bluuebunny/crossref_metadata_2025_split huggingface dataset:
Treatment patterns among HCPs and concordance with expert recommendations in HER2-negative early breast cancer: Analysis of an interactive online treatment decision support tool.
Authors: Brandt Kelly G , Becker Marie N. , Gupta Tanya , Kalinsky Kevin , Spring Laura , Traina Tiffany A. , Yardley Denise A. , Rosenthal Kristen M. 
Published: May 2024

e13601 Background: Management of HER2- EBC continues to evolve with expansion of targeted therapies, including abemaciclib, olaparib, and pembrolizumab, in the adjuvant setting for eligible pts. We sought to identify contemporary practice patterns among HCPs for HER2- EBC and compare them with those of experts using an online interactive decision support tool. Methods: An online decision support tool was developed with input from 5 BC experts to provide tx recommendations for 19 unique pt case scenarios based on considerations including subtype, tx history, and recurrence risk. HCPs entered specific patient criteria along with their intended management for that case, and then were shown 5 expert recommendations. HCPs were asked if the recommendations impacted their intended approach. An analysis of expert recommendations and HCP-selected tx was performed. Results: Between Oct 2023 and Jan 2024, 83 HCPs entered 105 cases. Of the 97 cases where HCPs noted a definitive tx plan, recommendations were discordant from experts in 29%. The most commonly queried cases, expert recommendations, and discordance between HCPs and experts are outlined in the table. Discord with experts was similar among HCP tx plans for cases of TNBC (33%) and HR+/HER2- EBC (27%). HCPs predominantly sought guidance for pts with HR+/HER2- EBC, no g BRCAm, and high-risk disease per monarchE trial criteria (79%), with 22% discordance from expert recommendations. Regardless of BC subtype, the highest level of discord between HCPs and experts (67%) was observed in cases where pts had received neoadjuvant tx and had no residual disease following surgery, although this represents a small subset (n = 12). When asked if the expert recommendations changed their tx choice, 7 of 27 (26%) HCPs who responded indicated that expert recommendations changed their intended tx, whereas 15% noted that there were barriers to implementing those recommendations. Conclusions: These data suggest ongoing challenges with incorporating the newest targeted therapies for high-risk EBC management. Continued education and development of resources for HCPs, including online decision support tools, may be increasingly important as the treatment of EBC continues to evolve. [Table: see text]
Prediction of therapeutic response and cancer outcomes in solid tumours via in silico clinical trials.
Authors: Asghar Uzma Saddia , Griffiths Matthew , Kubeyev Amanzhol , Laurie Jordan , Giorni Andrea , Zillmann da Silva Luiz A , Sivasubramaniam Prabu , Foster Matthew , Biankin Andrew V. 
Published: May 2024

e13603 Background: Oncology therapeutic development has high failure rates with substantial costs. Advances in technology, including molecular characterisations of cancer and computational power, provide the opportunity to better model therapeutic response for existing and emerging therapies. Methods: Concr adapted Bayesian statistical principles used by astrophysicists to effectively integrate multi-dimensional data (genomic & transcriptomic) for accurate modelling of cancer biology. Our proprietary computational framework was applied to predict clinical response and survival through construction of digital twins and in silico simulations of clinical trials. Predicted log odds ratios (LOR) were generated for drug response and compared to published data from 8 suitable historical clinical trials. Three distinct models underpin this framework: Drug Efficacy, Treatment Response & Overall Survival (OS). Three publicly available datasets were used for training: Cancer Therapeutic Response Portal ( in vitro dose-response data), Cancer Cell Line Encyclopaedia and TCGA. Results: For all 8 clinical studies, the digital twin model accurately simulated both trial arms, compared drug efficacy across arms and predicted which treatment was most active. Unblinded evaluation: Our model accurately predicted gemcitabine would have greater clinical benefit than 5-FU (LOR -0.10, P <0.0001) in metastatic pancreatic cancer. In advanced breast cancer, the model predicted docetaxel had a higher response rate than doxorubicin, and carboplatin response rates were similar to docetaxel in BRCA wild-type patients. In platinum-sensitive recurrent ovarian cancer, the model predicted cisplatin-based combination had a higher response rate than paclitaxel. Blinded evaluation: Using data for paclitaxel, the model correctly predicted that nab-paclitaxel+gemcitabine response rates were higher than gemcitabine in metastatic pancreatic cancer (predicted LOR -0.090, p = <0.001). In all 3 early breast cancer studies, predictions mirrored clinical trial results, adjuvant ECF > CMF, adjuvant TC > AC and adjuvant tamoxifen was superior to capecitabine. The underlying pan-tumour Overall Survival model used a Random Survival Forest approach, trained on 23 cancers with predictive accuracy of 0.78 AUC ROC and C-index 0.71. To enable cohort enrichment for drug response, the model segmented patient cohorts into treatment responder and non-responder. Using this approach, the log odds increased for drug response rate for 16 of the 19 different cancers and 14 of the different 17 drugs. Conclusions: Our digital twin model represents transformative technology for clinical trial simulation with strong potential for accelerating and de-risking clinical drug development, and utility for synthetic control arms.
OncoGPT: Enhancing oncological decision-making with ASCO guideline–trained AI.
Authors: El-Assal Ramy , Nazha Aziz 
Published: May 2024

