Emamekhoo, Hamid

TPS577 Background: Pembrolizumab (pembro) for one year following resection of localized high risk clear cell renal cell carcinoma (ccRCC) in a phase 3 trial resulted in improvement in relapse-free survival and overall survival (Choueiri, NEJM 2024). However, 20% of patients still experienced a relapse within 2 years with no biomarker yet identified to predict who will respond to or needs adjuvant therapy. Addition of a tyrosine kinase inhibitor (TKI) to immunotherapy (IO) is a standard of care in metastatic ccRCC, but to date no studies have addressed the addition of a TKI to IO in the adjuvant setting. Subsequently, phase 3 STRIKE! was developed to explore the benefit of 6 months of tivozanib (tivo) added to pembro vs pembro alone in the adjuvant setting for resected high risk ccRCC. Methods: Eligible patients have an ECOG performance status ≤2 and a histologically confirmed diagnosis of RCC with clear cell component with or without sarcomatoid features after complete resection of the primary tumor, with pathology revealing pT2 grade 4 disease or any grade ≥ T3 or TxN1. Patients are also eligible if they developed metastasis within a year after resection and then underwent resection, definitive radiation or ablation of solid, isolated, soft tissue metastases (excluding brain and bone lesions) with no evidence of active disease (M1NED). Patients will be randomized 1:1 to intravenous pembro for 48 weeks with or without the addition of tivo 1.34 mg by mouth daily D1-21 q28D for 6 months stratified by stage (T2T3 vs T4/N1 vs M1NED) . With no limit on dose holds or interruptions of tivo, dose reductions of tivo to 0.89 mg D1-21 q28D or tivo 0.89 mg every other day are allowed. No dose reductions of pembro are permitted. Following baseline imaging to confirm no disease, imaging is performed every 12 weeks for first year, every 16 weeks for second year, every 24 weeks for third year and then annually until 5 years after registration or progression. The primary endpoint will be disease-free survival (DFS) as assessed by investigator, with secondary endpoints being overall survival, safety and tolerability. The study will enroll 1040 patients to detect a minimum detectable hazard ratio of 0.67 (24-month DFS 84% in experimental arm) with 90% power. Quality of life analysis will compare global quality of life and fatigue between the two arms. Imaging and specimens will be banked for future research. The study opened to accrual in April 2025 and at this time 106 patients have enrolled. Clinical trial information: NCT06661720 .

Abstract The adoption of electronic health records (EHRs) has transformed health care, improving efficiency and chart accessibility. However, the widespread reliance on unstructured data entry and the lack of standardized documentation frameworks have resulted in significant data fragmentation across health care systems. The prevalence of unstructured data in EHRs limits their potential for clinical decision support, trial matching, real‐world evidence (RWE) generation, and quality measurement. Data fragmentation in health care triggers a cascade of challenges that ultimately compromise patient care. Clinicians face an excessive documentation burden and struggle to locate critical information buried in unstructured notes. Researchers encounter difficulties in extracting reliable clinical data. EHR vendors grapple with standardizing unstructured information for interoperability, and payers are unable to process unstructured clinical data efficiently to support value‐based care models. These challenges are particularly acute in oncology, where complex clinical elements like cancer staging, disease status, and treatment changes require precise, structured documentation. Emerging artificial intelligence (AI) technologies, such as large language models (LLMs) and ambient listening, offer a path to automate structured data generation while reducing the workload on providers. Here, the authors propose LLM‐based workflows that balance automation with clinician verification, streamlining data entry without compromising accuracy. Realizing these benefits requires coordinated efforts among clinicians, researchers, EHR vendors, payers, and policymakers to align regulatory frameworks with AI‐driven innovations. This article outlines a strategy to enhance structured data capture within EHRs, ultimately improving patient care, research, and health care efficiency.

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 1 and Appendix 2 ). Updates are published regularly and can be found at www.asco.org/genitourinary-cancer-guidelines .