tbuckley/cabot-search huggingface dataset:
Oncology triage utilization among patients with gynecologic cancers.
Authors: Ayotomiwa Ojo, Isabelle Kildee, Reena Modi, Victoria Lynn Caulfield, Mary Frances Mulcahy, Olivia Foley, Emma Longley Barber
Journal: Journal of Clinical Oncology (2024)

e13551 Background: Emergency department (ED) utilization among patients with cancer is associated with significant burden including financial costs, resource allocation, and the patients’ experience of time spent seeking care. Moreover, studies have demonstrated that cancer patients are commonly seen in the ED for issues that do not require inpatient management and could be managed in outpatient settings. Oncology-specific urgent care centers have previously been demonstrated to decrease ED utilization and may be a potential remedy to the time toxicity experienced by patients receiving cancer care. This retrospective review aims to identify patient characteristics, resource utilization, and disposition of patients with gynecologic cancers who present to an advanced practice provider-driven, cancer-specific urgent care center (“oncology triage”) embedded in a tertiary academic center in an urban setting. Methods: We conducted a retrospective review of all gynecologic oncology patients who presented to oncology triage for any indication from May 16, 2022 to May 16, 2023.We describe the patient population utilizing oncology triage, the services provided, and time-based outcomes to assess efficiency, including time from presentation until being roomed, time to admission, and time to undergoing imaging. Results: 104 patients with gynecologic cancer were seen in oncology triage during the study period. Most patients had ovarian (43.2%) and endometrial (42.3%) cancers, and fewer had cervical cancer (10.6%). 60.6% were on their first or second line of systemic treatment. Insurance coverage included 17.3% Medicaid, 33.7% Medicare, 46.2% private, and 2.9% were uninsured. Pain (40.4%) and nausea (31.7%) were the most common chief complaints. 25.0% and 23.1% of patients had an X-ray or CT scan ordered, respectively. Intravenous fluids (69.2%) and supportive medications (e.g., antiemetics, opiates; 51.9%) were the most frequent therapeutics administered. 19% were evaluated and reassured by provider without needing additional treatment. 35.5% of all patients seen were admitted. On average, patients were roomed and seen by the care team in 9 minutes. The average time from presentation to oncology triage to hospital admission was 5 hours and 1 minute. Conclusions: In a single-center study of oncology triage utilization among patients with a mix of gynecologic cancers, we found that an oncology-specific urgent care center evaluates and manages common acute cancer-related complaints in a timely fashion. Oncology triage offers a high-acuity patient population an alternative to the ED that is much more time efficient when compared to historic ED wait times. Our findings support oncology-specific triage as a valid model of care for a subset of common acute gynecologic cancer-related problems and highlight its potential to improve patients’ experience of their cancer care.
Evaluating generalizability of landmark randomized controlled trials in common metastatic cancers using machine learning-based emulated trials.
