occurred most frequently in NH White (71.5%) followed by NH Black (12.1%), Hispanic (9.1%), and NH Asian/PI (4.7%); rates were comparable to published tissue-based rates (White: 76%, Black: 7%, Asian: 6%, N/A for Hispanic; p = 0.97). Controlling for incidence, ctDNA testing rates were significantly less-than-expected for 75% (6/8) of cancers in NH White, 37.5% (3/8) of cancers in both NH Black and Asian/PI, and 100% (8/8) of cancers in the Hispanic cohort (Table 1). Conclusions: In this large clinicogenomic database, Black and Asian patients had higher than expected rates of ctDNA testing, while Hispanic patients had lower rates. While the representation of Black patients is encouraging, ongoing efforts are needed to improve availability and frequency of testing, particularly for Hispanic patients. [Table: see text]
Comprehensive genomic profiling in solid tumors: Enhancing treatment selection and prognosis.
Authors: Minghui Wang, Yanhong Shang, Zhen-gang Yuan, Qing Hao, Zhizheng Wang, Dong You, Yang Liu, Fei Pang, Wei Rao, Yinying Wu, Leilei Lu
Journal: Journal of Clinical Oncology (2024)

e13631 Background: The emergence of novel therapeutic targets, targeted drugs, and immune checkpoint inhibitors has catalyzed a paradigm shift from single-target testing to comprehensive genomic profiling in the treatment of solid tumors. However, the tangible benefits of this shift in terms of treatment optimization and patient prognosis improvement have yet to be fully substantiated. Methods: In this retrospective study, we analyzed genomic data from 1032 patients with solid tumors to identify actionable genetic alterations, calculate tumor mutation burden (TMB), measure PD-L1 expression via immunohistochemistry, and assess microsatellite instability (MSI) status. These assessments were utilized to evaluate potential therapeutic targets and immunotherapy-related markers for each patient. Results: Between January 2018 and July 2020, 1032 patients underwent genomic profiling, received treatment, and were followed up. Of 1156 treatment episodes, 423 involved targeted monotherapy or combination therapy, 114 involved immune checkpoint inhibitor therapy, and 619 involved chemotherapy or anti-angiogenic therapy. Patients receiving matched therapy (n=491) demonstrated significantly higher partial response rates (31.7% vs 10.4%, P&lt;0.001) and longer median progression-free survival (PFS) (159 days vs 96 days, P&lt;0.001) compared to those receiving unmatched therapy (n=665). Based on the highest level of mutation carried, there were 475, 8, 153, 405, and 115 patients with grade I, II, III, IV, and V alterations, respectively. Among patients with grade I-II alterations, 65.63% received targeted therapy, resulting in significantly longer PFS compared to those undergoing immunotherapy or chemotherapy (200 days vs 109 days vs 94 days). Conversely, only 16.8% of patients with grade III-IV alterations received targeted treatment, with marginal PFS improvement (112 days vs 114 days vs 92 days). Notably, an increase in the number of actionable variants was associated with a significant decrease in PFS among patients receiving targeted therapy. However, the number of actionable variants did not significantly affect PFS in patients receiving chemotherapy or immunotherapy. Within our cohort, 44 patients with cancer of unknown primary (CUP) underwent molecular diagnostic evaluation, leading to the reclassification of 32 patients based on their molecular profiles. Notably, 24 patients harbored actionable genetic variants, 9 patients had a high TMB (≥10 Muts/Mb), and 3 patients exhibited high PD-L1 expression (TPS ≥50%). Conclusions: Comprehensive genomic profiling demonstrates clinical utility in guiding treatment decisions and improving prognosis for patients with advanced solid tumors. These findings advocate for the integration of next-generation sequencing into the therapeutic regimens for these malignancies.
Technology enhanced multidisciplinary cancer conferences in gynecologic oncology.
