Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study
Abstract Background This study aims to develop and validate a multi-modal machine learning model for preoperative risk stratification in papillary thyroid carcinoma (PTC), addressing limitations of current systems that rely on postoperative pathological features. Methods We analyzed 974 PTC patients...
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BMC
2025-08-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-025-00921-w |
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| author | Jia-Wei Feng Lu Zhang Yu-Xin Yang Rong-Jie Qin Shui-Qing Liu An-Cheng Qin Yong Jiang |
| author_facet | Jia-Wei Feng Lu Zhang Yu-Xin Yang Rong-Jie Qin Shui-Qing Liu An-Cheng Qin Yong Jiang |
| author_sort | Jia-Wei Feng |
| collection | DOAJ |
| description | Abstract Background This study aims to develop and validate a multi-modal machine learning model for preoperative risk stratification in papillary thyroid carcinoma (PTC), addressing limitations of current systems that rely on postoperative pathological features. Methods We analyzed 974 PTC patients from three medical centers in China using a multi-modal approach integrating: (1) clinical indicators, (2) immunological indices, (3) ultrasound radiomics features, and (4) CT radiomics features. Our methodology employed gradient boosting machine for feature selection and random forest for classification, with model interpretability provided through SHapley Additive exPlanations (SHAP) analysis. The model was validated on internal (n = 225) and two external cohorts (n = 51, n = 174). Results The final 15-feature model achieved AUCs of 0.91, 0.84, and 0.77 across validation cohorts, improving to 0.96, 0.95, and 0.89 after cohort-specific refitting. SHAP analysis revealed CT texture features, ultrasound morphological features, and immune-inflammatory markers as key predictors, with consistent patterns across validation sites despite center-specific variations. Subgroup analysis showed superior performance in tumors > 1 cm and patients without extrathyroidal extension. Conclusion Our multi-modal machine learning approach provides accurate preoperative risk stratification for PTC with robust cross-center applicability. This computational framework for integrating heterogeneous imaging and clinical data demonstrates the potential of multi-modal joint learning in healthcare imaging to transform clinical decision-making by enabling personalized treatment planning. |
| format | Article |
| id | doaj-art-e3690aa47bad4f278dbe83a91ae18838 |
| institution | DOAJ |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
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| series | Cancer Imaging |
| spelling | doaj-art-e3690aa47bad4f278dbe83a91ae188382025-08-20T03:06:04ZengBMCCancer Imaging1470-73302025-08-0125111510.1186/s40644-025-00921-wDevelopment and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort studyJia-Wei Feng0Lu Zhang1Yu-Xin Yang2Rong-Jie Qin3Shui-Qing Liu4An-Cheng Qin5Yong Jiang6Department of Thyroid Surgery, The Third Affiliated Hospital of Soochow University, Changzhou First People’s HospitalDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of MedicineDepartment of Thyroid Surgery, The Third Affiliated Hospital of Soochow University, Changzhou First People’s HospitalThe Second Clinical Medical School of Nanjing Medical UniversityDepartment of Ultrasound, The Third Affiliated Hospital of Soochow University, Changzhou First People’s HospitalDepartment of Thyroid Surgery, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical UniversityDepartment of Thyroid Surgery, The Third Affiliated Hospital of Soochow University, Changzhou First People’s HospitalAbstract Background This study aims to develop and validate a multi-modal machine learning model for preoperative risk stratification in papillary thyroid carcinoma (PTC), addressing limitations of current systems that rely on postoperative pathological features. Methods We analyzed 974 PTC patients from three medical centers in China using a multi-modal approach integrating: (1) clinical indicators, (2) immunological indices, (3) ultrasound radiomics features, and (4) CT radiomics features. Our methodology employed gradient boosting machine for feature selection and random forest for classification, with model interpretability provided through SHapley Additive exPlanations (SHAP) analysis. The model was validated on internal (n = 225) and two external cohorts (n = 51, n = 174). Results The final 15-feature model achieved AUCs of 0.91, 0.84, and 0.77 across validation cohorts, improving to 0.96, 0.95, and 0.89 after cohort-specific refitting. SHAP analysis revealed CT texture features, ultrasound morphological features, and immune-inflammatory markers as key predictors, with consistent patterns across validation sites despite center-specific variations. Subgroup analysis showed superior performance in tumors > 1 cm and patients without extrathyroidal extension. Conclusion Our multi-modal machine learning approach provides accurate preoperative risk stratification for PTC with robust cross-center applicability. This computational framework for integrating heterogeneous imaging and clinical data demonstrates the potential of multi-modal joint learning in healthcare imaging to transform clinical decision-making by enabling personalized treatment planning.https://doi.org/10.1186/s40644-025-00921-wPapillary thyroid carcinomaRisk stratificationMachine learningRadiomicsSHAP analysis |
| spellingShingle | Jia-Wei Feng Lu Zhang Yu-Xin Yang Rong-Jie Qin Shui-Qing Liu An-Cheng Qin Yong Jiang Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study Cancer Imaging Papillary thyroid carcinoma Risk stratification Machine learning Radiomics SHAP analysis |
| title | Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study |
| title_full | Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study |
| title_fullStr | Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study |
| title_full_unstemmed | Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study |
| title_short | Development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma: a multicenter, retrospective cohort study |
| title_sort | development and validation of the multidimensional machine learning model for preoperative risk stratification in papillary thyroid carcinoma a multicenter retrospective cohort study |
| topic | Papillary thyroid carcinoma Risk stratification Machine learning Radiomics SHAP analysis |
| url | https://doi.org/10.1186/s40644-025-00921-w |
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