Khan, Muhammad Ali

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.

e17155 Background: Localized PCa is classified into six NCCN risk categories, each with specific treatment recommendations. However, treatment concordance across these categories and its variation by race in clinical practice remain unexplored. Therefore, we aim to describe patterns of guideline-concordant treatment receipt for localized PCa across risk groups and race. Methods: Surveillance, Epidemiology, and End Results (SEER) database (2018–2020) was queried to identify patients with localized PCa from 17 registries. Data regarding clinical T stage, Gleason patterns, PSA level, biopsy cores and initial treatment received (radiation, radical prostatectomy, systemic therapy) was obtained. Each patient was then stratified into one of six NCCN-based risk categories using a rule-based algorithm. Using the stratified risk category of each patient, treatment recommendations were obtained from the NCCN guidelines. Concordance was assessed by comparing the received treatment and guideline-recommended treatment. A focused bi-variate logistic regression analysis was performed adjusting for risk groups, and race. Odds ratio (OR) with 95% confidence intervals (CI) were computed to quantify the magnitude of guideline-discordant treatment receipt. Results: This analysis included 69,584 patients with localized PCa (White: 53,875 [77.4%]; Black: 11,265 [16.2%]; Asian/Pacific Islander: 4,133 [5.9%]; American Indian/Alaska Native: 311 0.5%]). In terms of risk groups, patients in higher risk categories had increased odds of discordance compared to the lower risk groups (Table). In terms of race, Black men had significantly higher odds of receiving guideline-discordant therapy (OR:1.67, 95% CI:1.58–1.76), while no statistically significant difference was observed with American Indian/Alaska Native men (OR:1.21, 95% CI: 0.92–1.61) and Asian/Pacific Islander men (OR:1.05, 95% CI:0.97–1.14) compared to White men. Conclusions: Population level data, based on limited sample size in some groups, suggests that treatment concordance with NCCN guidelines varies by risk group and race in localized PCa. Black patients, and those with higher risk groups were more likely to receive guideline-discordant therapy. These findings highlight the need for strategies and prospective studies at individual patient level to evaluate and enhance guideline adherence to promote personalized care for these patients. Variable Discordant Treatment (%) OR (95% CI) White 22168 (41.1) REF Black 5653 (47.5) 1.67 (1.58-1.76) * Asian/Pacific Islander 1822 (44.1) 1.05 (0.97-1.14) American Indian/Alaska Native 191 (61.4) 1.22 (0.92- 1.61) Very Low Risk 770 (9) REF Low Risk 146 (2) 0.16 (0.13-0.19) * Favorable Risk 35 (0.4) 0.03 (0.02-0.04) * Unfavorable Risk 15125 (83.2) 36.30 (33.72-39.25) * High/Very High Risk 13441 (50) 7.32 (6.83-7.86) * * p <0.05.

261 Background: Clinicians use next-generation sequencing (NGS) to tailor targeted treatments for PCa patients. However, the results are often available in unstructured formats and are saturated with technical jargon, making them inaccessible for patients. Therefore, we aimed to assess the efficacy of LLMs to accurately extract genomic characteristics from unstructured reports and generate patient-friendly summaries for ease of variant interpretation in PCa patients. Methods: This retrospective study included patients with PCa who underwent NGS from 2023-2024. The GPT4 model was utilized using structured zero-shot prompts to extract genomic characteristics from unstructured reports in a non-decomposed manner. Prompt development was conducted iteratively using a 2% random sample of the dataset. Genomic characteristics ascertained by LLM were assessed against held out vendor-curated test dataset for evaluation. Performance was assessed using weighted evaluation metrics (precision, recall, and F1-score). Additionally, the extracted variables were organized using rule-based criterion and presented as a separate prompt to GPT4 to generate summaries for interpretation at the level of each biologically relevant variant. Mean Flesch-Kincaid scores and Vocd-D statistic with standard deviations were computed to assess readability and lexical diversity of LLM-generated variant interpretation summaries. Results: A total of 331 patients (370 NGS reports) were included in the study. Evaluation on the held-out test dataset (Table) showed that the weighted average precision ranged from 80% for extracting tumor mutational burden to 100% for extracting the CtDNA fraction from unstructured reports. The weighted average recall ranged from 85% for extracting CtDNA tumor fraction to 99% for ascertaining variant/alteration. The weighted average F1-scores varied from 0.83 for extracting tumor mutational burden to 1.0 for extracting specimen site from NGS reports. The mean Flesch-Kincaid score was 8.29 ± 1 indicating average/standard readability ease. The mean Vocd-D statistic was 7.55 ± 1 indicating focused summaries, however, with limited lexical diversity. Conclusions: This study suggests that large language models can effectively extract genomic characteristics from unstructured reports and can potentially improve genomic literacy among prostate cancer patients by providing easy-to-interpret variant summaries. However, targeted prompting may be required to increase lexical diversity for improved engagement. Performance across different genomic characteristics. Category Precision Recall F1-Score Altered gene 0.86 0.97 0.89 Variant/alteration type 0.98 0.99 0.98 Variant allele fraction 0.96 0.96 0.96 Test name/type 1 0.99 1 Specimen site 1 0.99 1 Tumor mutational burden 0.8 0.93 0.83 CtDNA tumor fraction 1 0.85 0.92 MSI status 0.99 0.97 0.98

