Correlated Feature Aggregation by Region Helps Distinguish Aggressive from Indolent Clear Cell Renal Cell Carcinoma Subtypes on CT

TL;DR

This paper introduces CorrFABR, a novel machine learning method that correlates radiology and pathology features to improve the classification of aggressive versus indolent clear cell renal cell carcinoma on CT scans.

Contribution

The study presents a new automated approach, CorrFABR, that leverages correlations between radiology and pathology features for better RCC aggressiveness classification on CT images.

Findings

CorrFABR increased F1-score from 0.68 to 0.73.

CorrFABR outperformed radiology-only features.

Method enables classification using CT alone during inference.

Abstract

Renal cell carcinoma (RCC) is a common cancer that varies in clinical behavior. Indolent RCC is often low-grade without necrosis and can be monitored without treatment. Aggressive RCC is often high-grade and can cause metastasis and death if not promptly detected and treated. While most kidney cancers are detected on CT scans, grading is based on histology from invasive biopsy or surgery. Determining aggressiveness on CT images is clinically important as it facilitates risk stratification and treatment planning. This study aims to use machine learning methods to identify radiology features that correlate with features on pathology to facilitate assessment of cancer aggressiveness on CT images instead of histology. This paper presents a novel automated method, Correlated Feature Aggregation By Region (CorrFABR), for classifying aggressiveness of clear cell RCC by leveraging correlations between radiology and corresponding unaligned pathology images. CorrFABR consists of three main steps: (1) Feature Aggregation where region-level features are extracted from radiology and pathology images, (2) Fusion where radiology features correlated with pathology features are learned on a region level, and (3) Prediction where the learned correlated features are used to distinguish aggressive from indolent clear cell RCC using CT alone as input. Thus, during training, CorrFABR learns from both radiology and pathology images, but during inference, CorrFABR will distinguish aggressive from indolent clear cell RCC using CT alone, in the absence of pathology images. CorrFABR improved classification performance over radiology features alone, with an increase in binary classification F1-score from 0.68 (0.04) to 0.73 (0.03). This demonstrates the potential of incorporating pathology disease characteristics for improved classification of aggressiveness of clear cell RCC on CT images.