Authors: Xavier Orcutt, Qi Long, Ronac Mamtani, Ravi Bharat Parikh
Journal: Journal of Clinical Oncology (2024)

e13618 Background: Randomized controlled trial (RCT) results often lack generalizability to real-world oncology patients (pts). We present TrialTranslator, a machine learning (ML) framework to evaluate the generalizability of Phase III oncology RCTs by emulating trials across prognostic risk groups. Methods: TrialTranslator was applied to 11 landmark RCTs encompassing the most prevalent advanced cancers – non-small cell lung (aNSCLC), breast (mBC), prostate (mPC), and colorectal (mCRC). Flatiron Health’s EHR-derived de-identified database was used for model development and trial emulation. In Stage I, ML models (gradient boosted [GBM], random forest, support vector machine and penalized Cox) were trained to predict survival from time of metastatic diagnosis. Models used 130 longitudinal demographic, cancer-related, lab, and biomarker features. In Stage II, trial emulation, pts meeting RCT cancer and biomarker eligibility criteria were stratified into low, medium, and high-risk groups based on the top-performing ML model. We used inverse probability of treatment weighted survival analysis for each risk group to compare the median overall survival (mOS) or median progression free survival (mPFS) between experimental and control arms. Results: GBM models achieved the highest time-dependent area under the receiver operating curve (AUC) in the test set in all cancer types. For example, the 1-year AUC for the aNSCLC GBM vs the Lung Cancer Prognostic Index (validated Cox model) was 0.783 vs 0.689. ML-predicted low-risk pts typically had similar mOS or mPFS as RCT populations (Table). In emulated trials for low-risk pts, real-world survival benefit – the absolute difference in mOS or mPFS between experimental and control arms – was similar to RCT results. In contrast, for high-risk pts, survival benefits were not statistically or clinically significant. For example, in the FLAURA trial, RCT mPFS for experimental arm (osimertinib) vs control (gefitinib or erlotinib) was 18.9 mos vs 10.2 months; mPFS was 17.1 vs 10.3 months for low-risk pts and 5.1 vs 4.2 months for high-risk pts. Conclusions: ML frameworks like TrialTranslator may facilitate individual patient-level estimation of real-world treatment benefits of novel oncology agents. TrialTranslator is accessible at trialtranslator.com. [Table: see text]
“What you're hearing from all of us is simplify the language”: A focus group study on biomarker testing in oncology.
Authors: Anna Jo Bodurtha Smith, Ian Lee Ang, Rashmi Acharya, Tracy Moore, Sarah DeFeo, Caroline O'Brien, Emily Meichun Ko
Journal: Journal of Clinical Oncology (2024)

e13540 Background: In oncology, somatic biomarker testing is increasingly driving treatment decision-making. Exploration of patients’ understanding of, and perspectives on, biomarker testing has been limited, yet these are needed to improve health education and communication about biomarkers. Our objectives were to explore the perspectives of patients with gynecologic cancers on biomarker and genetic testing and to identify factors that influenced their decision-making on testing. Methods: Patients with gynecologic cancer enrolled in the University of Pennsylvania’s Biobank were invited to participate. Of the 82 patients contacted, 7 participated in 3 focus groups in fall 2023. Participants reviewed educational materials (pamphlet, slide set) about biomarker testing based on the Cancer Precision Medicine Commons’ patient education materials, explored understanding and perspectives around biomarker testing, and then discussed barriers and facilitators to testing. Thematic analyses by two researchers were performed using verbatim transcripts. Results: All patients strongly agreed that both genetic and biomarker testing were important after a gynecologic cancer diagnosis and impacted their treatment options. However, patients did not understand what a biomarker was and expressed confusion over terms and testing methods, labeling unknown terms as “jargon.” 85% of participants had received germline genetic testing, and 85% received biomarker testing, with the remaining participants uncertain about testing status. Participants utilized a variety of resources such as the Internet, peer forums, articles on the patient portal, discussions with clinicians, and scientific papers to understand their cancer diagnosis and the role of genetic and biomarker testing. They mentioned fear (i.e., seeing low survival rates for ovarian cancer online as a “death sentence”) and financial circumstances as key barriers to accessing genetic or biomarker testing. Patients desired simple tailored written materials that were relevant from diagnosis to across the disease course. Conclusions: Patients understand the importance of biomarker testing in oncology but reported difficulties understanding existing resources. Current online resources had jargon and induced fear in some participants. Work to develop patient-centered resources on biomarker testing is needed.
Comparing literature-based cell-free miRNA panels to whole genome miRNA screening for cancer detection.