270 Background: Inconsistent reporting of subgroup data across clinical trials makes interpretation and application of the evidence from trials challenging. We conducted a living systematic review to summarize evidence for American Society of Clinical Oncology (ASCO) guidelines for management of metastatic castration resistant prostate cancer (mCRPC). Here, we provide an overview of reporting patterns of subgroup data across mCRPC trials. Methods: MEDLINE and EMBASE were systematically searched from each database's inception through September 20 th , 2024, to identify phase II/III/IV randomized clinical trials assessing the efficacy and safety of treatment options in mCRPC. For outcomes of overall survival (OS) and progression free survival (PFS), data from the subgroup analyses was collected and compared across trials to assess heterogeneity in the reporting of subgroups. Results: This analysis included 141 trials (phase II: 83; phase III: 56; phase IV: 2). OS was reported by 113 (80%) trials; 32 (28%) reported OS by any subgroups with 95 unique subgroup categories. PFS was reported by 110 (78%) trials; 23 (21%) reported PFS by any subgroups with 63 unique subgroup categories. Even among consistently reported subgroups, such as age, ECOG performance status, race, geographical area, and Gleason score, considerable heterogeneity in different subgroup levels/thresholds was observed across trials. Top ten most commonly reported subgroups are summarized (Table). Conclusions: Lack of consistent reporting for subgroup analysis in mCRPC trials preclude meaningful conclusions about clinically relevant subgroup and synthesizing evidence for clinical guidelines. A standardized framework for consistent reporting of subgroup data across trials can help inform clinical practice and clinical practice guidelines. OS PFS Number of trials 113 110 Reporting subgroup analysis – N (%) 32 (28) 23 (21) Commonly reported subgroups – (N) Age (26)ECOG-PS (26)LDH (21)PSA (18)Geographic area (15)Alkaline phosphatase (14)Visceral disease (14)Gleason score (9)Race (9)Brief pain inventory level (8) Age (21) ECOG-PS (19)PSA (18)LDH (14)Geographic area (13)Visceral disease (11)Gleason score (10)Alkaline phosphatase (9)Bone metastasis (9)Disease location (9) Age – (N) <65 (14); ≥65 (9); ≤68 (1); ≥69 (1); <70 (5); ≥70 (5); ≤74 (4); 65-75 (5); ≤ 71 (1); >71 (1); ≥75 (11); >Median (1); <Median (1) <65 (12); 65-74 (6); ≥65 (6); <70 (5); ≥70 (5); <75 (4); ≥75 (10); 76-80 (1); >80 (1) ECOG-PS – (N) 0 (15); 0-1 (11), 1 (11); 1-2 (5); 2 (12) 0 (15); 0-1 (5), 1 (12); 1-2 (2); 2 (6) Race – (N) White (9)Non-white (6)African American/black (2)Asian (2) White (8)Non-white (5)African American/black (2)Asian (4) Geographic Area – (N) Europe (8)North America (14)United States (1)Australia (1)Asia (2) Europe (11)North America (13)United States (1)Australia (1)Asia (2)North and South America (2) Gleason Score – (N) 2-6 (1); 7 (1); 8 (1); <8 (8); ≥ 8 (8); 9-10 (1) <8 (10); ≥ 8 (10)

