Sadiq, Muhammad Hussnain

Abstract Background Diagnosis and risk stratification in rare hematologic malignancies such as myeloproliferative neoplasms (MPNs) – polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF) – require expert review of longitudinal, heterogeneous clinical records. This process is cognitively demanding, inconsistently applied, and difficult to scale beyond tertiary centers. No automated phenotyping workflow currently exists for hematologic malignancies. Methods A HIPAA-compliant large language model (LLM) framework for phenotyping MPN was developed to integrate (i) rule-based retrieval of bone marrow biopsy reports, clinical notes, and structured laboratory results from the electronic health record (EHR); (ii) zero-shot extraction of diagnostic and prognostic variables from unstructured text using GPT-4 Turbo; (iii) a clinician-informed source-prioritization algorithm to reconcile conflicting multi-source data; (iv) WHO/ICC-criteria–based diagnostic classification; and (v) NCCN-based risk stratification using the conventional risk model for PV, IPSET-thrombosis for ET, and DIPSS, DIPSS-plus, and MIPSS70/MIPSS70+ v2 for MF. Patients were identified via MPN-related ICD-9/10 codes; cases met 2017 WHO criteria or had a hematologist-documented diagnosis, and controls did not. The cohort was split into a prompt-development set (n = 60) and a held-out test set (n = 450; 75 cases and 75 controls per disease). Ground truth was established by independent dual-clinician chart review with consensus adjudication. LLM performance was evaluated against the ground truth: variable-level extraction using accuracy, F1 score, and Cohen’s kappa; patient-level diagnostic classification using sensitivity, specificity, and Cohen’s kappa; and prognostic risk stratification (among confirmed cases) using accuracy, weighted F1 score, and quadratic-weighted Cohen’s kappa. Wilson 95% confidence intervals (CIs) were used for proportions and bootstrap 95% CIs with 500 resamples for F1 scores. Results The held-out test set included 450 patients (PV: 150; ET: 150; MF: 150) with pathology reports and structured laboratory results, and 172 patients (PV: 52; ET: 55; MF: 65) with clinical notes. From pathology reports, overall variable extraction accuracy and F1 score were 99% (95% CI, 98–100) and 1.00 (0.99–1.00) for PV, 100% (99–100) and 0.99 (0.96–1.00) for ET, and 100% (99–100) and 0.99 (0.97–1.00) for MF. From clinical notes, overall accuracy and F1 score were 96% (91–100) and 0.94 (0.85–1.00) for PV, 100% (100–100) and 1.00 (1.00–1.00) for ET, and 100% (99–100) and 0.98 (0.95–1.00) for MF. Diagnostic sensitivity was 100% (95% CI, 95.1–100.0) for PV, ET, and MF; specificity was 98.7% (92.8–99.8) for PV and 100% (95.1–100.0) for both ET and MF, with Cohen’s kappa of 0.99 for PV and 1.00 for ET and MF. Risk stratification accuracy was 100% with weighted F1 score of 1.00 and quadratic-weighted Cohen’s kappa of 1.00 across all three diseases. A pre-specified source-ablation analysis showed that pathology reports alone were sufficient for diagnosis (sensitivity 98.7% for PV, 100% for ET, 96.0% for MF; specificity 100% across all three subtypes) but inadequate for prognostication (accuracy 69.3% for PV, 93.3% for ET, 77.3% for MF). Adding clinical notes to pathology reports recovered full prognostic accuracy of 100% across all three diseases. Conclusions This first-in-class automated framework achieved expert-level performance for MPN diagnosis and risk stratification from real-world EHR data, establishing a foundation for scalable, standardized phenotyping in rare hematologic malignancies. Prospective, multi-site validation is warranted before clinical deployment.

e17003 Background: 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.

567 Background: Selecting adjuvant therapy after nephrectomy for renal cell carcinoma (RCC) requires reviewing scattered electronic health records (EHR) and performing accurate eligibility and risk assessments. Large Language Models (LLMs) may assist physicians by extracting key clinical data and generating evidence-based recommendations between active surveillance and adjuvant pembrolizumab. Methods: We selected RCC patients who underwent nephrectomy at Mayo Clinic in Arizona, Florida, and Minnesota between December 2021 and September 2025. Data extraction from EHRs was performed by a HIPAA-compliant LLM (GPT-5 and Gemini 2.0-Flash-001) and compared to human labels. Data on patients’ demographics, medical history, pathology, laboratory results, and imaging were collected. GPT-5 generated treatment recommendations using a structured prompt that incorporated patients’ features and relevant clinical information that is available to clinicians. Concordance between the human labels and LLM was evaluated for extraction. For treatment recommendations, agreement between the LLM’s output and physician’s documented recommendation in clinical notes was assessed, with disagreements reviewed independently by two blinded genitourinary oncologists. Results: The dataset included 85 patients, median (range) age of 65 (34-84) years; 24.7% were females. Patients who declined adjuvant treatment for personal reasons were excluded. LLM achieved an extraction accuracy of 95% for presenting symptoms, 97% for BMI and ECOG, and > 99% for pathology variables. Concordance between LLM and physician treatment recommendations was 84.7% (72/85). Among 13 discrepant cases, one oncologist agreed with the LLM in 9 cases and the other in 8. The main reasons for disagreement between the physicians and the LLM were that the LLM favored active surveillance for patients with declining renal function, whereas physicians still considered adjuvant pembrolizumab appropriate in such cases. Additionally, the LLM demonstrated hesitancy to recommend pembrolizumab for patients with low performance status. Overall, the 13 discrepant cases were nuanced and challenging, with disagreement observed between the two oncologists themselves in three instances. Conclusions: LLMs demonstrated high accuracy in extracting clinically relevant variables and generating objective, evidence-based adjuvant treatment recommendations for RCC after nephrectomy. Importantly, LLM did not generate any inappropriate or potentially harmful recommendations. Our pipeline can serve as a supportive decision tool to enhance consistency in post-nephrectomy management and assist physicians in choosing between active surveillance and adjuvant pembrolizumab. However, final treatment decisions should remain the responsibility of the treating physician after comprehensive patient evaluation.

