Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning
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 approxima...
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MDPI AG
2025-06-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/11/6/191 |
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| author | Yixing Wu Alexander Shieh Steven Cen Darryl Hwang Xiaomeng Lei S. J. Pawan Manju Aron Inderbir Gill William D. Wallace C.-C. Jay Kuo Vinay Duddalwar |
| author_facet | Yixing Wu Alexander Shieh Steven Cen Darryl Hwang Xiaomeng Lei S. J. Pawan Manju Aron Inderbir Gill William D. Wallace C.-C. Jay Kuo Vinay Duddalwar |
| author_sort | Yixing Wu |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9e401fa311ca4c8890fac7ac30ae3abe |
| institution | OA Journals |
| issn | 2313-433X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-9e401fa311ca4c8890fac7ac30ae3abe2025-08-20T02:20:58ZengMDPI AGJournal of Imaging2313-433X2025-06-0111619110.3390/jimaging11060191Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green LearningYixing Wu0Alexander Shieh1Steven Cen2Darryl Hwang3Xiaomeng Lei4S. J. Pawan5Manju Aron6Inderbir Gill7William D. Wallace8C.-C. Jay Kuo9Vinay Duddalwar10Media Communications Lab, University of Southern California, Los Angeles, CA 90089, USARadiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USARadiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USARadiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USARadiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USARadiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USADepartment of Pathology, University of Southern California, Los Angeles, CA 90033, USAInstitute of Urology, University of Southern California, Los Angeles, CA 90033, USADepartment of Pathology, University of Southern California, Los Angeles, CA 90033, USAMedia Communications Lab, University of Southern California, Los Angeles, CA 90089, USARadiomics Lab, Department of Radiology, University of Southern California, Los Angeles, CA 90033, USAClear 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.https://www.mdpi.com/2313-433X/11/6/191radiomicsclear cell renal cell carcinomaPD-L1CD68Green Learningfeature selection |
| spellingShingle | Yixing Wu Alexander Shieh Steven Cen Darryl Hwang Xiaomeng Lei S. J. Pawan Manju Aron Inderbir Gill William D. Wallace C.-C. Jay Kuo Vinay Duddalwar Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning Journal of Imaging radiomics clear cell renal cell carcinoma PD-L1 CD68 Green Learning feature selection |
| title | Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning |
| title_full | Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning |
| title_fullStr | Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning |
| title_full_unstemmed | Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning |
| title_short | Prediction of PD-L1 and CD68 in Clear Cell Renal Cell Carcinoma with Green Learning |
| title_sort | prediction of pd l1 and cd68 in clear cell renal cell carcinoma with green learning |
| topic | radiomics clear cell renal cell carcinoma PD-L1 CD68 Green Learning feature selection |
| url | https://www.mdpi.com/2313-433X/11/6/191 |
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