Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images
Abstract Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims to develop a deep learning radiomics (DLR)...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-91575-y |
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| author | Jiameng Lu Xinyi Liu Xiaoqing Ji Yunxiu Jiang Anli Zuo Zihan Guo Shuran Yang Haiying Peng Fei Sun Degan Lu |
| author_facet | Jiameng Lu Xinyi Liu Xiaoqing Ji Yunxiu Jiang Anli Zuo Zihan Guo Shuran Yang Haiying Peng Fei Sun Degan Lu |
| author_sort | Jiameng Lu |
| collection | DOAJ |
| description | Abstract Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims to develop a deep learning radiomics (DLR)-based approach for predicting programmed death-ligand 1 (PD-L1) expression in patients with non-small cell lung cancer (NSCLC). Data from 352 NSCLC patients with known PD-L1 expression were collected, of which 48.29% (170/352) were tested positive for PD-L1 expression. Tumor regions of interest (ROI) were semi-automatically segmented based on CT images, and DL features were extracted using Residual Network 50. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and dimensionality reduction. Seven algorithms were used to build models, and the most optimal ones were identified. A combined model integrating DLR with clinical data was also developed. The predictive performance of each model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve analysis. The DLR model, based on CT images, demonstrated an AUC of 0.85 (95% confidence interval (CI), 0.82–0.88), sensitivity of 0.80 (0.74–0.85), and specificity of 0.73 (0.70–0.77) for predicting PD-L1 status. The integrated model exhibited superior performance, with an AUC of 0.91 (0.87–0.95), sensitivity of 0.85 (0.82–0.89), and specificity of 0.75 (0.72–0.80). Our findings indicate that the DLR model holds promise as a valuable tool for predicting the PD-L1 status in patients with NSCLC, which can greatly assist in clinical decision-making and the selection of personalized treatment strategies. |
| format | Article |
| id | doaj-art-9409f3b812d34675994d7a80a4daae72 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-9409f3b812d34675994d7a80a4daae722025-08-20T03:10:14ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-91575-yPredicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT imagesJiameng Lu0Xinyi Liu1Xiaoqing Ji2Yunxiu Jiang3Anli Zuo4Zihan Guo5Shuran Yang6Haiying Peng7Fei Sun8Degan Lu9Department of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical MedicineDepartment of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical MedicineDepartment of Nursing, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan HospitalDepartment of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical MedicineDepartment of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical MedicineDepartment of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical MedicineDepartment of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical MedicineDepartment of Respiratory and Critical Care Medicine, The Second People’s Hospital of Yibin CityDepartment of Respiratory and Critical Care Medicine, Jining No.1 People’s HospitalDepartment of Respiratory, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Institute of Respiratory Diseases, Shandong Institute of Anesthesia and Respiratory Critical MedicineAbstract Radiomics refers to the utilization of automated or semi-automated techniques to extract and analyze numerous quantitative features from medical images, such as computerized tomography (CT) or magnetic resonance imaging (MRI) scans. This study aims to develop a deep learning radiomics (DLR)-based approach for predicting programmed death-ligand 1 (PD-L1) expression in patients with non-small cell lung cancer (NSCLC). Data from 352 NSCLC patients with known PD-L1 expression were collected, of which 48.29% (170/352) were tested positive for PD-L1 expression. Tumor regions of interest (ROI) were semi-automatically segmented based on CT images, and DL features were extracted using Residual Network 50. The least absolute shrinkage and selection operator (LASSO) algorithm was used for feature selection and dimensionality reduction. Seven algorithms were used to build models, and the most optimal ones were identified. A combined model integrating DLR with clinical data was also developed. The predictive performance of each model was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve analysis. The DLR model, based on CT images, demonstrated an AUC of 0.85 (95% confidence interval (CI), 0.82–0.88), sensitivity of 0.80 (0.74–0.85), and specificity of 0.73 (0.70–0.77) for predicting PD-L1 status. The integrated model exhibited superior performance, with an AUC of 0.91 (0.87–0.95), sensitivity of 0.85 (0.82–0.89), and specificity of 0.75 (0.72–0.80). Our findings indicate that the DLR model holds promise as a valuable tool for predicting the PD-L1 status in patients with NSCLC, which can greatly assist in clinical decision-making and the selection of personalized treatment strategies.https://doi.org/10.1038/s41598-025-91575-yNon-small cell lung cancer (NSCLC)Deep learningProgrammed death-ligand 1(PD-L1). |
| spellingShingle | Jiameng Lu Xinyi Liu Xiaoqing Ji Yunxiu Jiang Anli Zuo Zihan Guo Shuran Yang Haiying Peng Fei Sun Degan Lu Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images Scientific Reports Non-small cell lung cancer (NSCLC) Deep learning Programmed death-ligand 1(PD-L1). |
| title | Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images |
| title_full | Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images |
| title_fullStr | Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images |
| title_full_unstemmed | Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images |
| title_short | Predicting PD-L1 status in NSCLC patients using deep learning radiomics based on CT images |
| title_sort | predicting pd l1 status in nsclc patients using deep learning radiomics based on ct images |
| topic | Non-small cell lung cancer (NSCLC) Deep learning Programmed death-ligand 1(PD-L1). |
| url | https://doi.org/10.1038/s41598-025-91575-y |
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