Authors: Amma Agyemang, Opuruiche Ibekwe, Carmelo Gaudioso, Kristopher Attwood, Sarah Werner, Chukwumere E. Nwogu, Peter Jonathan Frederick
Journal: Journal of Clinical Oncology (2024)

e13652 Background: Multidisciplinary Cancer Conferences (MCCs) improve quality of care and patient outcomes. The goals of our study were to investigate the impact of an information technology platform, Navify Tumor Board Solution (NTBS) on the (1) efficiency of preparing and conducting MCCs and (2) educational quality of the gynecologic oncology MCCs. Methods: We conducted a prospective, mixed methods study involving participants of the biweekly gynecologic oncology MCC at a comprehensive cancer center. NTBS was introduced to the gynecologic oncology MCC for the preparation and presentation of tumor board cases. Data were collected before and after the introduction of NTBS. We used a validated observational tool to assess the impact of the platform on the quality of case presentation and discussion, as well as decision making (as measured by a composite mean score). We also evaluated compliance of care plans with national guidelines, changes in care plans, and concordance of actual treatment received with MCC recommendations. The survey consisted of five qualitative and two demographic questions. Interviews lasting 10 minutes were recorded using Microsoft Teams. Analyses were conducted using SAS v9.4 (Cary, NC) and Dedoose v9.0.17, (Los Angeles, CA). Results: Pre- and Post-NTBS cohorts consisted of 49 and 60 patients, respectively. When comparing pre- versus post-NTBS cohorts, there was a difference in the mean composite score (50.57 vs 54.11; p=0.016). While there was no statistical difference between cohorts, patient care plans were changed in 13% of the cases and were 92% concordant with treatment received. Additionally, MCC recommendations were 100% compliant with national guidelines. Surveys of 13 MCC participants revealed that post-NTBS, the presentation format was more cohesive, concise, and structured. Common themes from gynecologic oncology fellows’ responses included improvement of case preparation time and simplified process while using NTBS. However, trainees reported limited use of NTBS features, difficulty incorporating graphical, and lack of a comprehensive list of NCCN guidelines. All respondents had educational goals for their participation and reported a high educational value of the MCC. These goals included knowledge acquisition of clinical diagnosis, management of standard of care versus complex situations, sentinel clinical trials underlying NCCN treatment guidelines, primary literature review, management of preparation time and flow of discussion, and preparation for oral boards. Conclusions: NTBS contributed positively to the gynecologic oncology MCC and clinical care for patients. Overall, NTBS had a positive impact on case formatting, preparation time and quality of trainee education. Comprehensive user training in the NTBS features could further enhance its utilization within the MCC.
Reliability of YouTube content targeting Arabic-speaking patients with breast cancer regarding post-mastectomy reconstruction.
Authors: Haifa Alotaibi, Marya Alsuhaibani, Abeer Alsulaimani, Eric Belzile, Nina Morena, Ari N. Meguerditchian
Journal: Journal of Clinical Oncology (2024)

e13626 Background: The dynamic and easily accessible nature of social media platforms, particularly YouTube (YT), has revolutionized the way breast cancer (BC) patients seek and process information. This study aims to evaluate the quality of YT videos addressing post-mastectomy reconstruction (PMR) that target Arabic-speaking women (ASW), a heavily digitally connected segment of society that may experience challenges in accessing mainstream medical information in Arabic Language (AL). Methods: YT was searched incognito, using the terms: "post mastectomy reconstruction" in (AL) in July 2023. Upload date, video length, number of views, poster identity and other relevant information were compiled. Video understandability and actionability were assessed by physician-reviewers (PR) with the Patient Education Materials Assessment Tool for audiovisual materials (PEMAT A/V), while the DISCERN tool appraised quality. Sponsorship presence, patient vs health care professional narratives, and intended audience were documented. Thematic analysis of most discussed topics was performed. Additionally, cluster analysis was employed to categorize the videos based on their quality. Results: Of 109 videos (mean length: 7 mins.), 67% had sponsorship (of which 45% corporate); 92% were information-based (vs experience-based); 82% targeted general population over BC patients; 93.6% were in AL, and 5.5% had translation. Mean PEMAT scores were 61.3% (understandability) and 20% (actionability). Mean DISCERN score was 2.6/5. Only 21% of videos featured women. Predominant themes were awareness (82.6%), appearance, and body image (68.8%). Less prevalent: sexuality and fertility (4.6%). The likelihood that the PR would recommend the video to a patient was low at 43.1%. Cluster analysis showed that high-quality videos had a higher understandability (74%), actionability (40.5%) and greater likelihood of being recommended by a PR (90.5%). Conclusions: YT content on PMR for ASW is easily understandable but lacks actionability and has moderate overall quality. Videos are highly sponsored and questionably representative of patients’ and women’s perspective. Therefore, Improving YT content, especially in AL, is crucial for supporting BC patients exploring PMR. The identified themes and insights into gender representation provide valuable guidance for content creators aiming to improve digital resources for this digitally connected demographic.