Authors: Tsung-Ting Hsieh, Ko-Han Lee, Jason Chia-Hsun Hsieh, Yu-Chuan Chang
Journal: Journal of Clinical Oncology (2024)

e13587 Background: Cell-free miRNAs (cf-miRNAs) found in bodily fluids like plasma or serum are adept at detecting cancer. When constructing a cancer detection model with a reasonably sized training sample, it seems logical that employing whole genome screening could enhance prediction performance and unveil novel miRNAs. However, screening all cf-miRNAs could be costly and its potential improvement compared to a curated panel remains uncertain. To evaluate the trade-off between accuracy and cost-effectiveness, our study aims to compare the performance of two approaches: a literature-based cf-miRNA panel and a whole genome screening. We utilized real-world datasets to assess the effectiveness of these two methods in cancer detection. Methods: To curate a cf-miRNA panel, we conducted a comprehensive review of published miRNA-related databases, identifying 27 databases, including dbDEMC 2.0, miRCancer, miRStress, HMDD, and others. Of these, 11 databases were both available and downloadable, yielding 196,839 records (database entries). Focusing on cf-miRNAs, we filtered the records to only include those clearly identified as originating from plasma or serum, resulting in 13,595 cf-miRNA records. Next, to ensure robustness, we employed two strategies: requiring multiple records for a given miRNA and confirming its mention in multiple databases to finalize the cf-miRNA panel. For comparison of prediction performance, we obtained a large-scale dataset from the Gene Expression Omnibus, specifically the GPL21263 platform, for modeling experiments. This dataset comprised 8,174 subjects with 2,565 miRNA targets across seven cancer types derived from various cf-miRNA-based cancer studies. We then trained machine learning models on both the curated panel and the full miRNAs from this dataset to facilitate a comparative analysis. Results: Out of the 13,595 cf-miRNA records, 499 miRNAs had over 4 records, and 612 miRNAs were mentioned in multiple databases. There was an overlap of 339 miRNAs, forming our final cf-miRNA panel. The model accuracy using this panel to distinguish between seven cancer types was 67.62%. In contrast, directly employing machine-learning to model whole genome screening resulted in an accuracy of 93.00%, demonstrating a significant improvement compared to the panel-based results. Conclusions: Our study curated a cost-effective cf-miRNA panel of 395 miRNAs, demonstrating the capability to distinguish multiple cancers. However, while the curated panel exhibited reasonable efficacy in cancer detection, the whole genome screening model displayed superior discriminatory power. Hence, for optimizing cancer detection methods, especially in large-scale datasets, employing whole genome screening appears to be a more promising strategy.
Perspectives on clinical adoption barriers to blood-based multi-cancer early detection tests across stakeholders.
Authors: Monica M Schroll, Elissa Quinn, Daryl Pritchard, Allina Chang, Kristen Garner Amanti, Omar Perez, Arushi Agarwal, Gary George Gustavsen
Journal: Journal of Clinical Oncology (2024)

e13574 Background: Current United States Preventive Services Task Force (USPSTF) recommendations include routine screening for breast, cervical, colorectal, and lung cancer; however, two out of every three cancer cases occur in other indications leading to diagnosis in advanced stages of the disease and higher likelihood of mortality. Current recommended cancer screening and early detection tests also face several hurdles hindering equitable access in a real-world setting. Blood-based MCED tests can impact cancer screening and early detection by monitoring for multiple different cancer types at once via a blood draw that can detect cancers in multiple indications, including indications where screening is not performed routinely today. Given their nascency, the role of blood-based MCED tests in clinical practice is currently undefined. To help facilitate the appropriate integration of MCED tests into patient care, it is important to understand the perspectives of stakeholders on the anticipated challenges to implementation. Methods: We conducted three separate double-blinded surveys in July 2023 of representative cohorts of U.S. Healthcare providers (HCPs) (n=238) that are or act as primary care providers (general practitioners and obstetrics and gynecologists (OB-GYNs)), payers (n=40), and patients (n=116), to evaluate the current cancer screening and early detection landscape and gather perspectives on barriers to implementation of new cancer screening technologies, including blood-based MCED tests. Results: Of the surveyed HCPs and payers, &gt;90% and 100% respectively reported to be aware of MCED tests. However, &lt;10% of HCPs reported having ordered an MCED test and 80% of payers had not yet evaluated an MCED test for coverage under their plan at the time of the survey. The survey results indicated that there is broad agreement on the value proposition that blood-based MCED tests would improve patient outcomes with earlier intervention. Despite this recognition, the survey highlighted that there is limited use today due to the perceived lack of clinical accuracy and utility data, high out-of-pocket patient costs, and lack of payer coverage. Conclusions: To overcome the hurdles for future widespread adoption of blood-based MCED tests, increased investment in data generation, education, and implementation of logistical support for HCPs will be critical. Initiatives include collaborations between stakeholders to generate near-term clinical utility data, equipping HCPs with the ability to educate patients on test accuracy, and seamlessly integrating the testing process into the patient visit workflow. The findings of this study can help build understanding of the anticipated barriers and inform strategies to alleviate them and build a foundation for clinical adoption of blood-based MCED tests.
