{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:22:58Z","timestamp":1760059378871,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wright Foundation"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/jimaging11060191","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T06:48:56Z","timestamp":1749538136000},"page":"191","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-3904-8710","authenticated-orcid":false,"given":"Yixing","family":"Wu","sequence":"first","affiliation":[{"name":"Media Communications Lab, University of Southern California, Los Angeles, CA 90089, USA"}]},{"given":"Alexander","family":"Shieh","sequence":"additional","affiliation":[{"name":"Radiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"given":"Steven","family":"Cen","sequence":"additional","affiliation":[{"name":"Radiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"given":"Darryl","family":"Hwang","sequence":"additional","affiliation":[{"name":"Radiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8899-5177","authenticated-orcid":false,"given":"Xiaomeng","family":"Lei","sequence":"additional","affiliation":[{"name":"Radiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"given":"S. J.","family":"Pawan","sequence":"additional","affiliation":[{"name":"Radiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1577-3586","authenticated-orcid":false,"given":"Manju","family":"Aron","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Southern California, Los Angeles, CA 90033, USA"},{"name":"Institute of Urology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"given":"Inderbir","family":"Gill","sequence":"additional","affiliation":[{"name":"Institute of Urology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"given":"William D.","family":"Wallace","sequence":"additional","affiliation":[{"name":"Department of Pathology, University of Southern California, Los Angeles, CA 90033, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9474-5035","authenticated-orcid":false,"given":"C.-C. Jay","family":"Kuo","sequence":"additional","affiliation":[{"name":"Media Communications Lab, University of Southern California, Los Angeles, CA 90089, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4808-5715","authenticated-orcid":false,"given":"Vinay","family":"Duddalwar","sequence":"additional","affiliation":[{"name":"Radiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USA"},{"name":"Institute of Urology, University of Southern California, Los Angeles, CA 90033, USA"},{"name":"Alfred E Mann Department of Biomedical Engineering, USC Viterbi School of Engineering, Los Angeles, CA 90089, USA"},{"name":"Department of Radiology, Los Angeles General Medical Center, Los Angeles, CA 90033, USA"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Simonaggio, A., Epaillard, N., Pobel, C., Moreira, M., Oudard, S., and Vano, Y.A. (2021). 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