Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer. Programmed Death-Ligand 1 (PD-L1) is an important prognostic marker for ccRCC and CT radiomics can be a non-invasive modality to predict PD-L1.


In a previous study by Shieh et al. [1], the research team explored using radiomic features from computer tomography (CT) imaging to predict the TIME measurements via mIHC analysis. Tumor specimens were categorized as positive or negative, guided by varying thresholds of PD-L1 expression of the tumor epithelium. A total of 1,708 radiomic features were extracted from CT images using a custom-built radiomics pipeline. To predict tumor binary classification based on these radiomic features.


Building upon this work [1], our study explored a novel approach known as Green Learning (GL).

As shown in Fig. 1, our study employs a modularized solution that includes the following modules: the DFT module for feature dimension reduction and the LNT module for new feature generation. Fig [2] presents the sorted DFT loss curve. The x-axis represents the sorted dimension index, while the y-axis represents the corresponding DFT loss value. The colored points scattered on the curve denote the DFT loss of the top 10 variables of importance (VOI), which are identified from the prior study [1]. All these top 10 VOIs possess relatively low DFT loss. The DFT loss value of LNT feature is the lowest among the raw features.


We observed a significant improvement in the radiomics prediction performance of tumor epithelium PD-L1 > 1%, >5% and >10%. Compared to prior research, the AUROC values improved from 0.61 to 0.76, 0.75 to 0.85 and 0.85 to 0.88, respectively.


[1] A. T.-W. Shieh, S. Y. Cen, B. Varghese, D. H. Hwang, X. Lei, K. Gurumurthy, I. Siddiqi, M. Aron, I. Gill, W. D. Wallace et al., “Bridging radiomics to tumor immune microenvironment assessment in clear cell renal cell carcinoma,” in 18th International Symposium on Medical Information Processing and Analysis, vol. 12567. SPIE, 2023, pp. 8–17