360 Background: Adding NHT to androgen deprivation therapy (ADT) improves survival in mCSPC, but their comparative impact on patient-reported QoL and time to deterioration (TTD) across FACT-P domains remains underexplored. Methods: MEDLINE and Embase were systematically searched from each database’s inception through August 21, 2025, to identify Phase III trials assessing NHT treatment in mCSPC. Time to FACT-P (total, physical, emotional, functional, and social/family well-being) deterioration across trials with the latest follow up were extracted. Hazards ratios (HRs) and 95% confidence intervals (CIs) were pooled and mixed treatment comparisons were made using network meta-analysis. P-scores were computed; a higher score indicated better QoL. Results: A total of 4 trials (12 references) with 4,037 patients met the inclusion criteria with median follow up ranging from 14.4 months to 30 months. Median time to QoL deterioration ranged from 11.4 to 12.9 months. In terms of FACT-P overall, darolutamide (DARO)+ADT (HR: 0.76; 95% CI: 0.62-0.93), abiraterone acetate (AAP)+ADT (0.85; 0.73-0.98) significantly improved TTD as compared to ADT alone. DARO+ADT was associated with significant improvement in TTD when compared to APA+ADT (0.75; 0.56-0.98). In terms of FACT-P social/family wellbeing, DARO+ADT significantly improved TTD when compared to AAP+ADT (0.75; 0.57-0.96), and ADT (0.79; 0.64-0.98). Likewise, ENZA+ADT significantly improved TTD when compared to AAP+ADT (0.77; 0.63-0.95), APA+ADT (0.88; 0.61-0.99) and ADT (0.82; 0.70-0.95). However, there was no statistically significant difference between DARO+ADT and ENZA+ADT. In terms of FACT-P physical wellbeing, AAP+ADT was associated with statistically significant improvement in TTD when compared to APA+ADT (0.66; 0.52-0.83), ENZA+ADT (0.74; 0.61-0.90), and ADT (0.75; 0.65-0.87). In terms of FACT-P functional wellbeing, DARO+ADT was associated with statistically significant improvement in TTD when compared to ADT (0.78; 0.63-0.96). The ranking analysis is showed in Table. Conclusions: Novel hormonal therapies significantly delay QoL deterioration in mCSPC, particularly across overall, emotional, and functional domains. Domain-specific analyses suggest darolutamide may preserve social/family and functional well-being, while abiraterone favors physical well-being. Rank table. Rank (P-score) FACT-Ptotal FACT-Psocial/family FACT-Pphysical FACT-Pfunctional FACT-Pemotional AAP+ADT 2 (0.73) 5 (0.18) 1 (0.98) 2 (0.62) 3 (0.52) APA+ADT 5 (0.17) 4 (0.22) 5 (0.07) 4 (0.33) 4 (0.52) DARO+ADT 1 (0.90) 1 (0.88) 2 (0.68) 1 (0.92) 2 (0.66) ENZA+ADT 3 (0.50) 2 (0.84) 4 (0.36) 3 (0.52) 1 (0.67) ADT 4 (0.18) 3 (0.38) 3 (0.42) 5 (0.11) 5 (0.13)