Treatment (Tx) inequities in patients (pts) with metastatic non-small cell lung cancer (mNSCLC) harboring actionable driver oncogenes (ADO) in the first-line (1L) setting.
Authors: Gregory A. Vidal, Thomas Stricker, Esprit Ma, Elaine Yu, Zhiyu Xia, Daniel Sheinson, Murat Sincan, Richard S. P. Huang, Victor T. G. Lin, Richard Michael Zuniga, Neha M. Jain, Richard Lewis Martin, Davey B. Daniel
Journal: Journal of Clinical Oncology (2024)

e13673 Background: Targeted treatments (TT) have been approved for pts with mNSCLC harboring ADO in the 1L setting in recent years. This study aimed to understand if subpopulations of mNSCLC pts harboring ADO do not receive equitable access to guideline recommended 1L TT in US clinical settings. Methods: This retrospective study used the Flatiron Health electronic health record-derived, US nationwide, de-identified database. Pts with de novo mNSCLC (stage IVA or IVB) diagnosed between 4/1/2018-6/30/2023 were included if they were ≥18 y, had ≥2 visits after advanced diagnosis (aDx) and ≥90 days follow-up, received any biomarker testing prior to 1L Tx initiation, and harbored ADO (ALK, BRAF, EGFR, MET, NTRK, RET and/or ROS1). Results: Overall 2165 pts met study criteria: mean age was 67 y, 65% female, 11% Medicare insured, 58% commercial insured, 76% treated in the community setting with 11% Medicare and 64% commercial insured (vs 12% and 41% treated in the academic setting with Medicare and commercial insurance), and 45% had smoking history; of non-missing data, 12% had lowest socioeconomic status (SES), 26% with highest SES, 95% had non-squamous NSCLC, and 83% had ECOG performance status ≤1. The median time from aDx to testing result among the 53% non-Latinx (nL) white, 8% nL Black, 7% Latinx, 13% nL Asian, 7% nL other and 13% missing race/ethnicity was 21, 23, 24, 21, 23 and 20 days, respectively; median time from aDx to TT initiation was 38, 38, 43, 36, 47 and 37 days, respectively. Pts significantly more likely to receive 1L TT were (Table): pts of color vs white pts; Medicare vs commercial insurance at community sites; without vs with smoking history. Pts less likely to receive 1L TT were (Table): community vs academic sites; Medicare vs commercial insurance at academic sites. Consistent findings were seen in the 1817 pts with established 1L ADO (ie. ALK, EGFR or ROS1) (Table). Conclusions: When pts with mNSCLC harboring ADO had biomarker testing before Tx initiation, contrary to prior observed racial inequity in NGS testing, there was no racial inequity in receiving 1L TT. However, inequity in receiving 1L TT was observed by insurance type, smoking history and site of care. Interventions are warranted to address these contributors to 1L TT inequities. [Table: see text]
A prospective study comparing AI-based clinical trial eligibility screening with traditional EMR-based screening.
