tbuckley/cabot-search huggingface dataset:
Predicting fitness for chemotherapy in older adults with metastatic bladder cancer.
Authors: Brian Russell, Daniel E.C. Fein, Stephanie A. Berg, Shilpa Gupta, Jonathan E. Rosenberg, Toni K. CHOUEIRI, Joaquim Bellmunt
Journal: Journal of Clinical Oncology (2024)

e13761 Background: Guidelines advocate using comprehensive geriatric assessments (CGA) in older and frailer adults to optimize treatment recommendations, but it is unclear if genitourinary (GU) oncologists employ them. Through a national survey, this study aimed to evaluate techniques that providers use to predict fitness for chemotherapy in older adults with metastatic bladder cancer. Methods: This cross-sectional study surveyed US-based GU medical oncologists. Providers were included if they were identified as specializing in bladder cancer, had an accessible email address and worked at a National Cancer Institute-designated cancer center or an institution with an ACGME accredited hematology-oncology fellowship. Surveys were sent via email and collected in REDCap. The eleven-item survey consisted of multiple-choice questions and Likert scales evaluating use of geriatric assessments and other tools to predict chemotherapy tolerance. Results: There were 300 GU oncologists contacted, and 37.3% (n=112) completed the survey. Of respondents, 41 (36.6%) practiced in the Northeast with the remainder spread throughout the country. 51.8% (n=58) had practiced for ≥ 10 years, and 81.3% (n=91) had a panel composed primarily of patients ≥ 65-years-old. When making specific chemotherapeutic treatment recommendations, all providers consider a patients’ frailty. The majority (77.7%, n=87) do not use a specific age cut off, but rather, evaluate frailty on a case-by-case basis. Still, in predicting fitness for chemotherapy, 90.2% (n=101) almost never or never utilize a comprehensive geriatric assessment. Primary barriers centered on logistics including time constraints (71.4%, n=80), inadequate staffing (65.2%, n=73) and insufficient resources (33.9%, n=38). Beyond medical comorbidities, factors within a CGA that at least 50% of respondents consider important in predicting treatment tolerance include mobility (87.5%, n=98), social support (81.3%, n=91), cognitive function (73.2%, n=82) and nutritional status (71.4%, n=80) (Table). Conclusions: All surveyed providers believe frailty is important in predicting chemotherapy fitness, but the majority infrequently utilize CGAs in their evaluations. Highlighted barriers include time and resource constraints. While the CGA remains part of the standard of care in evaluating older adults for treatment, if a CGA cannot be performed, GU oncologists could consider focusing pre-treatment evaluation on the most important domains identified in this study: mobility, social support, cognitive function and nutritional status. [Table: see text]
Patient’s perception of cancer treatment outcomes in geriatric age groups: A retrospective analysis from a tertiary care centre in India.
Authors: Jatin Choudhary, Vanita Noronha, Nandini Sharrel Menon, Minit Jalan Shah, Anupa Pillai, Anita Kumar, Abhijith Rajaram Rao, Akash Pawar, Ratan Dhekale, Kumar Prabhash
Journal: Journal of Clinical Oncology (2024)

e13774 Background: Geriatric population forms a significant part of the total cancer patients in any given country and their expectations, their needs and challenges are different from younger patients. Patients may perceive their “chances” differently from what is expected with cancer treatment. In our analysis we aimed to look at such perceived intent of therapy in geriatric patients and factors that might influence these perceptions. Methods: We retrospectively looked at data of comprehensive geriatric assessment (CGA) which is maintained by the geriatric clinic at our institute. Patients are referred to our geriatric clinic after their joint clinic meetings and after their meetings with their primary oncologists. We identified 2962 patients who underwent CGA and collected data of age, gender, social support, marital status, education level, actual intent of therapy, patients’ perception of intent, their desire of disclosure of disease status. We also collected objective scores of GDS, GAD 7, TUG and ECOG performance status. Descriptive statistics were performed to find out the factors significantly affecting patient’s perception of intent of therapy. Results: The median age of our cohort was 68 years (Interquartile range- 64-72 years). Results are shown (Table). 70.6% wanted full disclosure of disease. Out of 1404 patients who were in palliative setting, 40.2 percent patient had misperception of treatment. Conclusions: Our data highlights the significant impact of patient’s general wellbeing both physical and mental on the perception of their treatment goals. It brings forth the need to address these issues along with cancer therapy in an already vulnerable geriatric population. Our data also highlights the desire to know about the disease status in majority which may empower terminal patients to spend their time and energy on things they value. A nearly 41% misperception in palliative patients is indicative of an urgent need for better communication practices to help patients be more aware of outcomes and thus may be able to put more quality in the limited time they have. [Table: see text]
Efficacy of alpelisib (ALP) + fulvestrant (FUL) with rational use of metformin (MET) in patients with HR+/HER2- advanced breast cancer (ABC).
Authors: Daria Filonenko, Rashida Orlova, Mark Gluzman, Almira Vakhitova, Alla Goryainova, Katerina Grechukhina, Irina Bykonya, Lyudmila Zhukova
Journal: Journal of Clinical Oncology (2024)

