ArticleBMC medical imaging2024
Combining radiomics with thyroid imaging reporting and data system to predict lateral cervical lymph node metastases in medullary thyroid cancer.
Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 6 citations in OpenAlex.
- The Management of Medullary Thyroid Cancer: A Review.Annals of surgical oncology · 2026Review
- CT-derived quantitative imaging biomarkers for hypoxemia risk in repeated lung resection: a retrospective study.BMC medical imaging · 2026Article
- Artificial intelligence in thyroid ultrasound: clinical applications and perspectives.Frontiers in endocrinology · 2026Review
- Synchronous occurrence of papillary thyroid carcinoma and medullary thyroid carcinoma in the setting of Hashimoto's thyroiditis: a case report with literature review.Gland surgery · 2025Article
- Prediction of peripheral lymph node metastasis (LNM) in thyroid cancer using delta radiomics derived from enhanced CT combined with multiple machine learning algorithms.European journal of medical research · 2025Article
- Preoperative identification of the risk factors of cervical lymph node metastasis in medullary thyroid carcinoma.Frontiers in endocrinology · 2025Article
- Review
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8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMedullary Thyroid Carcinoma (MTC) is a rare type of thyroid cancer. Accurate prediction of lateral cervical lymph node metastases (LCLNM) in MTC patients can help guide surgical decisions and ensure that patients receive the most appropriate and effective surgery. To our knowledge, no studies have been published that use radiomics analysis to forecast LCLNM in MTC patients. The purpose of this study is to develop a radiomics combined with thyroid imaging reporting and data system (TI-RADS) model that can use preoperative thyroid ultrasound images to noninvasively predict the LCLNM status of MTC.
methodsWe retrospectively included 218 MTC patients who were confirmed from postoperative pathology as LCLNM negative (n=111) and positive (n=107). Ultrasound features were selected using the Student's t-test, while radiomics features are first extracted from preoperative thyroid ultrasound images, and then a two-step feature selection approach was used to select features. These features are then used to establish three regularized logistic regression models, namely the TI-RADS model (TM), the radiomics model (RM), and the radiomics-TI-RADS model (RTM), in 5-fold cross-validation to determine the likelihood of the LCLNM. The Delong's test and decision curve analysis (DCA) were used to evaluate and compare the performance of the models.
resultsThe ultrasound features of margin and TI-RADS level, and a total of 12 selected radiomics features, were significantly different between the LCLNM negative and positive groups (p<0.05). The TM, RM, and RTM yielded an averaged AUC of 0.68±0.05, 0.78±0.06, and 0.82±0.05 in the 5-fold cross-validation dataset, respectively. RM and RTM are statistically better than TM (p<0.05 and p<0.001) according to Delong test. DCA demonstrates that RTM brings more benefit than TM and RM.
conclusionsWe have developed a joint radiomics-based model for noninvasive prediction of the LCLNM in MTC patients solely using preoperative thyroid ultrasound imaging. It has the potential to be used as a complementary tool to help guide treatment decisions for this rare form of thyroid cancer.
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