Radiologic, Pathologic, and Deep Learning Predictors of Response to Immune Checkpoint Blockade in Renal Cell Carcinoma Patients Undergoing Post-Treatment Nephrectomy.

ABSTRACT:

Background

Response assessment of primary kidney tumors in the consolidation cytoreductive and neoadjuvant settings offers a unique opportunity to inform postoperative adaptive treatment strategies. Yet, systematic analyses evaluating radiology, pathology, and machine learning are lacking.

Methods

We retrospectively identified consecutive renal cell carcinoma (RCC) patients with locoregionally advanced or metastatic RCC who received at least one cycle of ICI-containing doublet therapy prior to nephrectomy at the UTSW Kidney Cancer Program (2017-2024). Radiologic and pathologic features were centrally reviewed and correlated with clinical outcomes: freedom from start of next systemic therapy (FFNT) in cytoreductive patients and metastasis-free survival (MFS) in neoadjuvant pa

SUBMITTER: Kapur P 

PROVIDER: S-EPMC12668094 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature