Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study
Abstract Objectives To establish and validate a dual-modal radiomics nomogram from grayscale ultrasound and color doppler flow imaging (CDFI) of cervical lymph nodes (LNs), aiming to improve the diagnostic accuracy of metastatic LNs in differentiated thyroid carcinoma (DTC). Methods DTC patients wit...
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| Format: | Article |
| Language: | English |
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BMC
2025-01-01
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| Series: | Cancer Imaging |
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| Online Access: | https://doi.org/10.1186/s40644-025-00825-9 |
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| author | Jiajia Tang Yan Tian Jiaojiao Ma Xuehua Xi Liangkai Wang Zhe Sun Xinyi Liu Xuejiao Yu Bo Zhang |
| author_facet | Jiajia Tang Yan Tian Jiaojiao Ma Xuehua Xi Liangkai Wang Zhe Sun Xinyi Liu Xuejiao Yu Bo Zhang |
| author_sort | Jiajia Tang |
| collection | DOAJ |
| description | Abstract Objectives To establish and validate a dual-modal radiomics nomogram from grayscale ultrasound and color doppler flow imaging (CDFI) of cervical lymph nodes (LNs), aiming to improve the diagnostic accuracy of metastatic LNs in differentiated thyroid carcinoma (DTC). Methods DTC patients with suspected cervical LNs in two medical centers were retrospectively enrolled. Pathological results were set as gold standard. We extracted radiomic characteristics from grayscale ultrasound and CDFI images, then applied lasso (least absolute shrinkage and selection operator) regression analysis to analyze radiomics features and calculate the rad-score. A nomogram based on rad-score, clinical data, and ultrasound signs was developed. The performance of the model was evaluated using AUC and calibration curve. We also assessed the model’s diagnostic ability in European Thyroid Association (ETA) indeterminate LNs. Results 377 DTC patients and 726 LNs were enrolled. 37 radiomics features were determined and calculated as rad-score. The dual-modal radiomics model showed good calibration capabilities. The radiomics model displayed higher diagnostic ability than the traditional ultrasound model in the training set [0.871 (95% CI: 0.839–0.904) vs. 0.848 (95% CI: 0.812–0.884), p<0.01], internal test set [0.804 (95% CI: 0.741–0.867) vs. 0.803 (95% CI: 0.74–0.866), p = 0.696], and external validation cohort [0.939 (95% CI: 0.893–0.984) vs. 0.921 (95% CI: 0.857–0.985), p = 0.026]. The radiomics model could also significantly improve the detection rate of metastatic LNs in the ETA indeterminate LN category. Conclusions The dual-modal radiomics nomogram can improve the diagnostic accuracy of metastatic LNs of DTC, especially for LNs in ETA indeterminate classification. |
| format | Article |
| id | doaj-art-99faacb15d744e7cb74c7005cdd866f0 |
| institution | Kabale University |
| issn | 1470-7330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | Cancer Imaging |
| spelling | doaj-art-99faacb15d744e7cb74c7005cdd866f02025-01-26T12:50:37ZengBMCCancer Imaging1470-73302025-01-0125111510.1186/s40644-025-00825-9Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center studyJiajia Tang0Yan Tian1Jiaojiao Ma2Xuehua Xi3Liangkai Wang4Zhe Sun5Xinyi Liu6Xuejiao Yu7Bo Zhang8Department of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship HospitalDepartment of Ultrasound, China-Japan Friendship Hospital, National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine of Chinese Academy of Medical SciencesAbstract Objectives To establish and validate a dual-modal radiomics nomogram from grayscale ultrasound and color doppler flow imaging (CDFI) of cervical lymph nodes (LNs), aiming to improve the diagnostic accuracy of metastatic LNs in differentiated thyroid carcinoma (DTC). Methods DTC patients with suspected cervical LNs in two medical centers were retrospectively enrolled. Pathological results were set as gold standard. We extracted radiomic characteristics from grayscale ultrasound and CDFI images, then applied lasso (least absolute shrinkage and selection operator) regression analysis to analyze radiomics features and calculate the rad-score. A nomogram based on rad-score, clinical data, and ultrasound signs was developed. The performance of the model was evaluated using AUC and calibration curve. We also assessed the model’s diagnostic ability in European Thyroid Association (ETA) indeterminate LNs. Results 377 DTC patients and 726 LNs were enrolled. 37 radiomics features were determined and calculated as rad-score. The dual-modal radiomics model showed good calibration capabilities. The radiomics model displayed higher diagnostic ability than the traditional ultrasound model in the training set [0.871 (95% CI: 0.839–0.904) vs. 0.848 (95% CI: 0.812–0.884), p<0.01], internal test set [0.804 (95% CI: 0.741–0.867) vs. 0.803 (95% CI: 0.74–0.866), p = 0.696], and external validation cohort [0.939 (95% CI: 0.893–0.984) vs. 0.921 (95% CI: 0.857–0.985), p = 0.026]. The radiomics model could also significantly improve the detection rate of metastatic LNs in the ETA indeterminate LN category. Conclusions The dual-modal radiomics nomogram can improve the diagnostic accuracy of metastatic LNs of DTC, especially for LNs in ETA indeterminate classification.https://doi.org/10.1186/s40644-025-00825-9Differentiated thyroid carcinomaLymph node metastasesUltrasoundDual-modal radiomicsMachine learningFeature extraction |
| spellingShingle | Jiajia Tang Yan Tian Jiaojiao Ma Xuehua Xi Liangkai Wang Zhe Sun Xinyi Liu Xuejiao Yu Bo Zhang Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study Cancer Imaging Differentiated thyroid carcinoma Lymph node metastases Ultrasound Dual-modal radiomics Machine learning Feature extraction |
| title | Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study |
| title_full | Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study |
| title_fullStr | Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study |
| title_full_unstemmed | Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study |
| title_short | Dual-modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma: a two-center study |
| title_sort | dual modal radiomics ultrasound model to diagnose cervical lymph node metastases of differentiated thyroid carcinoma a two center study |
| topic | Differentiated thyroid carcinoma Lymph node metastases Ultrasound Dual-modal radiomics Machine learning Feature extraction |
| url | https://doi.org/10.1186/s40644-025-00825-9 |
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