A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model
ObjectiveThis study aimed to evaluate the predictive performance of integrated clinical and CT-based radiomic models for assessing targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR (epidermal growth factor receptor) mutations.Materials and methodsThis retrospective study i...
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Frontiers Media S.A.
2025-05-01
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1599206/full |
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| author | Zekai Wu Zekai Wu Peiyan Hua Xiuying Chen Jie Lei Laian Zhang Peng Zhang |
| author_facet | Zekai Wu Zekai Wu Peiyan Hua Xiuying Chen Jie Lei Laian Zhang Peng Zhang |
| author_sort | Zekai Wu |
| collection | DOAJ |
| description | ObjectiveThis study aimed to evaluate the predictive performance of integrated clinical and CT-based radiomic models for assessing targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR (epidermal growth factor receptor) mutations.Materials and methodsThis retrospective study included 106 EGFR-mutated advanced lung adenocarcinoma patients treated with targeted therapies at the Second Hospital of Jilin University (2020–2023). Patients were classified as responders (PR) or non-responders (SD/PD) based on RECIST (Response Evaluation Criteria in Solid Tumors) 1.1 criteria, then randomly divided into training (n = 74) and validation (n = 32) cohorts at a 7:3 ratio. We segmented tumor regions on pre-and post-treatment CT scans using ITK-SNAP, then extracted radiomic features and applied mRMR-LASSO (Minimum Redundancy Maximum Relevance–Least Absolute Shrinkage and Selection Operator). A delta-radiomics model was developed by quantifying feature changes between treatment phases. Significant clinical predictors identified by logistic regression were integrated with radiomic features to build a combined model. Performance was assessed via AUC, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), DeLong’s test, calibration curves, and decision curve analysis.ResultsIn the pre-treatment radiomics model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.751, 0.690, 0.737, 0.639, 0.683, and 0.697; in validation cohorts, these values were 0.726, 0.656, 0.778, 0.500, 0.667, and 0.636. In the delta-radiomics model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.906, 0.865, 0.868, 0.861, 0.868, and 0.861, vs. 0.825, 0.719, 0.722, 0.714, 0.765, and 0.667 in validation. For the clinical model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.828, 0.729, 0.737, 0.722, 0.737, and 0.722, compared to 0.766, 0.750, 0.722, 0.786, 0.812, and 0.688 in validation. In the combined model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.977, 0.946, 0.947, 0.944, 0.947, and 0.944, while in the validation cohorts, these values were 0.913, 0.781, 0.778, 0.786, 0.824, and 0.733.ConclusionThe combined model integrating delta-radiomics with clinical predictors demonstrates superior predictive performance for evaluating targeted therapy efficacy in EGFR-mutated advanced lung adenocarcinoma, significantly outperforming conventional radiomics models relying exclusively on pre-treatment imaging data. |
| format | Article |
| id | doaj-art-23cfed73a82c400c95cffae73e88aa9e |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Medicine |
| spelling | doaj-art-23cfed73a82c400c95cffae73e88aa9e2025-08-20T03:21:56ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-05-011210.3389/fmed.2025.15992061599206A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics modelZekai Wu0Zekai Wu1Peiyan Hua2Xiuying Chen3Jie Lei4Laian Zhang5Peng Zhang6Department of Radiology, The Second Hospital of Jilin University, Changchun, ChinaDepartment of Radiology, Jiangyou People’s Hospital, Mianyang, ChinaDepartment of Thoracic Surgery, The Second Hospital of Jilin University, Changchun, ChinaDepartment of Radiology, The Second Hospital of Jilin University, Changchun, ChinaDepartment of Radiology, The FAW General Hospital of Jilin Province, Changchun, ChinaDepartment of Radiology, Jiangyou People’s Hospital, Mianyang, ChinaDepartment of Radiology, The Second Hospital of Jilin University, Changchun, ChinaObjectiveThis study aimed to evaluate the predictive performance of integrated clinical and CT-based radiomic models for assessing targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR (epidermal growth factor receptor) mutations.Materials and methodsThis retrospective study included 106 EGFR-mutated advanced lung adenocarcinoma patients treated with targeted therapies at the Second Hospital of Jilin University (2020–2023). Patients were classified as responders (PR) or non-responders (SD/PD) based on RECIST (Response Evaluation Criteria in Solid Tumors) 1.1 criteria, then randomly divided into training (n = 74) and validation (n = 32) cohorts at a 7:3 ratio. We segmented tumor regions on pre-and post-treatment CT scans using ITK-SNAP, then extracted radiomic features and applied mRMR-LASSO (Minimum Redundancy Maximum Relevance–Least Absolute Shrinkage and Selection Operator). A delta-radiomics model was developed by quantifying feature changes between treatment phases. Significant clinical predictors identified by logistic regression were integrated with radiomic features to build a combined model. Performance was assessed via AUC, sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), DeLong’s test, calibration curves, and decision curve analysis.ResultsIn the pre-treatment radiomics model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.751, 0.690, 0.737, 0.639, 0.683, and 0.697; in validation cohorts, these values were 0.726, 0.656, 0.778, 0.500, 0.667, and 0.636. In the delta-radiomics model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.906, 0.865, 0.868, 0.861, 0.868, and 0.861, vs. 0.825, 0.719, 0.722, 0.714, 0.765, and 0.667 in validation. For the clinical model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.828, 0.729, 0.737, 0.722, 0.737, and 0.722, compared to 0.766, 0.750, 0.722, 0.786, 0.812, and 0.688 in validation. In the combined model, the AUC, accuracy, sensitivity, specificity, PPV, and NPV of the training cohorts were 0.977, 0.946, 0.947, 0.944, 0.947, and 0.944, while in the validation cohorts, these values were 0.913, 0.781, 0.778, 0.786, 0.824, and 0.733.ConclusionThe combined model integrating delta-radiomics with clinical predictors demonstrates superior predictive performance for evaluating targeted therapy efficacy in EGFR-mutated advanced lung adenocarcinoma, significantly outperforming conventional radiomics models relying exclusively on pre-treatment imaging data.https://www.frontiersin.org/articles/10.3389/fmed.2025.1599206/fulllung adenocarcinomaEGFRtargeted therapyradiomicsdelta-radiomicsnomogram |
| spellingShingle | Zekai Wu Zekai Wu Peiyan Hua Xiuying Chen Jie Lei Laian Zhang Peng Zhang A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model Frontiers in Medicine lung adenocarcinoma EGFR targeted therapy radiomics delta-radiomics nomogram |
| title | A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model |
| title_full | A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model |
| title_fullStr | A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model |
| title_full_unstemmed | A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model |
| title_short | A study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with EGFR mutations using CT-based delta-radiomics model |
| title_sort | study on the prediction of targeted therapy efficacy in advanced lung adenocarcinoma patients with egfr mutations using ct based delta radiomics model |
| topic | lung adenocarcinoma EGFR targeted therapy radiomics delta-radiomics nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1599206/full |
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