# Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning

## TL;DR

This paper introduces a Green Learning framework to predict PD-L1 and CD68 biomarkers in kidney cancer using CT scans, improving accuracy and efficiency.

## Contribution

The novel Green Learning framework improves biomarker prediction from CT scans with enhanced accuracy and interpretability.

## Key findings

- The GL model achieved an MSE of 0.0041 and MAE of 0.0346 for PD-L1 expression prediction in 52 ccRCC patients.
- For TAM population prediction, the model reached an AUROC of 0.85, surpassing the previous benchmark of 0.81.
- The framework uses radiomic features processed through DFT, RFT, and LNT for robust biomarker approximation.

## Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer. Extensive efforts have been made to utilize radiomics from computed tomography (CT) imaging to predict tumor immune microenvironment (TIME) measurements. This study proposes a Green Learning (GL) framework for approximating tissue-based biomarkers from CT scans, focusing on the PD-L1 expression and CD68 tumor-associated macrophages (TAMs) in ccRCC. Our approach includes radiomic feature extraction, redundancy removal, and supervised feature selection through a discriminant feature test (DFT), a relevant feature test (RFT), and least-squares normal transform (LNT) for robust feature generation. For the PD-L1 expression in 52 ccRCC patients, treated as a regression problem, our GL model achieved a 5-fold cross-validated mean squared error (MSE) of 0.0041 and a Mean Absolute Error (MAE) of 0.0346. For the TAM population (CD68+/PanCK+), analyzed in 78 ccRCC patients as a binary classification task (at a 0.4 threshold), the model reached a 10-fold cross-validated Area Under the Receiver Operating Characteristic (AUROC) of 0.85 (95% CI [0.76, 0.93]) using 10 LNT-derived features, improving upon the previous benchmark of 0.81. This study demonstrates the potential of GL in radiomic analyses, offering a scalable, efficient, and interpretable framework for the non-invasive approximation of key biomarkers.