A web-based, LLM-powered AI symptom summarization tool (ASST) for monitoring of breast cancer treatment toxicity.
Authors: Jean-Emmanuel Bibault, David JH Wu
Journal: Journal of Clinical Oncology (2024)

e13622 Background: Traditional methods for capturing patient-reported outcomes (PROs) are often time-consuming and may lead to underreporting of symptoms by physicians. With advancements in artificial intelligence (AI), particularly large language models (LLMs) like GPT-4, there is potential to revolutionize this aspect of patient care. Methods: This study developed and tested a GPT-4-based web application for monitoring cancer treatment toxicity in breast cancer patients. The application utilized 35 items from the PRO-CTCAE scale to create an interactive form for patients to report treatment-related symptoms. Upon form completion, a natural language summary of the patient’s responses was generated using the GPT-4 API for physician review. Nine radiation oncologists (5 attendings, 4 residents) evaluated the summaries of virtual patients using the adapted Physician Documentation Quality Index (PDQI-9). PDQI-9 consists of 9 items scored using a 5-point Likert scale (1 -not at all- to 5. -extremely-). IRB approval was not required because this study did not use real patient data, in accordance with the 2018 Revised Common Rule Requirements of the NIH. All data used were researcher-generated and non-identifiable. Results: The mean time for textual summary generation was 7 (5.7-9.2) seconds. The AI-Symptom Summarization Tool (ASST) mean scores were 4.25 for accuracy and 4 for thoroughness. Usefulness, organization, comprehensibility, concision, synthesis quality and consistency were all rated between 3 and 4. Overall score was 42/50. No hallucinations (false information made up by the LLM) were found. Conclusions: The GPT-4-based application for cancer treatment toxicity monitoring demonstrates significant promise in enhancing the quality and efficiency of patient care. By automating the documentation of patient-reported symptoms, this tool could allow physicians to focus more time on patient interaction and individualized care. As AI technologies continue to evolve, their integration into clinical practice must be approached with caution, emphasizing augmentation over replacement and the importance of verification to maintain trust in these new tools.
Expanding diversity in colorectal cancer studies with a focus on Hispanic and Latino participant enrollment.
