Naqvi, Syed Arsalan Ahmed

This guideline has been updated. This ASCO Living Guideline is reviewed on an eight-week cycle. Please see the online dynamic version for the latest updates. Living guidelines are developed for selected topic areas with rapidly evolving evidence that drives frequent change in recommended clinical practice. Living guidelines are updated on a regular schedule by a standing expert panel that systematically reviews the health literature on a continuous basis, as described in the ASCO Guidelines Methodology Manual . ASCO Living Guidelines follow the ASCO Conflict of Interest Policy Implementation for Clinical Practice Guidelines . Living Guidelines and updates are not intended to substitute for independent professional judgment of the treating clinician and do not account for individual variation among patients. See appendix for disclaimers and other important information (Appendix I and Appendix II). Updates are published regularly and can be found on the ASCO Publications website .

PURPOSE To evaluate open-source large language models (LLMs) for extracting cancer-specific phenotypic data, benchmark their performance against GPT4 models, and assess the impact of fine-tuning with training data sizes. METHODS Open-source LLMs (Mistral, LLaMa, MAMBA, BioMistral) were evaluated in zero-/one-shot and fine-tuned setups against GPT4-turbo/GPT4o to extract the cancer presence, progression, response, and metastatic sites from radiology impressions of patients with solid tumors treated at Dana-Farber Cancer Institute. Performance metrics (accuracy, precision, recall, F1-score) were computed. McNemar's odds ratio (OR), measuring which model is more likely to be correct when they disagree, was computed with 95% CI. Statistical significance was assessed using the alpha of .000139. RESULTS This study included 2,623 patients (25,273 radiology impressions). In zero-/one-shot settings, GPT4-turbo/GPT4o outperformed open-source LLMs. However, fine-tuned open-source LLMs achieved higher F1-scores than GPT4 models. Compared with the best-performing GPT4 model, fine-tuned Mistral0.2-7.3B (OR, 0.27 [95% CI, 0.20 to 0.36]; P < .00001), Mistral0.3-7.3B (OR, 0.26 [95% CI, 0.19 to 0.36]; P < .00001), LLaMa2-6.7B (OR, 0.30 [95% CI, 0.22 to 0.40]; P < .00001), LLaMa3.1-8B (OR, 0.37 [95% CI, 0.28 to 0.48]; P < .00001), and MAMBA-2.8B (OR, 0.32 [95% CI, 0.24 to 0.42]; P < .00001) showed significantly better performance in ascertaining disease progression. Performance was consistently better for inferring overall response, any evidence of cancer, and sites of metastases, with no significant differences among fine-tuned open-source LLMs. Fine-tuning gains plateaued at 25% of training data (5,718 impressions) and remained comparable at 5% (1,144 impressions). CONCLUSION Open-source LLMs, when fine-tuned using labeled data, can effectively automate the ascertainment of key radiophenotypic variables using only the impression section of radiology reports, without the full report text. Their consistent performance in small training sets suggests that these models may provide a scalable approach for phenotypic characterization of patients with cancer in real-world clinical settings.

ABSTRACT Background Artificial intelligence (AI) and automation offer opportunities to enhance the efficiency, timeliness, and sustainability of living guidelines (LGs). However, how AI and automation have been integrated into existing LG development frameworks remains unclear. This scoping review represents the first step in a broader programme of work aimed at developing a framework which aims to guide responsible and coordinated AI adoption across all phases of LG development. Objective To identify existing frameworks, methods, and approaches that integrate AI or automation into any stage of LG development. Methods We conducted a structured search of PubMed, Embase, Web of Science, Scopus, Cochrane Database for Systematic Review and Cochrane Central Register of Controlled Trials from inception to March 12, 2026. We included peer‐reviewed articles describing frameworks, models, or methods that applied AI or automation in the development of LGs. We extracted data into a standardised form, capturing study characteristics, AI methods, targeted guideline processes, and key findings and synthesised findings using a thematic narrative approach. Results Of 1090 records identified, three studies met the inclusion criteria. The included studies described AI or automation applied to select components of the LG process, to support continuous evidence surveillance, to semi‐automate study screening, and incremental updating of living systematic reviews. None addressed multiple stages of LG development in an integrated manner. Conclusions Current evidence demonstrates fragmented and narrowly focused AI or automation applications within LG development processes. Our findings highlight opportunities for future work to develop and evaluate more comprehensive frameworks that span multiple stages of the LG lifecycle.