e13607 Background: General-purpose language models like ChatGPT, while proficient in a wide array of topics, often lack the specialized knowledge and precision required for specific fields such as oncology. The American Society of Clinical Oncology (ASCO) guidelines offer a comprehensive resource for oncology professionals, yet sifting through extensive documents for specific information can be daunting. To bridge this gap, we introduce OncoGPT, a specialized GPT model, tailored to provide precise and guideline-adherent oncological advice. Methods: We leveraged GPT-4's advanced language understanding capabilities as the foundation for OncoGPT. This customization involved an extensive training regimen using the complete text of the ASCO clinical guidelines. The training process was meticulously designed to ensure that OncoGPT not only assimilates the detailed information within these guidelines but also understands the context and intricacies of oncological care. Additionally, we created disease-specific versions of OncoGPT, each fine-tuned with guideline subsets pertinent to particular oncological conditions. Further, the training was augmented with specific prompts to foster kind, sincere, and respectful interactions, especially for patient-oriented responses. The model was designed to discern whether the user is a healthcare professional or a patient and adjust the complexity of its responses accordingly. For patients, additional emphasis was placed on delivering information in a kind, responsible, and empathetic manner. Results: OncoGPT's performance was rigorously evaluated for accuracy and reasoning against real-world clinical scenarios. The responses generated by the model were meticulously reviewed by an experienced oncologist to confirm adherence to ASCO guidelines and clinical relevance. This assessment revealed a high level of precision and contextual understanding in OncoGPT's outputs, demonstrating its potential as a valuable tool for oncology practitioners. Importantly, it showed a remarkable ability to tailor its communication style based on the user's background, providing simplified explanations for patients and more detailed responses for professionals. An example of a specific OncoGPT: Breast Cancer OncoGPT: https://chat.openai.com/g/g-VcuECT3jg-oncogpt-breast-cancer Conclusions: OncoGPT represents a significant advancement in the application of AI for medical decision-making. By integrating the depth of ASCO guidelines into a responsive and accurate AI model, OncoGPT offers oncology professionals a powerful tool for accessible, guideline-compliant advice. This development marks a crucial step towards integrating AI more profoundly into specialized medical fields, enhancing both the efficiency and accuracy of clinical decision-making.
Retrieval-augmented large language models for clinical trial screening.
Authors: Tan Ryan , Ho Si Xian , Oo Shiyun Vivianna Fequira , Chua Shi Ling , Zaw Ma Wai Wai , Tan Daniel Shao-Weng 
Published: May 2024

