Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis
Abstract Background Postoperative papillary thyroid cancer (PTC) patients often have enlarged cervical lymph nodes due to inflammation or hyperplasia, which complicates the assessment of recurrence or metastasis. This study aimed to explore the diagnostic capabilities of computed tomography (CT) ima...
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BMC
2025-08-01
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| Online Access: | https://doi.org/10.1186/s12885-025-14594-y |
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| author | Feng Pang Lijiao Wu Jianping Qiu Yu Guo Liangen Xie Shimin Zhuang Mengya Du Danni Liu Chenyue Tan Tianrun Liu |
| author_facet | Feng Pang Lijiao Wu Jianping Qiu Yu Guo Liangen Xie Shimin Zhuang Mengya Du Danni Liu Chenyue Tan Tianrun Liu |
| author_sort | Feng Pang |
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| description | Abstract Background Postoperative papillary thyroid cancer (PTC) patients often have enlarged cervical lymph nodes due to inflammation or hyperplasia, which complicates the assessment of recurrence or metastasis. This study aimed to explore the diagnostic capabilities of computed tomography (CT) imaging and radiomic analysis to distinguish the recurrence of cervical lymph nodes in patients with PTC postoperatively. Materials and methods A retrospective analysis of 194 PTC patients who underwent total thyroidectomy was conducted, with 98 cases of cervical lymph node recurrence and 96 cases without recurrence. Using 3D Slicer software, Regions of Interest (ROI) were delineated on enhanced venous phase CT images, analyzing 302 positive and 391 negative lymph nodes. These nodes were randomly divided into training and validation sets in a 3:2 ratio. Python was used to extract radiomic features from the ROIs and to develop radiomic models. Univariate and multivariate analyses identified statistically significant risk factors for cervical lymph node recurrence from clinical data, which, when combined with radiomic scores, formed a nomogram to predict recurrence risk. The diagnostic efficacy and clinical utility of the models were assessed using ROC curves, calibration curves, and Decision Curve Analysis (DCA). Results This study analyzed 693 lymph nodes (302 positive and 391 negative) and identified 35 significant radiomic features through dimensionality reduction and selection. The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed. |
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| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
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| series | BMC Cancer |
| spelling | doaj-art-5d9cd10e2d0e48d2ab1789e333cfd8f62025-08-20T03:43:02ZengBMCBMC Cancer1471-24072025-08-0125111010.1186/s12885-025-14594-yMachine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysisFeng Pang0Lijiao Wu1Jianping Qiu2Yu Guo3Liangen Xie4Shimin Zhuang5Mengya Du6Danni Liu7Chenyue Tan8Tianrun Liu9Department of General Surgery (Thyroid Surgery), The Sixth Affiliated Hospital, Sun Yat- sen UniversityDepartment of Otorhinolaryngology Head and Neck Surgery, Shenshan MedicalCenter，SunYat-sen Memorial Hospital, Sun Yat-sen UniversityBiomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen UniversityDepartment of General Surgery (Thyroid Surgery), The Sixth Affiliated Hospital, Sun Yat- sen UniversityDepartment of General Surgery (Thyroid Surgery), The Sixth Affiliated Hospital, Sun Yat- sen UniversityDepartment of General Surgery (Thyroid Surgery), The Sixth Affiliated Hospital, Sun Yat- sen UniversityDepartment of General Surgery (Thyroid Surgery), The Sixth Affiliated Hospital, Sun Yat- sen UniversityDepartment of General Surgery (Thyroid Surgery), The Sixth Affiliated Hospital, Sun Yat- sen UniversitySun Yat-sen University School of Medicine, Sun Yat-sen UniversityDepartment of General Surgery (Thyroid Surgery), The Sixth Affiliated Hospital, Sun Yat- sen UniversityAbstract Background Postoperative papillary thyroid cancer (PTC) patients often have enlarged cervical lymph nodes due to inflammation or hyperplasia, which complicates the assessment of recurrence or metastasis. This study aimed to explore the diagnostic capabilities of computed tomography (CT) imaging and radiomic analysis to distinguish the recurrence of cervical lymph nodes in patients with PTC postoperatively. Materials and methods A retrospective analysis of 194 PTC patients who underwent total thyroidectomy was conducted, with 98 cases of cervical lymph node recurrence and 96 cases without recurrence. Using 3D Slicer software, Regions of Interest (ROI) were delineated on enhanced venous phase CT images, analyzing 302 positive and 391 negative lymph nodes. These nodes were randomly divided into training and validation sets in a 3:2 ratio. Python was used to extract radiomic features from the ROIs and to develop radiomic models. Univariate and multivariate analyses identified statistically significant risk factors for cervical lymph node recurrence from clinical data, which, when combined with radiomic scores, formed a nomogram to predict recurrence risk. The diagnostic efficacy and clinical utility of the models were assessed using ROC curves, calibration curves, and Decision Curve Analysis (DCA). Results This study analyzed 693 lymph nodes (302 positive and 391 negative) and identified 35 significant radiomic features through dimensionality reduction and selection. The three machine learning models, including the Lasso regression, Support Vector Machine (SVM), and RF radiomics models, showed.https://doi.org/10.1186/s12885-025-14594-yPapillary thyroid CancerCervical lymph node recurrenceMachine learningCT radiomics |
| spellingShingle | Feng Pang Lijiao Wu Jianping Qiu Yu Guo Liangen Xie Shimin Zhuang Mengya Du Danni Liu Chenyue Tan Tianrun Liu Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis BMC Cancer Papillary thyroid Cancer Cervical lymph node recurrence Machine learning CT radiomics |
| title | Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis |
| title_full | Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis |
| title_fullStr | Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis |
| title_full_unstemmed | Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis |
| title_short | Machine learning models for diagnosing lymph node recurrence in postoperative PTC patients: a radiomic analysis |
| title_sort | machine learning models for diagnosing lymph node recurrence in postoperative ptc patients a radiomic analysis |
| topic | Papillary thyroid Cancer Cervical lymph node recurrence Machine learning CT radiomics |
| url | https://doi.org/10.1186/s12885-025-14594-y |
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