Barker, Kenneth

e16502 Background: Accurate postoperative risk stratification in localized clear-cell renal cell carcinoma (ccRCC) informs prognosis, surveillance intensity, and eligibility for adjuvant systemic therapy. Although validated prognostic models (Leibovich 2018, SSIGN, and UISS) exist, they are inconsistently applied in routine practice due to workflow and documentation barriers. Using AI assisted tools within the electronic medical record (EMR) may enable rapid, standardized risk assessment and improve clinical use. Methods: We retrospectively analyzed 92 patients with localized ccRCC undergoing radical nephrectomy and postoperative oncology evaluation (December 2021–September 2025) across three Mayo Clinic sites. Pathology reports and oncology notes were extracted via structured SQL queries. Clinical and pathologic variables were parsed using a zero-shot GPT-4o pipeline. Gold-standard prognostic risk classification and adjuvant therapy eligibility were established by an expert oncology clinician using validated prognostic calculators and KEYNOTE-564 eligibility criteria. AI-generated outputs and novice manual abstraction by two first-year fellows were compared against expert assessment using accuracy, precision, recall, and F1 score. Leibovich and SSIGN were evaluated as three-class models (low/intermediate/high), and UISS as binary risk stratification (high vs non-high). Results: Among 92 patients with localized ccRCC (median age 64 years [range 41–78]), AI-generated outputs demonstrated near-perfect agreement with expert assessment for KEYNOTE-564 adjuvant immunotherapy eligibility (accuracy 97%, precision 100%, recall 97%, F1 score 98%). For three-class prognostic stratification, AI achieved perfect agreement for Leibovich disease-free survival risk (accuracy, precision, recall, and F1 all 100%), outperforming novice abstraction. For SSIGN, AI demonstrated strong multi-class performance (accuracy 91%, precision 91%, recall 93%, F1 score 92%), compared with novice abstraction (accuracy 92%, F1 score 90%). For binary UISS risk stratification, AI achieved 81% accuracy, precision 74%, recall 100%, and F1 score 85% showing a tendency towards false positives. Conclusions: This study demonstrates AI can use existing prognostic and systemic therapy eligibility to accurately risk stratify and identify patients eligible for adjuvant systemic immunotherapy post nephrectomy. For our next steps, we plan to develop a multimodal AI platform integrating pathologic, radiologic, and clinical data to improve prognostication for outcomes and predictive capabilities for patients who would benefit from adjuvant systemic therapy for more personalized medicine.

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)

483 Background: Sarcomatoid dedifferentiation (sRCC) in metastatic clear-cell renal cell carcinoma (mccRCC) confers aggressive disease and poor prognosis. We maintain a living meta-analysis of first-line immune checkpoint inhibitor (ICI)–based combinations in mccRCC (Riaz et al., Eur Urol Oncol 2021), which demonstrated improved survival with ICI-based regimens over sunitinib. Here, we present an updated synthesis focused on the comparative efficacy of ICI combinations in the sarcomatoid subset. Methods: A systematic search of MEDLINE, EMBASE, and CENTRAL through October 2025 was performed as part of our living meta-analysis of first-line mccRCC therapy. Eligible phase III RCTs compared ICI combinations (IO/IO or IO/TKI) versus sunitinib, including CheckMate 214, CheckMate 9ER, KEYNOTE-426, CLEAR/KEYNOTE-581, JAVELIN Renal 101, and IMmotion151. Extracted outcomes included overall (OS) and progression-free survival (PFS), and objective (ORR), complete (CR), and partial (PR) response rates. Random-effects meta-analyses generated pooled hazard ratios (HRs) for time-to-event outcomes and risk ratios (RRs) for response rates with 95% confidence intervals. Subgroup interaction analyses compared sarcomatoid versus non-sarcomatoid subgroups, with p < 0.1 considered significant. Results: Six phase III RCTs (n = 5,121; 624 sarcomatoid) were included. Across all ICI-based combinations, ICI therapy improved outcomes versus sunitinib in both sarcomatoid and non-sarcomatoid disease. In the pooled analysis, the OS interaction favored sRCC meeting the prespecified significance threshold for subgroup analyses (HR 0.53 vs 0.68; p-interaction = 0.06 < 0.10), while no significant interaction was seen for PFS. Response outcomes showed the strongest differential benefit, particularly with IO/TKI regimens. IO/IO results were directionally consistent but limited by smaller non-sarcomatoid samples. Conclusions: ICI-based combinations significantly improve survival and response outcomes versus sunitinib in mccRCC. Using a prespecified α = 0.10 for interaction testing, the pooled analysis showed a significant OS interaction and higher response rates in sRCC, indicating enhanced ICI sensitivity. These updated meta-analysis data support ICI regimens as the preferred first-line therapy for mRCC with sarcomatoid de-differentiation. Endpoint Regimen Subgroup (S = sarcomatoid; NS = non-sarcomatoid) Effect (95% CI) p-interaction PFS IO/IO S 0.51 (0.40–0.65) – IO/TKI S vs NS 0.48 (0.36–0.64) vs 0.46 (0.39–0.52) 0.81 OS IO/IO S 0.54 (0.07–4.42) – IO/TKI S vs NS 0.51 (0.17–1.48) vs 0.68 (0.30–1.58) 0.24 ORR IO/IO S 2.93 (0.49–17.37) – IO/TKI S vs NS 2.43 (1.84–3.21) vs 1.95 (1.67–2.28) 0.03 CR IO/IO S 3.81 (0.82–17.71) – IO/TKI S vs NS 5.93 (4.73–7.42) vs 2.85 (0.10–83.73) 0.007 PR IO/IO S 3.48 (1.70–7.11) – IO/TKI S vs NS 2.10 (1.78–2.47) vs 1.75 (1.18–2.61) <0.001