A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study
PurposeThyroid Imaging Reporting and Data System (TIRADS) does not perform well in thyroid adenomatoid nodules on ultrasound (TANU). Therefore, we aimed to generate and validate a nomogram based on radiomics features and clinical information to predict the nature of TANU.MethodsA total of 200 TANU i...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1549866/full |
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| author | Sheng Cheng Xian-Tao Zeng Xia Liang Zhi-Liang Hong Jian-Chuan Yang Zi-Ling You Song-Song Wu |
| author_facet | Sheng Cheng Xian-Tao Zeng Xia Liang Zhi-Liang Hong Jian-Chuan Yang Zi-Ling You Song-Song Wu |
| author_sort | Sheng Cheng |
| collection | DOAJ |
| description | PurposeThyroid Imaging Reporting and Data System (TIRADS) does not perform well in thyroid adenomatoid nodules on ultrasound (TANU). Therefore, we aimed to generate and validate a nomogram based on radiomics features and clinical information to predict the nature of TANU.MethodsA total of 200 TANU in 200 patients were enrolled. Firstly, radiomics nomograms (R_Nomogram) and clinical nomograms (C_Nomogram) were constructed using eight machine-learning algorithms. The best R_Nomogram and C_Nomogram generated the Radiomics-clinical nomogram (R-C_nomogram). We compared the Area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) of different nomograms. The unnecessary intervention rates were compared between nomograms and the 2017 ACR TI-RADS recommendations.ResultsThe R-C_Nomogram had a higher AUC than other nomograms [training cohort: R-C_Nomogram (AUC: 0.922) Vs. C_Nomogram (AUC: 0.825): p<0.001, R-C_Nomogram Vs. R_ Nomogram (AUC:0.836), p=0.007); validation cohort: R-C_Nomogram (AUC: 0.868) Vs. C_Nomogram (AUC: 0.850): p=0.778, R-C_Nomogram Vs. R_Nomogram (AUC:0.684), p=0.005). The R-C_Nomogram has the lowest unnecessary intervention rate among all approaches.ConclusionThe R-C_Nomogram exhibited excellent diagnostic performances for predicting the nature of TANU. By incorporating clinical and radiomics features, the R-C Nomogram can reduce unnecessary biopsies and guide treatment decisions such as ultrasound-guided thermal ablation, improving patient management and reducing healthcare resource burden. |
| format | Article |
| id | doaj-art-6ead5b3dc51f43548b943b624ee308dc |
| institution | DOAJ |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-6ead5b3dc51f43548b943b624ee308dc2025-08-20T03:10:05ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-05-011510.3389/fonc.2025.15498661549866A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center studySheng Cheng0Xian-Tao Zeng1Xia Liang2Zhi-Liang Hong3Jian-Chuan Yang4Zi-Ling You5Song-Song Wu6Department of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Ultrasound, First Affiliated Hospital of Fujian Medical University, National Regional Medical Center, Fujian Medical University, Fuzhou, Fujian, ChinaDepartment of Ultrasound, Fuzhou University Affiliated Provincial Hospital, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, ChinaPurposeThyroid Imaging Reporting and Data System (TIRADS) does not perform well in thyroid adenomatoid nodules on ultrasound (TANU). Therefore, we aimed to generate and validate a nomogram based on radiomics features and clinical information to predict the nature of TANU.MethodsA total of 200 TANU in 200 patients were enrolled. Firstly, radiomics nomograms (R_Nomogram) and clinical nomograms (C_Nomogram) were constructed using eight machine-learning algorithms. The best R_Nomogram and C_Nomogram generated the Radiomics-clinical nomogram (R-C_nomogram). We compared the Area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) of different nomograms. The unnecessary intervention rates were compared between nomograms and the 2017 ACR TI-RADS recommendations.ResultsThe R-C_Nomogram had a higher AUC than other nomograms [training cohort: R-C_Nomogram (AUC: 0.922) Vs. C_Nomogram (AUC: 0.825): p<0.001, R-C_Nomogram Vs. R_ Nomogram (AUC:0.836), p=0.007); validation cohort: R-C_Nomogram (AUC: 0.868) Vs. C_Nomogram (AUC: 0.850): p=0.778, R-C_Nomogram Vs. R_Nomogram (AUC:0.684), p=0.005). The R-C_Nomogram has the lowest unnecessary intervention rate among all approaches.ConclusionThe R-C_Nomogram exhibited excellent diagnostic performances for predicting the nature of TANU. By incorporating clinical and radiomics features, the R-C Nomogram can reduce unnecessary biopsies and guide treatment decisions such as ultrasound-guided thermal ablation, improving patient management and reducing healthcare resource burden.https://www.frontiersin.org/articles/10.3389/fonc.2025.1549866/fullthyroid neoplasmsradiomicsultrasonographymachine learningnomogram |
| spellingShingle | Sheng Cheng Xian-Tao Zeng Xia Liang Zhi-Liang Hong Jian-Chuan Yang Zi-Ling You Song-Song Wu A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study Frontiers in Oncology thyroid neoplasms radiomics ultrasonography machine learning nomogram |
| title | A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study |
| title_full | A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study |
| title_fullStr | A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study |
| title_full_unstemmed | A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study |
| title_short | A nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound: a dual-center study |
| title_sort | nomogram for predicting the nature of thyroid adenomatoid nodules on ultrasound a dual center study |
| topic | thyroid neoplasms radiomics ultrasonography machine learning nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1549866/full |
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