Authors: Syed K Lateef, Kanza Chaudhary, Karolina Kutnik, Yontao Lu, Chuanbo Xu, Lilian C. Lee, Lance Baldo, Aasma Shaukat, Theodore R. Levin
Journal: Journal of Clinical Oncology (2024)

e13532 Background: Colorectal cancer (CRC) is a leading cause of cancer mortality among Hispanic and Latino groups (Hispanic/Latinx). Despite making up 18.9% of the U.S. population, Hispanic/Latinx representation is only 2.3%-3.9% of subjects in oncology trials, highlighting the need for diversity in CRC research. Freenome is developing a blood test (MyTectCRC) intended to provide a convenient and accessible option for CRC screening. Its clinical validation will be supported by PREEMPT CRC, a prospective multi-center observational study. To ensure a diverse enrollment, Freenome partnered with several sites, including IHS, in communities with a diverse population. This analysis explores strategies used at IHS Health to effectively enroll minority groups. Methods: Initiated in 2020, PREEMPT CRC study (NCT04369053) enrolled participants aged 45-85 at average CRC risk. Eligible and consenting participants underwent a blood draw and a screening colonoscopy (CS), with the blood sample analyzed by Freenome using the investigational blood test. IHS Health, a research institute in Kissimmee, Florida, utilized a variety of methods to maximize the enrollment of diverse populations reflective of its local communities, such as partnering with local organizations to establish community outreach and employing bilingual staff to translate, educate, answer study questions, with cultural sensitivity. Participants were also able to have blood drawn and undergo CS on the same day due to its proximity to a gastroenterology office. Results: The site of IHS Health enrolled 427 participants in the PREEMPT CRC study, including substantial representation of Hispanic/Latinx (78.7%) which exceeded their proportional representation in the U.S. population. The self-reported race of enrolled participants was predominantly White (71.2%), with a significant proportion of Black or African American (24.1%). The age distribution showed higher enrollment for the 65-74 age group, emphasizing inclusivity for older populations characteristic of the catchment area. Participants exhibited healthy lifestyle behavior with the majority of the population abstaining from tobacco (88.8%), alcohol (84.1%) or drug use (99.5%). Participants were very receptive to the blood test/CS with all undergoing blood draw and 96% CS adherence. Conclusions: The results underscore the success of IHS Health's recruitment methods in addressing health disparity by emphasizing the importance of inclusivity in clinical trials, leading to high enrollment of Hispanic/Latinx, reflective of local demographics. Success of PREEMPT CRC study highlights the need for careful site selection, robust recruitment strategies, and practical accessibility for patients to ensure a comprehensive understanding of medical interventions across diverse populations. Clinical trial information: NCT04369053 .
Contemporary management of patients with advanced melanoma: Utility of an online interactive decision support tool to assess concordance of healthcare professional treatment patterns with expert recommendations.
Authors: Marie N. Becker, Kristen M. Rosenthal, Allison Betof Warner, Adil Daud, Evan J. Lipson, Ahmad A. Tarhini, Hussein A. Tawbi
Journal: Journal of Clinical Oncology (2024)

e13548 Background: The treatment landscape for patients with melanoma continues to evolve with approvals of novel therapeutic agents for advanced disease and new indications for therapy in earlier-stage disease. Using an online decision support tool, we compared practice patterns and treatment recommendations between community healthcare professionals (HCPs) and key opinion leaders (KOLs). Methods: The online decision support tool was developed with input from 5 KOLs who provided treatment recommendations for 151 hypothetical patients. Case scenarios covered a spectrum of stages (III, IV), central nervous system (CNS) metastases, BRAF mutation status, and treatment histories. HCPs entered specific patient criteria into the tool to define a case and their intended therapy choice and the tool then showed 5 KOL recommendations for that patient case. An analysis of KOL recommendations and HCP-selected therapy was performed. Results: Between August 2023 and January 2024, 156 patient cases were entered by 100 HCPs. HCP and KOL recommendations were concordant in 51% (80/156) of cases. The highest level of discord among HCPs and KOLs was 72% for patients with completely resected stage IIIA melanoma and no positive lymph nodes having a metastatic focus ≥1mm (n = 25) (Table). A lower level of discordance (31%, n = 35) was observed in therapy selection for patients with stage IIIA melanoma and ≥1 positive lymph node. For first-line therapy in patients with unresectable stage III or IV BRAF wild-type melanoma (n = 16), discordance was 50% between HCPs and KOLs. The level of discordance was higher for first-line therapy for patients with BRAF-mutant melanoma (68%, n = 19). For the management of patients with CNS metastases, 40% of HCP therapy selections were not aligned with KOLs (n = 40). More than one half (56%) of HCPs noted that the tool confirmed their treatment plan, and 21% intend to change practice to align with KOLs. Conclusions: This analysis identified areas of discordance in clinical practice between HCPs and KOLs for advanced melanoma that can guide further education and enhance communication of evolving evidence to HCPs. Our data suggest that use of the decision tool can confirm best practices with KOL recommendations and improve alignment of treatment choices with KOLs. [Table: see text]