1049 Background: Hyperglycemia (HG) is an on-target effect of ALP, and prophylactic with metformin was shown to decrease the rate and severity of HG. Here we present efficacy and safety of ALP in a large cohort of patients with rational approach to metformin usage based on risk factors of HG. Methods: Patients (pts) with HR+/HER2- PIK3CAmut ABC were treated with ALP 300mg orally and FUL 500mg IM until disease progression or intolerance. The primary endpoints were progression free survival (PFS) and rate of grade 3/4 HG in different risk groups. Pts were stratified to risk groups of HG based on the algorithm (table 1) before starting ALP and received MET for prevention of HG. Results: A cohort comprised 139 pts, median lines of previous treatment in ABC was 3 (1-9): 88% received CDK4/6i, 60% - FUL, and 55% - chemotherapy (77) prior to ALP. The median PFS (mPFS) on ALP - was 7.0 (CI 95%: 5.0-8.9). The risk of HG was assessed in 138 pts: low (47), moderate (46), and high (42) risk, and 3 pts with type 2 diabetes mellitus. Any grade HG was reported in 62% pts, no cases HG G4 were detected. HG G3 was reported in 13% pts; 10% pts required ALP dose reduction to 250 mg due to HG G3; 3 pts (2%) discontinued ALP due to HG. The mPFS in our analysis was longer than in BYlieve trial in cohort C: 7.0 vs 5.6 mo. We suggest that prophylactic of HG allowed maintenance of full dose of ALP in most patients and thus increased the efficacy of ALP. Furthermore, in contrast to the METALLICA trial we distinguished a low risk group (33%) of pts who don’t need the prophylactic use of MET. Conclusions: Rational use of MET in pts with moderate and high risk of HG reduces the frequency and severity of HG, decreases the rate of ALP dose reductions and as a result may increase the efficacy of ALP. Pts with low risk of HG don’t need MET for HG prevention. [Table: see text]
Auto-machine learning for opportunistic thyroid nodule detection in lung cancer screening chest CT.
Authors: Sumir Patel, Veysel Kocaman, Mehmet Burak Sayici, Nikhil Patel
Journal: Journal of Clinical Oncology (2024)

e13639 Background: Automated Machine Learning (Auto-ML) in medical imaging is a process that allows non-experts to utilize machine learning techniques, opening the door for non-coder physician-driven exploitation of the technology. Auto-ML was applied for opportunistic detection of thyroid nodules in the context of low-dose lung cancer screening chest CT, facilitated by an innovative platform integration. By leveraging scans originally intended for lung cancer screening, suspicious appearing asymptomatic thyroid nodules can also be screened for where technically feasible. Methods: CT scans from the National Lung Screening Trial (NLST) dataset were utilized. This dataset contains low-dose chest CT examinations initially used for lung cancer screening. Annotation was carried out within a no-code web-based platform, Gesund.ai. A board-certified diagnostic radiologist annotated 100 CT examinations. A case was labeled "suspicious" if it presented a thyroid nodule of 1 cm or greater in diameter, while cases with no such nodules were deemed "not suspicious." The Auto-ML feature was then engaged within the software platform for parameter adjustments to refine the model without the need for manual coding. This phase was characterized by an iterative exploration of model performance, facilitated by the platform’s robust validation tools. Results: Upon training and validation, the Auto-ML model, cultivated from 100 annotated cases and assessed against a separate set of 130 cases, demonstrated a discernment accuracy of 0.51 in identifying "suspicious" thyroid nodules with a sensitivity and precision of 0.51 and 0.62 respectively. The AUC for this class stands at 0.69, indicating a moderate ability to distinguish between the presence and absence of thyroid nodules. This metric, while foundational, offers insight into the model's potential efficacy in real-world diagnostic scenarios, reflecting the initial capabilities of the platform's Auto-ML integration in enhancing diagnostic processes. Conclusions: The findings from this study highlight the potential of Auto-ML in revolutionizing opportunistic screening for thyroid nodules via lung cancer CT. The initial accuracy rate underscores the necessity for further refinement and validation of the model, but also demonstrates the promising potential of Auto-ML. Institutional Auto-ML would include extraction of appropriate studies through the no-code software platform integrated directly within the PACS framework. This process would ensure data immobility and foster a secure environment for dataset creation. Annotation would be carried out by multiple physician imagers within the same facility, enhancing the data’s accuracy and comprehensiveness specific for the institution’s patient population. This exploration highlights the strategic role of Auto-ML in advancing cancer patient care through innovative technological integration.
Association between social determinants of health and time to treatment for newly diagnosed multiple myeloma.
Authors: Hamlet Gasoyan, Faiz Anwer, Jeffrey D. Kovach, Nicholas J. Casacchia, Ming Wang, Michael B. Rothberg
Journal: Journal of Clinical Oncology (2024)