ABSTRACT Background Men with castration‐resistant prostate cancer (CRPC) who have a pre‐existing history of cardiovascular disease (CVD) or other comorbidities are often excluded from clinical trials involving oral androgen receptor pathway inhibitors (ARPi). In this study, we compared all‐cause and prostate cancer‐specific mortality among CRPC patients, with and without a pre‐existing history of CVD, receiving ARPi compared to chemotherapy. Methods Men with CRPC were identified using the Surveillance, Epidemiology, and End Results‐Medicare Linked Database from 2004 to 2015. Patients were grouped into two analytical cohorts by drug use. Inverse probability treatment weights (IPTW)‐adjusted Cox models and Fine‐Gray subdistribution hazards models were used to evaluate associations between ARPi and chemotherapy, and between ENZ and AA for all‐cause mortality and cancer‐specific mortality separately. Results The study cohort included 1438 men with CRPC. Nearly 54.4% of patients had a pre‐existing history of CVD. Patients with a pre‐existing history of CVD had a higher incidence of all‐cause and prostate cancer‐specific mortality compared to patients without a history of CVD (all‐cause mortality: 69.7% vs. 59.3%, prostate cancer‐specific mortality: 56.0% vs. 49.5%, respectively). In the pre‐existing history of CVD cohort, the IPTW‐adjusted Cox model showed a significantly lower all‐cause mortality in patients who received APRi, enzalutamide, and abiraterone, versus chemotherapy (IPTW‐adjusted hazard ratio [AHR], 0.56; 95% Confidence Interval [CI], 0.48–0.64; p ‐value < 0.001). Further, the IPTW‐adjusted competing risk model showed significantly lower prostate cancer‐specific mortality in patients who received ARPi compared with those who received chemotherapy (IPTW‐AHR, 0.48; 95% CI, 0.41 to 0.57; p ‐value < 0.001). In the without pre‐existing history of CVD cohort, the adjusted Cox model showed significantly lower all‐cause mortality in patients who received APRi than those who received chemotherapy (IPTW‐AHR, 0.49; 95% CI, 0.41–0.60; p ‐value < 0.001). Whereas the IPTW‐adjusted competing risk model showed significantly lower prostate cancer‐specific mortality in patients who received ARPi compared with those who received chemotherapy (IPTW‐AHR, 0.52; 95% CI, 0.42–0.64; p ‐value < 0.001). Conclusion In this population‐based cohort of older men with castration‐resistant prostate cancer, treatment with oral androgen receptor pathway inhibitors was associated with lower estimated risks of all‐cause and prostate cancer–specific mortality compared with chemotherapy in patients with and without pre‐existing CVD. These findings add real‐world comparative effectiveness evidence for patient populations not well represented in randomized clinical trials; however, given the observational design and limitations of administrative data, residual confounding and unmeasured clinical differences may have influenced the results.

e17005 Background: We previously developed a hybrid framework combining LLMs and rule-based algorithms (RBA) for automated risk stratification in localized prostate cancer (locPCa), achieving 89% accuracy. However, clinical T-stage (cTS) inference from narrative MRI reports remained the primary source of errors. Herein, we evaluated whether ensemble approaches using multiple state-of-the-art LLMs could improve cTS inference and overall NCCN risk stratification accuracy. Methods: This study included patients with locPCa presenting with at least one positive prostate biopsy and MRI report available. Building on our previously validated hybrid framework that extracts structured phenotypic variables (PSA, prostate volume, cTS, Gleason patterns, grade group, positive and examined cores) from unstructured reports using LLMs and applies NCCN-concordant RBA for risk stratification, we focused on improving cTS inference. Four HIPAA-compliant LLMs (GPT-4.1, GPT-5.1, Gemini-2.5-Pro, Gemini-2.5-Flash) were evaluated using structured zero-shot prompts iteratively refined through oncologist feedback. Majority voting was employed to generate consensus cTS predictions. Performance was evaluated using weighted accuracy. Results: A total of 358 patients were included. The median age at diagnosis was 65 years (IQR: 60-69); the majority were White (92%) and non-Hispanic (94%). cTS distribution was T2a (51%), T2c (20%), T3a (16%), T3b (7%), T2b (4%), and T4 (2%). The most prevalent risk category was unfavorable intermediate (38%) followed by favorable intermediate (20%), high risk (19%), very high risk (16%), low (5%), and very low (1%). Individual LLM accuracy for cTS inference ranged from 85-90%, with Gemini-2.5-Pro achieving highest accuracy (90%), followed by GPT-4.1 (88%), GPT-5.1 (87%), and Gemini-2.5-Flash (85%). The four-model ensemble reached consensus in 92% of cases, achieving 93% cTS inference accuracy. When integrated into NCCN risk stratification schema, single-model approaches achieved 89-92% accuracy, while ensemble consensus cases achieved 95% accuracy. Conclusions: The ensemble framework using multiple LLMs significantly improves cTS inference and achieves 95% NCCN risk stratification accuracy in consensus cases (92% of patients), representing a 6-percentage-point improvement over our previous single-model framework (89%). This strategy demonstrates that ensemble methods can overcome individual model limitations for complex clinical tasks and holds promise for reliable AI-assisted risk stratification.