145 Background: Multiple trials have demonstrated addition of androgen-receptor pathway inhibitors (ARPIs) improve survival in patients with metastatic prostate cancer but there is a need to quantify cardiovascular toxicity. Understanding the nature and extent of ARPI-associated cardiovascular adverse events across disease states and toxicity grades is critical to inform treatment decisions and long-term patient safety. Methods: MEDLINE, and EMBASE were systematically searched from database inception through August 30, 2025, for phase III randomized controlled trials evaluating abiraterone, enzalutamide, apalutamide, darolutamide, orteronel in metastatic prostate cancer. Trials were included if they reported CVS toxicity. Meta-analysis using a random-effects model was performed to assess the difference of all-cause and treatment-emergent cardiovascular adverse events between ARPI+ADT and ADT alone. Summary effects were expressed as relative risk (RR) with associated 95% confidence intervals. Results: A total of 21 trials (45 references) with 25,576 patients (mCSPC: 12,537, mCRPC: 13,039) were included in this meta-analysis. Compared to ADT alone, ARPI+ADT significantly increased the risk of all cause, all grade hypertension in both mCSPC (RR 1.60, 1.22–2.10) and mCRPC (RR 1.61, 1.20–2.16) as well as grade ≥ 3 hypertension (RR 1.71, 1.17–2.48) in mCRPC setting. There was an increased risk of all-cause, all grade acute coronary syndrome (RR 2.21, 1.09–4.49) and AV nodal disease (RR 2.53, 1.18–5.43) with ARPI+ADT compared to ADT alone in mCSPC setting. There was increased risk of all cause, all grade, any cardiac events (RR 1.30, 1.10–1.54) as well as grade ≥ 3 any cardiac events (RR 1.70, 1.20–2.42) in mCRPC setting. Risk of all cause, all grade hot flashes (RR 1.47, 1.16–1.85) was also significantly increased with ARPI+ADT when compared to ADT alone. In terms of treatment-emergent adverse effects, a significant increase was observed only for pulmonary embolism with ARPI+ADT when compared to ADT alone (RR 1.66, 1.06–2.60) in mCRPC setting. Conclusions: Current evidence indicates potentially increased risk of hypertension, venous thromboembolic, and ischemic cardiac events with ARPI therapy in metastatic prostate cancer. Proactive CV risk assessment, particularly in patients with preexisting comorbidities or prolonged ARPI exposure, may optimize outcomes and minimize treatment interruptions. All cause adverse events. Adverse Event Disease State All Grade Grade ≥3 Any Cardiac Event mCSPC 0.81 (0.37–1.76) 0.88 (0.16–4.64) mCRPC 1.30 (1.10–1.54) 1.70 (1.20–2.42) Hypertension mCSPC 1.60 (1.22–2.10) 1.43 (0.82–2.52) mCRPC 1.61 (1.20–2.16) 1.71 (1.17–2.48) ACS mCSPC 2.21 (1.09–4.49) 1.77 (0.86–3.61) mCRPC – – Hot Flashes mCSPC – – mCRPC 1.47 (1.16–1.85) 1.46 (0.21–9.97)

129 Background: Androgen receptor pathway inhibitors (ARPIs) improve metastasis-free and overall survival in men with non-metastatic hormone-resistant prostate cancer (nmHRPC). Apalutamide and darolutamide were approved by the Federal Drug Administration in 2018 and 2019, respectively, based on pivotal clinical trials showing similar efficacy. However, no head-to-head comparisons exist in real-world settings. We compared prescription patterns and survival outcomes between apalutamide and darolutamide in men with nmHRPC using the Surveillance, Epidemiology and End Results (SEER)-Medicare data. Methods: We identified men aged ≥66 years with localized or locoregional prostate cancer between 2000 and 2021 using the SEER-Medicare linked database. Eligible patients initiated apalutamide or darolutamide between January 2019 and December 2021, defined by Medicare Part D claims. The index date was the first prescription of either agent. Men with distant metastases, prior malignancies and age ≤65 were excluded. Baseline demographics and clinical characteristics were compared using chi-square and Wilcoxon rank-sum tests. Survival was assessed using Kaplan-Meier analysis with log-rank tests. Cox proportional hazards regression was used to estimate adjusted hazard ratios (HRs) for mortality. Results: Among 582 men with nmHRPC, 435 (74.7%) received apalutamide and 147 (25.3%) received darolutamide. The mean age at diagnosis was 73.8 vs 74.1 years (p=0.95). Most patients were White (84.6% vs 81.0%, p=0.21) and resided in metropolitan areas (85.0% vs 88.4%, p=0.39). Darolutamide use was more frequent in the Northeast (42.9% vs 24.6%, p<0.001), while apalutamide was more common in the South (28.7% vs 15.6%, p<0.001). Patients receiving darolutamide had higher-grade tumors (68.0% grade III vs 51.3%, p=0.002) and a longer median time from diagnosis to prescription (93 vs 64 months, p<0.001). Kaplan-Meier curves and adjusted Cox models showed no significant difference in overall survival (HR 1.00; 95% CI 0.51–1.98; p=1.00). Age was independently associated with mortality (HR 1.05 per year; 95% CI 1.00–1.10; p=0.04). Conclusions: Apalutamide and darolutamide demonstrated comparable real-world survival outcomes in nmHRPC. Geographic and clinical factors influenced prescribing patterns, but survival did not differ between agents. Treatment choice may therefore be guided by patient comorbidities, tolerability, and clinician preference. Variable Comparison HR (95% CI) P-Value Treatment Darolutamide vs Apalutamide 1.00 (0.51,1.98) 1.00 Age at Diagnosis 1.05 (1.00, 1.10) 0.04* Race White vs Non-White 0.71 (0.28, 1.80) 0.47 Marital Status Married vs Unmarried 1.29 (0.60, 2.75) 0.51 Geographic Region -Midwest vs West 1.18 (0.34, 4.15) 0.79 -Northeast vs West 1.92 (0.92, 3.88) 0.07 -South vs West 1.01 (0.43, 2.36) 0.99