Authors: Jiby Joseph-Thomas, Cristina Green, Abhishek Tibrewal, Frank Dupont, Jen Flach, Amelia Goldstein, Vivek Prabhakar Vaidya, Rajeev Kulkarni, Ravi Bharat Parikh
Journal: Journal of Clinical Oncology (2024)

e13636 Background: For clinical trials of novel oncology therapies, screening patients for eligibility relies on research staff manually abstracting complex eligibility criteria, which is time-consuming and subject to inconsistent interpretations. AI algorithms based on structured data fields may facilitate automated eligibility criteria extraction, assure more complete screening for the benefit of all patients, improve workflow for research staff, and assure no bias in who is reviewed. AI algorithms can support abstraction and imputation approaches to assure the greatest number of clinical records get reviewed. Prospective validation of such algorithms and approaches for sponsored registrational clinical trials is lacking from literature. Methods: We conducted a prospective controlled observational study of an AI model to prioritize potentially eligible patients, as part of a phase 3, randomized, multi-site therapeutic trial for Relapsed or Refractory Multiple Myeloma. In this observational study, we studied eligibility criteria focused on measurable disease and prior anti-myeloma treatments. The AI eligibility model extracted structured eligibility criteria to predict likelihood of meeting study eligibility. A single CRC (Clinical Research Coordinator) allocated 40 hours to screening in both control and intervention phases of the study. In the control phase, the CRC screened myeloma patients’ electronic health records in alphabetical order by last name. In the intervention phase, the CRC screened a different set of patients ranked by the eligibility likelihood model. Descriptive statistics were used to compare primary outcomes of efficiency (percentage of patients watch-listed) and speed (average time to screen a patient) between control and intervention phase. Patients marked as watch-listed meet eligibility criteria but need additional testing or disease progression before becoming eligible for the study. Results: The AI Eligibility method captured the same share (19% 1 ) of screened patients as the EMR method, with a 3.3x (12.5 versus 41 minutes) improvement per screened patient. Conclusions: The AI eligibility method allows research staff to screen three times more patients with similar outcomes as compared to routine CRC EMR screening. Future work can leverage AI to identify and predict patient progression, further enhancing screening efficiency. [Table: see text]
Investigating large language model (LLM) performance using in-context learning (ICL) for interpretation of ESMO and NCCN guidelines for lung cancer.
Authors: Sanna Iivanainen, Jarkko Lagus, Henri Viertolahti, Lauri Sippola, Jussi Koivunen
Journal: Journal of Clinical Oncology (2024)

e13637 Background: The recent development of advanced LLMs has been suggested to improve patient care across several areas such as clinical-decision support or helping to answer patients’ questions. Hallucinations have been identified as a blocker for the use of LLMs in routine clinical practice. ICL and Retrieval Augmented Generation (RAG) could improve the LLM performance and reduce hallucinations, consecutively making the use of LLMs possible in clinical practice. Methods: A method using ICL and RAG was developed on top of health AI platform (Gosta MedKit) to interpret the most recent ESMO (Dec 2022 for NSCLC, Mar 2021 for SCLC) and NCCN (Nov 2023 for NSCLC and SCLC) clinical guidelines for lung cancer. Guidelines (including tables and diagrams) were curated into a text format, and split and stored into a vector database. OpenAI’s GPT4 Turbo model version gpt-4-1106-preview (GPT4-T), having the knowledge cutoff in April 2023, was used in all implementations. 