1583 Background: Delayed access to costly anti-myeloma agents due to insurance precertification process or out-of-pocket costs results in treatment initiation with less-costly corticosteroids in some patients with newly diagnosed multiple myeloma (NDMM). Less is known about the scope of the delay and the factors associated with it. Methods: This retrospective cohort study used Taussig Cancer Center’s Myeloma Patient Registry to identify adults with NDMM between January 1, 2017 and December 31, 2021. Electronic health records data captured (1) time from initial diagnosis to receipt of FDA-approved anti-myeloma agent including vs. excluding corticosteroids, (2) time to initial prescription fill for FDA-approved anti-myeloma drug excluding corticosteroids, and (3) association of race, sex, primary insurance type, area deprivation index (ADI), urbanicity, and time to initial fill for anti-myeloma drug excluding corticosteroids. The latter was examined via cumulative incidence at 30 days since initial diagnosis using the log-rank test and multivariable Cox regression model. Results: We identified 723 patients with a mean age at diagnosis of 67 years ±11; 55% were male, 77% White, 22% Black, and 1% other races, covered by private insurance (36%), traditional Medicare (28%), Medicare Advantage (25%), Medicaid (8.4%), and self-pay/other (1.8%); and median follow-up of 784 days (interquartile range [IQR], 415, 1313). Over a third of patients (37%) resided in an area in the most disadvantaged quartile of the ADI; 83% resided in a metropolitan area. Median time to receipt of anti-myeloma treatment was 10 days (IQR, 2, 24) including corticosteroids vs. 18 days (IQR, 8, 37) excluding corticosteroids. Median time to initial prescription fill for an anti-myeloma drug excluding corticosteroids was 31 days (IQR, 16, 75). The cumulative incidence of prescription fill for anti-myeloma drug excluding corticosteroids at 30 days was 40% (95% CI, 36%, 44%) in White individuals, 28% (95% CI, 21%, 36%) in Black patients, and 17% (95% CI, 0.42%, 64%) in other races (P=.009) [adjusted HR for Black patients (v. White), 0.63, 95% CI, 0.44, 0.90]; 31% (95% CI, 25%, 38%) in patients with traditional Medicare, 37% (95% CI, 30%, 44%) with Medicare Advantage, 44% (95% CI, 38%, 50%) with Private Insurance, 34% (95% CI, 23%, 48%) with Medicaid, and 31% (95% CI, 9.1%, 61%) self-paying/with other insurance (P=.07) [adjusted HR for private insurance (v. traditional Medicare), 1.41, 95% CI, 1.04, 1.93]; 40% (95% CI, 36%, 44%) treated in Taussig Cancer Center and 25% (95% CI, 17%, 34%) in regional hospitals (P=.003) [adjusted HR for regional hospitals, 0.59, 95% CI, 0.40, 0.89]. Conclusions: Disparities in time to prescription fill for anti-myeloma drugs exist based on socio-demographic factors. Future research should examine the role of precertification criteria and out-of-pocket costs in delays with recommended care.
Clinical decision making by ChatGPT vs medical oncologists: A retrospective concordance study.
Authors: Marine Rushanyan, Tigran Aghabekyan, Gevorg Tamamyan, Davit Zohrabyan, Liana Safaryan, Karen Bedirian, Karine Sargsyan, Gohar Mkrtchyan
Journal: Journal of Clinical Oncology (2024)

e13634 Background: This research explores the application of AI language models in clinical decision-making within the context of oncology, focusing on the concordance between AI-generated recommendations and actual treatment decisions made by physicians. Leveraging ChatGPT version 4.0, a large language model, we conducted a concordance study using retrospective patient cases from Yeolyan Hematology and Oncology Center. The hypothesis, centered on the alignment of AI chatbots with human decisions, spurred an in-depth exploration of concordance rates. Methods: A total of 228 adult solid malignancy cases from Yeolyan Hematology and Oncology Center (Jan 2020 - Sep 2021) were analyzed, focusing on primary cancers and data from multidisciplinary tumor board meetings. We scored concordance and analyzed the response times as well. Results: The study unveiled an overall 62.3% concordance rate, with varying rates among different cancer types: lung (50%), breast (50%), gastrointestinal (66%), and gynecological (81.8%) cancers. Despite promising results, challenges emerged, including variability in ChatGPT-4 responses and concerns about reliability and reproducibility. The study acknowledges the influence of prior assessments from other healthcare facilities, financial disparities impacting treatment decisions, and potential oversimplification of chemotherapy regimens. Conclusions: AI-based language models like ChatGPT present intriguing possibilities for healthcare, yet further research, controlled trials, and prospective studies are imperative to comprehensively understand and enhance their applicability in personalized patient care. This research sheds light on the complexities and considerations associated with integrating AI into oncological decision-making, offering valuable insights for future advancements in this evolving field.
Impact of a proactive patient assistance program for diverse ambulatory oncology patients: A retrospective cohort study.
Authors: Patricia P Falconer, Alexandra Elyse Phillip, Aja M Scott, Lisa Philipp
Journal: Journal of Clinical Oncology (2024)

