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Hybrid AI framework for automated prostate cancer disease state ascertainment from real-world electronic health records
Abstract   Peer reviewed

Hybrid AI framework for automated prostate cancer disease state ascertainment from real-world electronic health records

Umair Ayub, Syed Arsalan Ahmed Naqvi, Muhammad Uzair Sarfraz, Fouad Nahhat, Muhammad Umar Afzal, Muhammad Hussnain Sadiq, Muhammad Abdullah Humayun, Parminder Singh, Yousef Zakharia and Irbaz Bin Riaz
Journal of clinical oncology, Vol.44(16_suppl), pp.e17003-e17003
06/01/2026
DOI: 10.1200/JCO.2026.44.16_suppl.e17003

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Abstract

e17003Background: Accurate ascertainment of prostate cancer (PCa) disease states-biochemical recurrence (BCR), castration-sensitive prostate cancer (CSPC), and castration-resistant prostate cancer (CRPC)-is critical for automated clinical trial enrollment, quality improvement initiatives, and real-world evidence generation. Essential information for disease state ascertainment resides in unstructured EHR data, requiring labor-intensive manual abstraction that is inconsistent and not scalable. We developed an automated hybrid AI framework using large language models to identify PCa disease states from real-world EHR data. Methods: This retrospective study included patients with histopathologically confirmed PCa (2017-2022) and ≥3 years follow-up. Sequential EHR data-including PSA and testosterone levels, clinical notes, pathology and radiology reports-were retrieved across temporal points. BCR was identified using rule-based algorithms (post-radical prostatectomy [RP]: PSA ≥0.2 ng/mL ×2; post-radiation therapy [RT]: nadir + 2 ng/mL ×2). CRPC was determined using androgen deprivation therapy (ADT) timing, PSA kinetics, testosterone < 50 ng/dL, and radiographic progression per PCWG/EAU-ASCO criteria. GPT-4o with structured parametrized prompts extracted metastatic status and radiographic progression. Independent clinicians performed manual annotation for validation. The framework was developed on 10% of data with evaluation on 90%. External validation used a held-out cohort of clinical trial patients. Results: Among 200 PCa patients (median age 66 years [IQR 60-71]; 91% White, 93% non-Hispanic), the framework demonstrated high accuracy: PCa diagnosis (92%), RP dates (94%), RT dates (93%), and ADT initiation (92%). For disease state identification, BCR detection achieved 90% accuracy, castration status 93%, and metastatic state (M0/M1) 87%. External validation on trial-enrolled patients confirmed robust performance: 26/31 (84%) CRPC trial patients and 26/27 (96%) CSPC trial patients were correctly classified, validating the framework's reliability for clinical trial matching. Conclusions: This hybrid LLM-based framework accurately identifies PCa disease states using real-world EHR data with minimal manual intervention. The system offers a scalable, reproducible approach for automated disease state identification, enabling streamlined clinical trial matching and enhanced real-world data curation in oncology.

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