e17004 Background: Living clinical practice guidelines offer the opportunity to keep recommendations aligned with the latest evidence, but they also present a fundamental challenge: as evidence accumulates, panelists cannot sustainably query and synthesize a growing body of literature. In developing ASCO's living metastatic castration-resistant prostate cancer (mCRPC) guideline, we encountered this challenge firsthand. To address it, we developed and evaluated an interactive agentic framework that enables guideline panelists to query evidence in natural language and retrieve structured facts, tables, and figures—with the goal of extending this tool as a clinician-facing companion to enhance guideline accessibility. Methods: The mCRPC systematic review dataset supporting ASCO's clinical practice guideline was used, comprising 212 structured columns capturing trial characteristics, patient populations, and outcomes extracted from 188 references across 88 clinical trials. A large language model (LLM)-based agentic framework was developed employing a coding agent that translates natural language queries into executable scripts for structured evidence retrieval. Claude (Haiku 4.5, Sonnet 4.5, and Opus 4.5) served as the core LLM agents. We constructed a benchmark of 50 fact-based queries with gold-standard answers targeting trial identification, treatment modalities, and endpoint characteristics (e.g., "Which trials evaluated systemic monotherapy?"). Performance was evaluated using exact-match accuracy for final answers, and precision, recall, and F1 score for trial-level retrieval. Results: Across 50 queries, exact-match accuracy ranged from 82% to 86%. Haiku 4.5 and Sonnet 4.5 each achieved 82% (42/50), while Opus 4.5 achieved 86% (43/50). Precision ranged from 0.93–0.97, recall from 0.94–0.96, and F1 scores from 0.91–0.94. Conclusions: This agentic framework enables scalable, auditable, natural language interrogation of guideline evidence, addressing a critical bottleneck in living guideline maintenance. For panelists, it supports rapid, reproducible evidence surveillance during recommendation development. For clinicians, it offers a foundation for an interactive companion tool that transforms static guidelines into queryable resources—enabling personalized evidence retrieval at the point of care. LLM EM Precision Recall F1 claude-haiku-4-5 0.82(42/50) 0.95 0.94 0.91 claude-sonnet-4-5 0.82(42/50) 0.97 0.96 0.93 claude-opus-4-5 0.86(43/50) 0.93 0.96 0.94

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.

4589 Background: Recent years have seen a shift in aUC management following approval of enfortumab vedotin–pembrolizumab (EV-P) and gemcitabine/cisplatin plus nivolumab (GC-NIVO) as first-line therapies. Despite improved clinical outcomes, their relative economic value remains uncertain. Herein, we conducted a cost-effectiveness analysis of current 1L regimens in aUC from a U.S. payer perspective using 2026 pricing. Methods: A partitioned survival model with three health states (progression-free [PF], progressed disease [PD], and death) was developed over a 10-year horizon from a U.S. payer perspective. Analyses focused on cisplatin-eligible patients. Survival data were reconstructed from published Kaplan–Meier curves and extrapolated using parametric survival models. Treatment dosing, schedules, and duration were modeled according to trial protocols. Patients receiving gemcitabine/cisplatin without progression were modeled to receive avelumab maintenance (GC→AVEL). Subsequent-line therapy costs were applied at incident progression, and grade ≥3 adverse event costs and disutilities were applied as one-time events. Drug acquisition costs (2026 USD) were based on average sales prices from the Centers for Medicare & Medicaid Services. Costs and quality-adjusted life years (QALYs) were discounted at 3% annually. Incremental cost-effectiveness ratios (ICERs) and net monetary benefit (NMB) were evaluated at willingness-to-pay (WTP) thresholds of $100,000, $150,000, and $200,000 per QALY. Parameter uncertainty was characterized using probabilistic sensitivity analysis. Results: In the base-case analysis, total discounted costs and QALYs were $90,269 and 1.62 for GC→AVEL, $131,836 and 2.09 for GC-NIVO, and $1,451,885 and 2.77 for EV-P. Compared with GC→AVEL, GC-NIVO gained 0.46 QALYs at an incremental cost of $41,567, resulting in an ICER of $89,501 per QALY and positive NMB across all evaluated WTP thresholds. Compared with GC→AVEL, EV-P gained 1.15 QALYs at an incremental cost of $1.36 million (ICER $1.19 million per QALY). Compared with GC-NIVO, EV-P gained 0.68 QALYs at an incremental cost of $1.32 million (ICER $1.93 million per QALY), exceeding all evaluated WTP thresholds. In a scenario analysis limiting EV-P to 10 treatment cycles, total costs decreased to $287,360, with ICERs of $172,000/QALY versus GC→AVEL and $225,000/QALY versus GC-NIVO, remaining above the $150,000/QALY threshold. Conclusions: Using updated 2026 pricing, GC-NIVO is likely to be the most cost-effective first-line regimen for cisplatin-eligible patients with aUC. EV-P offers the greatest clinical benefit but is not cost-effective at current prices. A major limitation of this study is cross-trial variability in study populations and design, limiting the validity of head-to-head economic comparisons.

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)