520 Background: The annual incidence of renal cancer is 0.02% among the general United States population. End stage renal disease (ESRD) and exposure to dialysis are thought to be the primary drivers of increased risk of renal cancer in renal transplant patients rather than immunosuppression. This has been evidenced by how the incidence of RCC in heart transplant recipients is no different than the normal population. In this study, we aim to characterize RCC incidence among kidney transplant patients (usually exposed to ESRD/dialysis) versus other solid organ transplant patients (usually unexposed to ESRD/dialysis). Methods: We used CPT codes to search for patients who had received a solid organ transplant (kidney, liver, heart, lung, pancreas) at all Mayo Clinic sites from 1/1/2018 to 1/1/2024. Within this cohort, we identified those who had been diagnosed with kidney cancer after initiating dialysis or receiving a transplant, and retrospectively extracted data related to demographics, cancer diagnosis, and transplant details. Results: We identified 10873 patients who had received a solid organ transplant (Table 1). Of these, 178 (1.76%) were diagnosed with kidney cancer after either starting dialysis or receiving a transplant. These 178 patients were of median age 56 years at time of cancer diagnosis (STD 12), 71% male, 70% white, 20% black, 4.5% Asian, 2.8% American Native. Of 5792 patients who had received a kidney transplant (with or without another solid organ transplant), 172 (2.97%) were diagnosed with kidney cancer after having started dialysis or receiving a kidney transplant. Of these 172, 156 (91%) were exposed to dialysis (107 primarily hemodialysis and 48 peritoneal). Of 4392 patients who had received a solid organ transplant other than kidney (liver, heart, lung, and/or pancreas), 6 (0.14%) were diagnosed with kidney cancer after having received a kidney transplant. Of these 6, none underwent dialysis. Conclusions: Patients with kidney transplants are at increased risk of kidney cancer compared to patients with non-kidney solid organ transplants (2.97% vs 0.14%). This supports prior evidence showing ESRD plays a significant role in the pathogenesis of kidney cancer. Patients who received solid organ transplants at Mayo Clinic from 2018-2024 and were subsequently diagnosed with kidney cancer. n % With RCC after TXP 178 1.7587% Cancer Diagnosis after Kidney HD/TXP 172 2.9696% Cancer Diagnosis after Other TXP 6 0.1386% Patients who received solid organ transplants at Mayo Clinic from 2018-2024 n % Unique Patients 10121 100% Kidney Transplant 5792 57.23% Alone 5088 50.27% &Liver 332 3.28% &Heart 119 1.18% &Lung 8 0.08% &Pancreas 245 2.42% Non-kidney Transplant (Liver, Heart, Lung, or Pancreas) 4329 42.77% TXP: Transplant, HD: Hemodialysis.

745 Background: The combination of EV-P therapy has recently been approved as first-line (1L) therapy for patients with LA/mUC based on the results of the EV-302 trial. Herein, we report the updated results from a cohort of patients with LA/mUC who received EV-P in real world clinical setting. Methods: This retrospective study included patients with LA/mUC (07/2022-08/2024) at the Mayo Clinic who completed at least one cycle of EV-P. The best overall response (BOR) was evaluated using radiographic imaging and was adjudicated as complete response (CR), partial response (PR), stable disease (SD), mixed response (MR), or progressive disease (PD). The median progression-free survival (PFS), defined as the time from treatment initiation to disease progression or death, was estimated using the Kaplan-Meier method. Patients who had not experienced disease progression by their last follow-up were censored for the PFS analysis. Results: A total of 120 patients were included; 79 (65.8%) were males, 41 (34.1%) females, 109 (91%) White, and 119 (99%) were non-Hispanic/Latino. The median age was 72 years (IQR: 65-77). Of these, only 22 (18.3%) had locally advanced disease while the remaining had mUC; 62 (51.6%) had lower tract urothelial carcinoma (LUTC), 36 (30%) had upper tract urothelial carcinoma (UTUC). The median follow-up time was 7.1 months (IQR: 4.93-9.26). In terms of BOR, 56 patients (46.6%) experienced PR, 34 (28.3%) CR, 12 (10%) PD, 7 (5.8%) MR, and 8 (6.6%) experienced SD. 3 (2.5%) patients were not evaluable. The BOR across subgroups, LA and mUC, including LUTC and UTUC, is summarized in the table. Among the responders, the median number of treatment cycles to first response was 3 (IQR: 2.25–4). Within the follow-up, 28 patients (23.3%) experienced eventual radiographic disease progression; death was observed in 25 patients (20.8%). The median PFS was 12.7 months (95% CI: 9.8-NE), and OS was 25.1 months (16.3-NE). Conclusions: In real world setting, the combination of EV-P in LA/mUC demonstrated clinical efficacy compatible with the findings from the EV-302 trial. Our results showed comparable efficacy with EV-P in both UTUC and LUTC, highlighting it's broad therapeutic potential. However, clinical data at longer follow-up is required to fully evaluate overall survival and the durability of response. mUC LA Overall LUTC UTUC Total patients 98 62 36 22 BOR CR 23 (23.4%) 13 (20.9%) 10 (28.5%) 11 (50%) PR 49 (52.6%) 31 (50%) 18 (50%) 7 (31.8%) SD 6 (6.4%) 2 (3.2%) 4 (11.4%) 2 (9.0%) PD 10 (10.7%) 9 (15.5%) 1 (2.8%) 2 (9.0%) MR 7 (7.5%) 5 (8.6%) 2 (5.7%) 0 (0%)

