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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.

703 Background: The association of circulating tumor DNA (ctDNA) reduction with treatment response has been investigated across multiple malignancies. This study aims to assess the impact of ctDNA response on survival outcomes in patients receiving enfortumab-pembrolizumab (EV-P). Methods: We identified 32 patients with advanced urothelial carcinoma who received EV-P and had ≥2 ctDNA assessments with ≥1 detectable level. Deep molecular response was defined as >2-log reduction from highest ctDNA detection. Radiographic progression was determined from radiology reports documenting progression by RECIST criteria. Kaplan Meier analyses were performed to compute Hazard Ratios (HR) with 95% confidence intervals (CI) for radiographic progression free survival (rPFS), defined as time from treatment start to radiographic progression or death and overall survival (OS) defined as time from treatment start to death were computed. Log-rank tests were performed to compare PFS and OS in patients with deep molecular response to those without response. A p-value <0.05 indicated a statistically significant association with response. Results: Thirty-two patients were included (median age 71 years; range 39–90). Median follow-up was 10.6 months. At cutoff (10/1/2025), 26 patients were alive and 6 had died. Seventeen (53%) achieved a ≥2-log ctDNA reduction (responders), while 15 (47%) did not (non-responders). Progression-free survival was significantly longer in responders (log-rank P < 0.001); median PFS was 5.9 months (95% CI 2.4–NR) in non-responders and not reached in responders (HR 0.09, 95% CI 0.02–0.44; P = 0.0026). Median overall survival was 12.7 months (95% CI 10.0–NR) in non-responders versus not reached in responders (log-rank P = 0.011; HR 0.11, 95% CI 0.01–0.88; P = 0.037). At cutoff, 94% of responders and 67% of non-responders were alive. Conclusions: Achieving a >2-log decrease in ctDNA during EV + Pembrolizumab therapy was significantly associated with improved progression free and overall survival. These findings support ctDNA kinetics as a potential early biomarker of response warranting prospective validation. Clinical outcomes by >2-log ctDNA reduction status. Endpoint > 2-log drop (n = 17 ≤ 2-log drop (n = 15) Log-rank p Median (mo) ≤ 2-log > 2-log HR (95% CI) P C-index PFS 2 events (12%) 10 events (67%) 0.0002 5.9 (2.4–NA) NR 0.09 (0.02–0.44) 0.0026 0.77 OS 1 event (6%) 6 events (40%) 0.011 12.7 (10.0–NA) NR 0.11 (0.01–0.88) 0.037 0.73

127 Background: Bone metastases affect up to 90 % of patients with metastatic hormone-resistant prostate cancer (mHRPC) and cause substantial morbidity from skeletal-related events (SREs) such as fracture, spinal-cord compression, and palliative interventions. Bone-modifying agents (BMAs) including bisphosphonates and denosumab reduce SRE risk and are NCCN-recommended, however, real-world use remains inconsistent. Methods: Patients with mHRPC treated from January 2017-September 2024 across Mayo Clinic sites were identified using SQL-based EHR queries confirmed by manual chart review. Bone Metastases were detected through keyword searchers of radiology reports and confirmed through manual chart review. SRE-prevention dosing was defined as zoledronic acid 4mg IV or denosumab 120mg SC; osteoporosis dosing was excluded. SREs were identified using diagnosis codes and text-pattern searches for radiotherapy, surgery, fracture, and spinal-cord compression. Descriptive statistics summarized BMA utilization and SRE incidence. Categorical variables were compared using Chi-square tests, and a risk ratio (RR) with 95% confidence intervals was calculated to evaluate association between early BMA initiation and SRE risk. Results: Among 778 patients with mHRPC, 685 (88 %) had bone metastases (mean age 70.5 years). Of these, 169 (24.7 %) had an SRE, and 261 (38.1 %) received BMAs at SRE-prevention doses (denosumab n = 157 [60 %]; zoledronic acid n = 104 [40 %]). Based on a chi-square test, combined SRE incidence did not differ significantly (No BMA 23 %, denosumab 23 %, zoledronic 33 %; p = 0.069). Bone pain at baseline was more common in patients who later received a BMA (136 [68 %]) than in those who did not (82 [41 %]; p < 0.001). Among 200 randomly sampled non-BMA patients, 83 had no recorded BMA discussion (66 also lacked bone pain), 38 lacked dental clearance, 32 had uncompleted plans and 18 declined therapy. Early BMA initiation (before