e13513 Background: Significant differences persist in cancer incidence, survival, morbidity, and mortality among populations in the U.S. Individuals of lower socioeconomic status (SES) suffer disproportionately from cancer and other disease burdens compared to individuals with higher SES. Financial hardship is increasingly common, with many cancer survivors reporting difficulty paying medical bills, high financial distress, and delaying or forgoing care because of cost. Cancer patients reporting financial hardship are more likely to have psychosocial distress. Financial distress has been linked with several clinically relevant patient outcomes including quality of life, symptom burden, compliance, and survival. An NCI-designated comprehensive cancer center (CCC) with a diverse racial, ethnic, and linguistic patient population utilizes My Wellness Check (MWC) program to identify patients experiencing emotional, physical, practical, and social concerns. Among 4,117 MWC patient responders representing 43.1% of eligible patients during October 2019 - February 2022, 801 patients (19.5%) with 976 supportive or practical needs alerts were triggered to social workers where only 68.4% of the alerts were acted upon within a 72-hour window. For patients identified by MWC as having financial toxicity, Social Workers identified available resources to assist with patients’ out-of-pocket costs. Methods: In July 2022, the CCC launched a patient financial assistance program which included screening of all patients receiving systemic therapy administered at infusion centers. A third-party partner was contracted to provide patient advocates and technology augmenting the EHR. Patient Advocates identified eligible patients and enrolled them into financial assistance programs. Results: A retrospective analysis was performed for the period of May 2022 - December 2023, comparing patients receiving philanthropic medical financial assistance consisting of copay assistance and grants sponsored by drug manufacturers and diagnosis based non-profit foundations with infusion center patient population demographic data. The cohort participating in patient assistance programs included a higher percentage of female, Hispanic, age 27-64, commercially insured, and patients that reside in high and medium-high Social Vulnerability Index (SVI) counties. Conclusions: A proactive patient financial assistance program can help reduce financial distress for patients with cancer, especially those residing in high and medium high SVI counties. Leveraging the expertise of a third party helps mitigate financial toxicity in the short term and can help drive more equitable cancer outcomes.
The transformative role of digital health platforms: Bridging diversity gaps in cancer care in younger patient populations.
Authors: Yelena Lapidot, Avital Gaziel, Tzvia Bader
Journal: Journal of Clinical Oncology (2024)

e13503 Background: Digital health platforms are transforming cancer care by offering unprecedented access to treatment options, including clinical trials (CTs). In this study, we analyzed data from the Leal digital health platform to identify demographic and behavioral trends among patients that may have implications for quality care and access to advanced treatments. Methods: Leal is an AI-based platform that matches cancer patients to CTs based on a self-reported medical profile. The profile includes parameters essential for CT matching, including disease status, stage, biomarker/mutational status, treatment history, comorbidities and demographics. The analysis incorporated data from 34,913 Leal Health community members, spanning a range of cancer types and encompassing all disease stages. Results: The digital platform attracted a significantly younger user base, while also reflecting key demographic trends consistent with the broader real-world cancer patient population. Specifically, 45% of the users were under 60 years old, and 20% were under 50, leading to a mean age of 60 years. This is notably younger than the general cancer patient population's mean age at diagnosis of 66 years (p = 0.03). The platform engaged diverse demographics, particularly among younger age groups (p&lt;0.0001). Notably, one-third of users under 40 and over a quarter under 60 belonged to minority groups. African American at younger ages represented about 10% of all signups, bridging a significant disparity for this minority group, even more so within the space of CT search and recruitment where African Americans represent up to 5% of patients. Moreover, younger users exhibited significantly higher disease knowledge (p&lt;0.0001), enhancing their potential for precise matching with advanced treatments. Conclusions: Digital health platforms uniquely attract younger and more diverse demographics, reflecting a broader spectrum of patient needs and experiences while significantly bridging racial disparity in CTs representation. This shift has profound implications for healthcare knowledge and outcomes. Our findings highlight the importance of adopting digital platforms to address the unique needs of the younger patient population.