e13611 Background: Clinical trial screening is currently manual and laborious. We tested several large language models enhanced with retrieval-augmented generation (RAG-LLM) to assess their performance on this task. Methods: We extracted eligibility criteria of 184 oncology trials with FDA approval notifications between 8 January 2020 and 18 January 2024, as well as information on cancer staging and performance status scoring for the RAG-LLM vector database. A medical oncologist and 2 senior clinical trial coordinators developed a test set of 975 synthetic patient profiles which included primary site, stage, prior therapy, tumor mutations and one additional clinical feature. Each profile was paired with one of the 184 trials and annotated for ground-truth eligibility and the reason for it. This process was repeated for another validation set of 240 longer and more challenging profiles paired with 8 ongoing trials. We developed RAG-LLMs with 4 leading LLMs (Zephyr-7B, Med42, GPT 3.5, GPT4) and evaluated their accuracy in determining trial eligibility as well as retrieval-augmented generation assessment (RAGAs) metrics. A response was deemed accurate only if it correctly assigned both trial eligibility and the reason for assignment. Results: GPT4 performed best and achieved an accuracy of 95.18% and 80.00% on the test and validation set respectively with a mean inference time of 10.95 seconds. It also demonstrated the highest answer relevancy, context relevancy and faithfulness (Table). Conclusions: Our results demonstrate potential for RAG-LLMs to assist with trial screening at scale. Further evaluation in real-world cohorts utilizing electronic records and full protocol data with tracking of impact on trial enrolment can be explored within secure firewalls. [Table: see text]
Clustering analysis for predicting racial disparities in genitourinary cancer–related financial burden.
Authors: Zhang Yanjia , Khosla Atulya Aman , Chhabra Sufal , Batra Nitya , Ganiyani Mohammad Arfat , Goyal Anmol , Bellur Shreyas S , Singh Rohit , Jatwani Karan , Ramamoorthy Venkataraghavan , Rubens Muni , Saxena Anshul , Garje Rohan 
Published: May 2024

e13612 Background: Healthcare costs in the United States (US) are significantly impacted by genitourinary (GU) cancers. Prostate cancer treatment alone cost the US more than $22 billion in 2020. We sought to utilize machine learning (ML)-based classification to investigate differences in the total cost of healthcare expenditure among patients with GU cancer in the US. Methods: The study included eligible adult GU cases from the 2019–2021 Medical Expenditure Panel Survey. Renal cell, prostate, urothelial, testicular, and penile cancers were identified using ICD-10 CM codes. Patients were clustered using demographic information such as age, race, sex, marital status, highest degree, insurance status, perceived health status, and perceived mental health status. The analysis accounted for the complex survey design and sampling weights. The cluster groups were taken into account when conducting descriptive and regression analyses. GU cancer cases were divided into low-, medium-, and high-risk healthcare expenditure clusters. Results: The study included a representative sample of 9,061,181 cases of GU cancer in the US between 2019 and 2021. The average age of the sample was 71.6 years (95% CI: 70.7, 72.5). There were 7.9% Hispanic (Hisp), 74.8% non-Hispanic White (NHW), 13.8% non-Hispanic Black (NHB), and 3.5% individuals from Asian or other races. Overall, the mean total healthcare expenditures within low-, med-, and high-risk clusters were $17974 (13816, 22133), $20496 (13100, 27893), and $30512 (21389, 39635), respectively. Regression analysis showed that, after adjusting for sampling weight, cluster, gender, family income, age, insurance status, and survey year, the mean total expenditure was higher by $11209.70 and $13100.81 in med- and high-risk clusters when compared to the low-risk cluster (ref.). Within each race or ethnicity, the proportion of Hisp or NHB individuals was higher in med- and high-risk clusters. Med- and high-risk clusters were more likely to contain people with cognitive impairments, poor health status, and financial difficulties in their families. Conclusions: We used an unsupervised clustering algorithm that can distinguish between GU cases with high and low expenditure based solely on sociodemographic variables. We demonstrate that our findings were consistent for all three data cohorts. This approach can predict the disparity in utilization in any insured population. [Table: see text]
Evaluating generalizability of landmark randomized controlled trials in common metastatic cancers using machine learning-based emulated trials.
Authors: Orcutt Xavier , Long Qi , Mamtani Ronac , Parikh Ravi Bharat 
Published: May 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]
AI-generated synthetic clinical-genomic data for precision oncology research: Validation using a case study on lung adenocarcinoma.
Authors: Theodorou Brandon , Das Amar , Eagle Craig , Sun Jimeng , Hardin Aaron , Zhang Nicole , Yay Donderici Elifnur 
Published: May 2024