first bone metastases) was associated with fewer SREs (16 % vs 29 %; RR 0.56, 95 % CI 0.33–0.96). Conclusions: Less than half of bone-metastatic mHRPC patients received BMAs. Non-initiation was often linked to modifiable factors such as delayed dental clearance or lack of treatment discussions, highlighting the need for system-level strategies to improve timely bone-directed care. SREs were common, affecting one quarter of patients. Small sample size precluded meaningful comparison of the impact of BMAs on SRE incidence however early BMA initiation may lower SREs. Incidence of SREs stratified by no BMA vs BMA treatment. SRE No BMA BMA Overall Pathological Fracture 6.4% (27/424) 4.9% (13/261) 5.8% (40/685) Radiotherapy to Bone 13.9% (59/424) 18.8% (49/261) 15.7% (108/685) Spinal Cord Compression 0.7% (3/424) 3.1% (8/261) 1.6% (11/685) Surgery to Bone 2.1% (9/424) 0.3% (1/261) 1.5% (10/685) Combined 23% (98/424) 27.2% (71/261) 24.7% (169/685)

Abstract Introduction Persistent symptom burden in classic myeloproliferative neoplasms (MPNs) i.e., polycythemia vera (PV), essential thrombocythemia (ET) and myelofibrosis (MF) is linked to shorter survival, more thromboembolic events and impaired quality of life. Identifying demographic, clinical, laboratory, and treatment-related predictors of symptom improvement can inform risk-adapted care and guide supportive interventions. Methods Patients with PV, ET, and MF were identified through a retrospective chart review. Total symptom scores (TSS) and scores of individual symptoms were recorded at the time of first TSS assessment. Demographic (age, sex, race, ethnicity), seasonal (summer: April–September; winter: October–March), clinical (time since diagnosis, depression, obesity, spleen size), treatment-related (phlebotomy, hydroxyurea, interferon-alpha, anagrelide, JAK inhibitors, antidepressants) and laboratory (complete blood counts, serum chemistry) factors were captured at the baseline. Symptom improvement was defined as a ≥50% TSS reduction from baseline. Cumulative incidence analyses, accounting for death as a competing risk, were performed to estimate the probability and timing of symptom improvement. Multicollinearity was assessed using variance inflation factors (VIFs). Highly correlated (VIF ≥5) features and features with ≥15% missing observations were excluded. Multivariable Fine-Gray hazards regression was performed to identify predictors of symptom improvement. Sub-distribution hazard ratios (sHR) with 95% confidence intervals (CI) were computed with p-values &lt;0.05, indicating a statistically significant association with symptom improvement. Results A total of 532 patients (PV: 195, ET: 180, MF: 157) were identified between 2019 and 2024. The median age was 65 (IQR: 57-74) for PV, 65 (52-75) for ET, and 69 (63-75) for MF. Most patients were White (PV: 178, ET: 164, MF: 145) and female (PV: 99, ET: 134, MF: 84). Fatigue was the most frequently reported symptom (PV: 161, ET: 155, MF: 136) whereas weight loss was the least reported (PV: 9, ET: 12, MF: 26). Baseline TSS was highest in MF (median: 15; IQR: 7-27) followed by PV (13; 6-25) and ET (13; 5-23). Symptom improvement was observed in 89 (46%) PV, 71 (39%) ET, and 44 (28%) MF patients. Among those who improved, the cumulative incidence of symptom improvement reached 100% by 55 months in PV, 53 months in ET, and 58 months in MF. At 6, 12, and 24 months, cumulative symptom improvement was 22%, 34%, and 57% in PV; 15%, 25%, and 43% in ET; and 19%, 28%, and 39% in MF. Median time to improvement was not reached in any group. Death before symptom improvement was observed in 5 (3%) PV, 6 (3%) ET, and 37 (24%) MF patients. Multivariable regression analyses in PV identified higher baseline TSS (sHR: 1.02; 95%CI: 1.01-1.03) as associated with symptom improvement (p = 0.02). In ET, lower white blood cell count (sHR: 0.89; 95%CI: 0.81-0.98) and less time since diagnosis (0.99; 0.98-0.99) were associated with symptom improvement (p = 0.02). In MF, more months since diagnosis (sHR: 1.01 95%CI: 1.01-1.01) was associated with symptom improvement (p = 0.01). Conclusions Symptom trajectories differed across MPN subtypes. In PV, a higher baseline symptom burden; in ET, a lower white blood cell count and more recent diagnosis and in MF, a longer disease duration were associated with symptom improvement. These readily available parameters may help inform supportive strategies, tailor response expectations, and identify patients for symptom-directed interventions or clinical trials.