134 Background: Prostate cancer patients with visceral metastases at diagnosis are often treated using one-size-fits-all approach. For example, CHAARTED criteria treats any visceral metastasis as high volume. Therefore, we aimed to assess overall survival (OS) by site of visceral involvement in patients with metastatic PCa. Methods: Surveillance, Epidemiology, and End Results (SEER) database (2010-2021) was queried to obtain case listing data on metastatic PCa patients from 17 registries. The patients were categorized into three groups based on visceral metastasis at diagnosis: lung metastasis, liver metastasis, and others. Kaplan-Meier survival analysis was performed to estimate median OS with 95% confidence interval (CI). Log-rank test was conducted to assess if survival was significantly different among the groups. Cox proportional hazard regression analysis was performed to assess the magnitude of difference among the groups. P-value <0.05 established statistical significance. Results: This analysis included 21,007 metastatic PCa patients (lung: n=319; liver: n=123; brain: 107; bone: 20538). Metastatic PCa population with lung metastasis exhibited a median OS of 60 months (95% CI: 48-72) compared to 24 months (95% CI: 12-36) in population with liver metastases and to 36 months (95% CI: 36-36) in population with other metastases. The difference in OS among the groups was statistically significant (p < 0.001). Compared to population with lung metastasis, those with liver (HR: 1.91; 95% CI: 1.41-2.57) but not those with other metastases (1.16; 0.97-1.39) had significantly worse OS adjusting for Gleason score, PSA and race (Table). Conclusions: In metastatic PCa, patients with lung-only metastasis have significantly better overall survival and should be considered differently in prognostication to liver metastases. Consideration of site of visceral metastasis is critical for treatment intensification decisions. Limitations of this analysis include lack of accounting for confounding relationships at individual patient level. Variable Hazard Ratio (95% CI) Metastases (Lung) REFERENCE Metastases (Liver) 1.91 (1.41-2.57) Metastases (Other) 1.16 (0.97-1.39) Gleason score≥8 REFERENCE Gleason score <8 0.68 (0.64 -0.72) PSA - ng/dL 1.01 (1.00 – 1.01) Race (White) REFERENCE Race (Black) 0.99 (0.94 – 1.05) Race (Asian or Pacific Islander) 0.70 (0.64 – 0.76)

