Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks
ObjectiveTo assess the accuracy of pN staging prediction in papillary thyroid carcinoma (PTC) using ultrasound radiomics and deep neural networks (DNN). MethodsA retrospective analysis was conducted on 375 patients with pathologically confirmed PTC, comprising 261 cases in the training set and 114 i...
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Magazine House of Cancer Research on Prevention and Treatment
2025-02-01
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| Series: | Zhongliu Fangzhi Yanjiu |
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| Online Access: | http://www.zlfzyj.com/cn/article/doi/10.3971/j.issn.1000-8578.2025.24.0617 |
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| author | Jieli ZHOU Linjuan WU Pengtian ZHANG Yanxia Peng Dong HAN |
| author_facet | Jieli ZHOU Linjuan WU Pengtian ZHANG Yanxia Peng Dong HAN |
| author_sort | Jieli ZHOU |
| collection | DOAJ |
| description | ObjectiveTo assess the accuracy of pN staging prediction in papillary thyroid carcinoma (PTC) using ultrasound radiomics and deep neural networks (DNN). MethodsA retrospective analysis was conducted on 375 patients with pathologically confirmed PTC, comprising 261 cases in the training set and 114 in the test set. Staging was categorized as pN0 (no cervical lymph node metastasis), pN1a (central neck lymph node metastasis), and pN1b (lateral neck lymph node metastasis). An ultrasound physician manually segmented the regions of interest (ROIs) for PTC, extracting 1899 radiomic features. Dimensionality reduction was performed using the least absolute shrinkage and selection operator (LASSO) regression. A DNN model for predicting PTC pN staging was developed using the H2O deep learning platform, trained on the training set, and validated on the test set to assess the accuracy of the optimal model. ResultsA total of 153 patients were in the pN0 stage, 131 patients in the pN1a stage, and 91 patients in the pN1b stage. LASSO regression selected 15 radiomic features for each PTC. The optimal DNN model, constructed using these 15 features, achieved accuracies of 85.82% on the training set and 81.57% on the test set. ConclusionUltrasound radiomics of PTC demonstrates high accuracy in predicting pN staging and shows potential for automating N staging in patients. |
| format | Article |
| id | doaj-art-ba7013e116f446dcbc66f060787a5e74 |
| institution | DOAJ |
| issn | 1000-8578 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Magazine House of Cancer Research on Prevention and Treatment |
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| series | Zhongliu Fangzhi Yanjiu |
| spelling | doaj-art-ba7013e116f446dcbc66f060787a5e742025-08-20T02:45:37ZzhoMagazine House of Cancer Research on Prevention and TreatmentZhongliu Fangzhi Yanjiu1000-85782025-02-0152215115510.3971/j.issn.1000-8578.2025.24.061720240617Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural NetworksJieli ZHOU0Linjuan WU1Pengtian ZHANG2Yanxia Peng3Dong HAN4Department of Ultrasound, The First Affiliated Hospital of Air Force Medical University, Xi’an 710032, ChinaDepartment of Ultrasound, Xi’an Fengcheng Hospital, Xi’an 710018, ChinaDepartment of Medical Imaging, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, ChinaDepartment of Ultrasound, Northwest Women’s and Children’s Hospital, Xi’an 710061, ChinaDepartment of Medical Imaging, Affiliated Hospital of Shaanxi University of Chinese Medicine, Xianyang 712000, ChinaObjectiveTo assess the accuracy of pN staging prediction in papillary thyroid carcinoma (PTC) using ultrasound radiomics and deep neural networks (DNN). MethodsA retrospective analysis was conducted on 375 patients with pathologically confirmed PTC, comprising 261 cases in the training set and 114 in the test set. Staging was categorized as pN0 (no cervical lymph node metastasis), pN1a (central neck lymph node metastasis), and pN1b (lateral neck lymph node metastasis). An ultrasound physician manually segmented the regions of interest (ROIs) for PTC, extracting 1899 radiomic features. Dimensionality reduction was performed using the least absolute shrinkage and selection operator (LASSO) regression. A DNN model for predicting PTC pN staging was developed using the H2O deep learning platform, trained on the training set, and validated on the test set to assess the accuracy of the optimal model. ResultsA total of 153 patients were in the pN0 stage, 131 patients in the pN1a stage, and 91 patients in the pN1b stage. LASSO regression selected 15 radiomic features for each PTC. The optimal DNN model, constructed using these 15 features, achieved accuracies of 85.82% on the training set and 81.57% on the test set. ConclusionUltrasound radiomics of PTC demonstrates high accuracy in predicting pN staging and shows potential for automating N staging in patients.http://www.zlfzyj.com/cn/article/doi/10.3971/j.issn.1000-8578.2025.24.0617thyroid cancerpapillaryultrasonographypn stagingpredictinglymph nodes |
| spellingShingle | Jieli ZHOU Linjuan WU Pengtian ZHANG Yanxia Peng Dong HAN Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks Zhongliu Fangzhi Yanjiu thyroid cancer papillary ultrasonography pn staging predicting lymph nodes |
| title | Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks |
| title_full | Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks |
| title_fullStr | Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks |
| title_full_unstemmed | Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks |
| title_short | Prediction of pN Staging of Papillary Thyroid Carcinoma Using Ultrasonography Radiomics and Deep Neural Networks |
| title_sort | prediction of pn staging of papillary thyroid carcinoma using ultrasonography radiomics and deep neural networks |
| topic | thyroid cancer papillary ultrasonography pn staging predicting lymph nodes |
| url | http://www.zlfzyj.com/cn/article/doi/10.3971/j.issn.1000-8578.2025.24.0617 |
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