Abstract Background Accurate diagnosis and risk stratification in myeloproliferative neoplasms (MPNs) rely on expert interpretation of complex clinical records. Outside major academic centers, specialist expertise is scarce, and time-consuming chart reviews extend clinic visits and delay treatment decisions, underscoring the need for automated, point-of-care phenotyping for MPN patients. Methods Patients with the ICD codes of PV, ET and MF were identified from the electronic health record. Cases were defined as patients meeting the 2017 WHO diagnostic criteria for PV, ET or MF or those with a definitive MPN diagnosis determined by a hematologist. Controls were the patients who met neither the diagnostic criteria nor had a hematologist-assigned diagnosis. Two clinicians extracted demographic, clinical (symptoms, thrombotic history), laboratory (complete blood count, serum chemistry), genetic (mutations, karyotype), bone marrow morphology, diagnostic mentions and prognostic score information from unstructured clinical notes and pathology reports. Prognostic scores included the conventional risk model for PV, the International Prognostic Score of Thrombosis (IPSET) for ET and the Dynamic International Prognostic Scoring System (DIPSS), DIPSS-plus or Mutation-enhanced International Prognostic Scoring System (MIPSS-70) for MF. Discrepancies in data extraction were resolved by a consensus. A random sample of 510 patients (PV: 85, ET: 85, MF: 85, controls: 255) was split into prompt development (n=60) and test (n=450) sets for iterative workflow engineering and evaluation. The workflow included: (i) a rule-based algorithm for automated retrieval of demographic details, the first bone marrow biopsy report, the first clinical note after biopsy and the latest laboratory results within 90 days preceding biopsy (ii) a HIPAA-compliant LLM (GPT-4 turbo) for data extraction from the clinical notes and pathology reports (iii) a regular expressions-based algorithm for post-processing extracted data into structured tables, (iv) a clinician-informed source prioritization algorithm to select data from preferred sources, (v) a modified WHO-informed diagnostic characterization tool incorporating hematologist-assigned diagnoses to distinguish cases from controls and; (vi) an NCCN guidelines-based algorithm for prognostic tool selection and risk-stratification based on available data. Structured prompts were used to extract demographic, clinical, laboratory, genetic and pathological data in a zero-shot deterministic setting. The performance of automated data extraction, diagnostic characterization and prognostication was compared against the manually curated data. Accuracy and F1 scores were computed for data extraction and prognostication tasks while sensitivity and specificity were calculated to assess diagnostic performance. Results A total of 450 (PV: 150, ET: 150, MF: 150) pathology reports and 50 (PV: 52, ET: 55; MF: 65) clinical notes were identified for 450 test set patients. The test set data extraction accuracy was 95% (reports: 97%, notes: 92%) for PV, 93% (reports: 92%, notes: 94%) for ET and 98% (reports: 99%, notes 97%) for MF. The test set F1 score was 0.95 (reports: 0.96, notes: 0.93) for PV, 0.95 (reports: 0.95, notes: 0.96) for ET and 0.98 (reports: 0.99, notes: 0.97) for MF. PV patients were diagnosed with 100% sensitivity and 98% specificity whereas ET and MF patients were diagnosed with 100% sensitivity and specificity. The accuracy of risk stratification was 100% for PV and ET and 94% for MF. The F1 score for risk stratification was 1.00 for PV and ET and 0.94 for MF. Conclusions The first-in-class automated framework successfully identified pertinent clinical information, performed accurate data extraction from unstructured records and enabled clinician-guided source prioritization for accurate diagnosis and risk stratification of MPN patients. Prospective validation of the framework is required for widespread clinical implementation.