e13627 Background: The analysis of genomic variants is crucial in precision oncology research, offering insights into cancer risks and progression, especially in diverse types such as lung adenocarcinoma (LUAD). However, such research often grapples with balancing patient privacy with the need for comprehensive, high-quality genomic datasets. Our project addresses this by creating synthetic clinical-genomic data, which maintains patient confidentiality and provides a rich resource for genomic cancer research. Methods: Leveraging the GuardantINFORM database, which includes anonymized genomic data and structured payer claims, we focused on generating synthetic data for LUAD patient cohorts. This approach involves processing real patient data into a format compatible with Medisyn’s generative AI models, ensuring the synthetic data retains the original's statistical properties, and processing the output back into the original database structure and format. This method plays a crucial role in maintaining patient privacy and serves as a valuable tool for research by enabling the generation of realistic patients with desired properties on demand. Results: Our synthetic data closely mirrors real-world genomic and claims variable distributions, evidenced by a 0.994 R2 correlation between real and synthetic data along with comparable Oncoprints. Importantly, privacy tests show that patient confidentiality is effectively maintained despite this effective performance. The synthetic data's utility was then demonstrated in a study replicating real-world findings: LUAD patients with KRAS G12C in combination with STK11 mutations showed a significantly higher risk of early mortality. This underscores the potential of synthetic data in advancing cancer research. Conclusions: This research offers a promising avenue for the cancer research community. By providing a method to share privatized, synthetic genomic data, which can be combined and generated on demand, we enable broader, more responsible data sharing. This approach protects patient privacy and offers a rich dataset for groundbreaking research, potentially accelerating advances in cancer diagnosis and treatment. [Table: see text]
Assessing ChatGPT's potential as a clinical resource for medical oncologists: An evaluation with board-style questions and real-world patient cases.
Authors: Lee Hyun , Hamedi Zahra , Oliver David , Lin Hui , Athar Uzma , Vohra Hiba Zainab , Daniels Peter , Kothadia Sejal 
Published: May 2024

e13628 Background: ChatGPT is a conversational artificial intelligence (AI) model that learns from massive text-based datasets and then responds to user input, which often involves completing tasks or answering questions. Recent studies showed ChatGPT’s success in passing multiple specialty medical licensing and board examinations, showcasing its promising capabilities in the medical domain. Here, we investigated ChatGPT's potential as a swift and reliable information source for medical oncologists using board examination style questions and real patient cases. Methods: We randomly selected 121 board-style questions from the American Society of Clinical Oncology Self-Evaluation Program (ASCO SEP). The questions were entered into ChatGPT in both multiple-choice (MC) and open-ended (OE) prompts. ChatGPT’s answers and explanations were evaluated for accuracy and concordance. Non-inferiority analysis was performed with power of 80% at α = 0.05 and non-inferiority margin set at 70% correct answers given the historical board exam pass rate of about 65% correct answers. For subgroup analysis, the questions were categorized by tested competency and primary tumor pathology. ChatGPT was also given 10 questions derived from real patient cases. We compared its responses to the answers provided by experienced oncologists to determine accuracy and practical applicability. Results: ChatGPT answered 75 (62.0%) MC queries correctly. Among the correctly answered queries, 2 responses contained faulty explanations. Such inaccurate or discordant explanations were found in 26 of the 46 incorrectly answered queries. In OE prompts, ChatGPT answered 53 (43.8%) questions correctly with correct explanations for all. Of the 68 incorrect responses, 32 of them contained inaccurate or discordant explanations. Subgroup analysis suggested varying performance across the categories. The best performance was seen with malignant hematology (81.8% of MC and 72.8% of OE prompts answered correctly) while the weakest performance was seen with genitourinary malignancies (60% of MC and 20% of OE prompts answered correctly). As for the real-world patient case questions, responses from ChatGPT and the clinicians were concordant in 5 questions. None of the discordant responses contained inaccurate information while 80% of the concordant responses contained sufficient details to assist with patient management decisions. Conclusions: ChatGPT's performance fell short of the non-inferiority margin, highlighting the challenges with incorporating AI in the rapidly evolving field of medical oncology. Despite the limitations, ChatGPT’s partial success, in both board-style and real-world patient care questions, affirms its potential for clinical utility in future.