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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.

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

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.

Abstract Background: Chronic Lymphocytic Leukemia (CLL) is the most prevalent leukemia among adults in Western populations, disproportionately affecting the elderly, with a median age at diagnosis of approximately 70 years. Over the past two decades, significant advancements in therapeutic options, including Bruton tyrosine kinase (BTK) inhibitors, BCL-2 antagonists, and monoclonal antibodies, have improved clinical outcomes. However, despite these advances, limited data exist on long-term mortality trends and disparities in CLL outcomes among older adults in the United States. Understanding such patterns is critical to guiding geriatric oncology care, identifying gaps in healthcare delivery, and informing public health strategies. Methods: We conducted a population-based analysis using the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research (CDC WONDER) Underlying Cause-of-Death database. We identified all deaths among individuals aged ≥65 years from 1999 to 2020 in which CLL (ICD-10 code C91.1) was listed as the underlying cause. Mortality data were stratified by sex, age group (65–74, 75–84, ≥85), U.S. Census region, urban-rural classification, state, and place of death. We calculated crude mortality rates (CMRs) and age-adjusted mortality rates (AAMRs) per 100,000 persons, standardized to the U.S. 2000 population. Joinpoint regression analysis was used to assess temporal trends in AAMRs and compute annual percent changes (APCs) and average annual percent changes (AAPCs), with statistical significance set at p&lt;0.05. Results: Between 1999 and 2020, a total of 85,371 CLL-related deaths occurred among U.S. adults aged ≥65 years. The overall AAMR declined from 11.18 per 100,000 in 1999 to 7.75 in 2020, reflecting a significant downward trend (AAPC: -1.86%; 95% CI: -2.18 to -1.54; p&lt;0.0001). Men consistently exhibited higher mortality rates (AAMR: 13.48) than women (AAMR: 6.45), though the mortality decline was more pronounced among women (AAPC: -2.10% vs. -1.65%, respectively; both p&lt;0.0001). Regional disparities were evident, with the Midwest reporting the highest AAMR (10.63), and the West the lowest (8.71). Urbanization analysis revealed higher mortality in non-metropolitan areas (AAMR: 10.33) than metropolitan ones (AAMR: 9.08), though both experienced significant declines over time. Age-stratified analysis showed the steepest declines in the 65–74 age group (AAPC: -3.91%; p&lt;0.0001), followed by those aged 75–84 years (AAPC: -1.96%) and ≥85 years (AAPC: -0.25%). States with the highest mortality included Minnesota, Iowa, and South Dakota, while Hawaii, Nevada, and New Mexico had the lowest. Most deaths occurred in medical facilities (43.8%), followed by home (26.7%), nursing homes/long-term care (19.5%), and hospice (5.6%). Conclusion: CLL-related mortality among the U.S. geriatric population declined significantly between 1999 and 2020, likely reflecting the impact of therapeutic innovations and improved disease management. However, substantial disparities persist across sex, age, region, urbanization, and state-level subgroups. These findings underscore the importance of equitable access to modern CLL treatments and the need for targeted interventions to reduce mortality in high-risk, underserved older populations. Efforts to close these gaps will be essential in optimizing outcomes in the era of personalized cancer care for aging Americans.

Introduction and importance: Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin lymphoma, often presenting with nodal or extranodal masses. However, involvement of the chin region is extremely rare and underreported in literature. This case highlights an unusual extranodal manifestation of germinal center B-cell-like (GCB) DLBCL. Case presentation: A 56-year-old man with a history of hypertension and type II diabetes presented with an 8-month history of a painless, progressively enlarging chin mass. Prior antibiotic therapy failed to resolve the lesion. Examination revealed a firm, 4 × 3 cm subcutaneous swelling involving skin and mucosa. Computed tomography (CT) and positron emission tomography/CT imaging revealed hypermetabolic soft tissue lesions in the chin and right buccogingival region. Histopathology and immunohistochemistry confirmed GCB-type DLBCL. The patient received six cycles of rituximab, cyclophosphamide, pirarubicin, vincristine, and prednisolone chemotherapy, resulting in a significant reduction in lesion size and metabolic activity. Follow-up showed no recurrence. Clinical discussion: This case highlights the diagnostic challenges posed by extranodal DLBCL in atypical locations. Misdiagnosis as an infection led to a delayed diagnosis. Immunophenotyping revealed expression of both B- and T-cell markers, which complicated the classification of the lymphoma. A prior history of spontaneously resolving axillary masses suggested an indolent precursor phase that later transformed into aggressive DLBCL. Conclusion: DLBCL may present in rare extranodal sites such as the chin, mimicking benign conditions. Persistent lesions unresponsive to antibiotics should prompt a malignancy workup. Comprehensive evaluation, including imaging and immunohistochemistry, is critical for timely diagnosis and effective treatment. Further research is needed to understand unusual immunophenotypes and progression pathways in DLBCL.