132 Background: Distant metastasis at the time of prostate cancer (PCa) diagnosis is associated with increased morbidity and mortality. This study aimed to assess the impact of sociodemographic characteristics on distant metastasis at the time of PCa diagnosis. Methods: The Surveillance Epidemiology and End Results (SEER) database (2000-2021), including PCa patients from 17 registries, was queried. Patients with late-stage diagnosis (LSD) were defined as those who were initially diagnosed with stage IV disease, indicating distant metastasis. Sociodemographic predictors included race, ethnicity, age, marital status, residential status, and median household income. Multicollinearity was assessed using variance inflation factors (VIF). Univariable logistic regression was performed to identify significant predictors of LSD, which were further analyzed in multivariable logistic regression using a maximum likelihood estimation approach. Odds ratios with 95% confidence intervals (CI) were computed with a p-value of <0.05 indicating a statistically significant association. Results: Of the 1,089,736 PCa patients diagnosed, 64,983 (6%) had an LSD. The majority were white (80%), non-Hispanic/Latino (HL) (91%), aged ≥65 years (60%), married (75%), residents in metropolitan areas (88%), and had an annual house income of ≥$70,000 (70%). Multivariable logistic regression identified Black (OR: 1.24; 95%CI: 1.22-1.27), Asian or Pacific Islander (1.35; 1.30-1.40), and American Indian/Alaska Native (1.70; 1.53-1.89) to have higher odds of LSD compared to White patients (Table). In addition, HL ethnicity compared to non-HL (OR: 1.45; 95% CI: 1.42-1.49), age ≥65 compared to <65 (1.68; 1.65-1.71), unmarried status compared to married (1.88; 1.85-1.91), residency in non-metropolitan areas compared to metropolitan (1.10; 1.07-1.12), and household income <$70,000 compared to ≥$70,000 (1.03; 1.01-1.05) had a significant association with LSD. Conclusions: Older, non-White (Black, Asian/Pacific Islander, American Indian/Alaska Native), Hispanic/Latino, unmarried men, residents of non-metropolitan areas, or those with lower annual household incomes have higher odds of late-stage diagnosis. Interventions targeting early detection in these at-risk groups may improve prostate cancer outcomes in the US. Variable Odds Ratio (95% CI) p-value Race (Black vs White) 1.24 (1.22-1.27) <0.01 Race (Asian or Pacific Islander vs White) 1.35 (1.31-1.40) <0.01 Race (American Indian/Alaska Native vs White) 1.70 (1.53-1.89) <0.01 Ethnicity (Hispanic/Latino vs non-/Hispanic/Latino) 1.45 (1.42-1.49) <0.01 Age (≥65 vs <65) 1.68 (1.65-1.71) <0.01 Marital status (unmarried vs married) 1.88 (1.85-1.91) <0.01 Residence status (non-metropolitan vs metropolitan) 1.10 (1.07-1.12) <0.01 Household income (<$70,000 vs ≥$70,000) 1.03 (1.01-1.05) <0.01

Abstract Objective Data extraction from the published literature is the most laborious step in conducting living systematic reviews (LSRs). We aim to build a generalizable, automated data extraction workflow leveraging large language models (LLMs) that mimics the real-world 2-reviewer process. Materials and Methods A dataset of 10 trials (22 publications) from a published LSR was used, focusing on 23 variables related to trial, population, and outcomes data. The dataset was split into prompt development (n = 5) and held-out test sets (n = 17). GPT-4-turbo and Claude-3-Opus were used for data extraction. Responses from the 2 LLMs were considered concordant if they were the same for a given variable. The discordant responses from each LLM were provided to the other LLM for cross-critique. Accuracy, ie, the total number of correct responses divided by the total number of responses, was computed to assess performance. Results In the prompt development set, 110 (96%) responses were concordant, achieving an accuracy of 0.99 against the gold standard. In the test set, 342 (87%) responses were concordant. The accuracy of the concordant responses was 0.94. The accuracy of the discordant responses was 0.41 for GPT-4-turbo and 0.50 for Claude-3-Opus. Of the 49 discordant responses, 25 (51%) became concordant after cross-critique, increasing accuracy to 0.76. Discussion Concordant responses by the LLMs are likely to be accurate. In instances of discordant responses, cross-critique can further increase the accuracy. Conclusion Large language models, when simulated in a collaborative, 2-reviewer workflow, can extract data with reasonable performance, enabling truly “living” systematic reviews.

Purpose of review This review underscores the critical role and challenges associated with the widespread adoption of artificial intelligence in cancer care to enhance disease management, streamline clinical processes, optimize data retrieval of health information, and generate and synthesize evidence. Recent findings Advancements in artificial intelligence models and the development of digital biomarkers and diagnostics are applicable across the cancer continuum from early detection to survivorship care. Additionally, generative artificial intelligence has promised to streamline clinical documentation and patient communications, generate structured data for clinical trial matching, automate cancer registries, and facilitate advanced clinical decision support. Widespread adoption of artificial intelligence has been slow because of concerns about data diversity and data shift, model reliability and algorithm bias, legal oversight, and high information technology and infrastructure costs. Summary Artificial intelligence models have significant potential to transform cancer care. Efforts are underway to deploy artificial intelligence models in the cancer practice, evaluate their clinical impact, and enhance their fairness and explainability. Standardized guidelines for the ethical integration of artificial intelligence models in cancer care pathways and clinical operations are needed. Clear governance and oversight will be necessary to gain trust in artificial intelligence-assisted cancer care by clinicians, scientists, and patients.