11 questions about SCLC and 13 questions about NSCLC treatment recommendations and definitions were developed to evaluate the performance of different settings: GPT4-T (existing knowledge of the model), ICL with maximum context (ICL-MC) length (128k tokens) and ICL with RAG (ICL-RAG) heuristically including only the most relevant parts from vector database. Question prompts were generated for different settings and guidelines (ESMO, NCCN and both combined) and two oncologists evaluated 216 different responses and their alignment with ESMO and NCCN guidelines. Results: For responses using ESMO guidelines having oncologists’ consensus, ICL-MC and ICL-RAG respectively provided accurate responses for 83.3% and 79.2% of questions vs. 62.5% for GPT4-T. For responses using NCCN guidelines having oncologist consensus, ICL-RAG provided accurate responses for 83.3%, GPT4-T for 75.0% and ICL-MC for 33.3% of questions. When more flexibility was allowed in results interpretation (alignment either with ESMO or NCCN), GPT4-T provided accurate response for 87.5% vs. 70.8% with ICL-RAG and 58.3% with ICL-MC. No consensus around hallucinations was reported for ICL approaches, whereas GPT4-T hallucinated the response for 4.2% of questions with ESMO guidelines. Conclusions: ICL seems to improve the LLM performance for stricter tasks such as providing responses according to specific guidelines and reducing hallucinations. ICL outperformed GPT4-T in case of ESMO guidelines. This highlights the importance of taking local and latest guidelines into account when LLMs are used across different health systems and regulatory environments. In line with earlier studies, longer context for ICL makes models forget crucial information, which can be mitigated with the use of RAG to improve ICL performance and reduce costs when using the models.
Implementing a natural language processing pipeline to expedite clinical chart review to assess germline genetic testing in veterans with breast cancer.
Authors: Vikas Patil, Richard Mcshinsky, Ahmad Sami Halwani, Deborah Morreall, Chunyang Li, Kelli M Rasmussen, Christina Yong, Zachary R. Burningham, Carolyn S. Menendez, Akiko Chiba, Haley Moss, Sarah Violet Colonna, Kerry G. Rowe, Michael J. Kelley
Journal: Journal of Clinical Oncology (2024)

e13640 Background: The Making Advances in Mammography and Medical Options for Veterans Act of 2022 mandated that the Department of Veterans Affairs (VA) perform a study of germline genetic testing in Veterans with breast cancer (BC). Assessing whether a Veteran with BC was offered germline genetic testing based on electronic health records requires time-consuming review of unstructured clinical notes by clinical experts who are also extremely busy providing care. To expedite this review process, we implemented a productionized natural language processing (NLP) pipeline to identify relevant concepts and highlight them within a custom-made chart review interface. Methods: Clinical subject-matter experts (SMEs) were convened to identify keywords suggestive of genetic testing. These were categorized into 3 sets: gene-related concepts, VA-specific services, and exams and tests. We then used entity recognition and entity linking to link these concepts to a knowledge base. These were enriched with entities similarly extracted from an open-source reference clinical text on germline genetic testing in BC. The resulting concepts were visualized as a word cloud and reviewed by the SMEs. We implemented a Power BI and Power Apps interface to display clinical notes from 200 randomly selected Veterans, with relevant concepts highlighted. Each Veteran’s notes were reviewed independently by 2 of the 5 SMEs, who assessed whether the Veteran was offered testing, referred to a genetic counseling service, or otherwise had evidence of testing. Disagreements were adjudicated by the whole team. Results: 11622 notes (mean 58/Veteran, 3028 characters/note) were reviewed. Table shows the most prevalent entities among genes, mutations, and exam names. Of the 5 reviewers, 3 annotated 80 patients each in a single session, lasting less than 50 minutes on average. Reviewers had an inter-annotator agreement of 85.5%. Mention of target concepts was associated with germline genetic testing with an accuracy of 80.4%, precision of 76.4%, recall of 99.1%, and F1 of 86.3%. Germline genetic testing rates are detailed in a separate submission. Conclusions: Modern informatics tools such as productionized NLP, the Microsoft Power Platform, and high-performance computing environments allow for rapid implementation of health information technology tools that can enable clinicians to gain critical insights quickly and efficiently from electronic health record data. [Table: see text]