Aggressiveness classification of clear cell renal cell carcinoma using registration-independent radiology-pathology correlation learning.

ABSTRACT:

Background

Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Clear cell RCC (ccRCC) is the most common RCC subtype, with both aggressive and indolent manifestations. Indolent ccRCC is often low-grade without necrosis and can be monitored without treatment. Aggressive ccRCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most RCCs are detected on computed tomography (CT) scans, aggressiveness classification is based on pathology images acquired from invasive biopsy or surgery.

Purpose

CT imaging-based aggressiveness classification would be an important clinical advance, as it would facilitate non-invasive risk stratification and treatment planning. Here, we present a novel machine learning method

SUBMITTER: Bhattacharya I 

PROVIDER: S-EPMC11700780 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature