Improved Preoperative Diagnosis of Medullary Thyroid Carcinoma Using Dual-Mode Ultrasound Radiomics.
Simple Summary: Medullary thyroid carcinoma (MTC) is a rare but aggressive type of thyroid cancer. Accurate preoperative diagnosis using standard ultrasound is highly challenging and relies heavily on the doctor's experience, often leading to misdiagnosis or inadequate surgery. To address this, we d...
| Publicado en: | Cancers Vol. 18; no. 11; pp. 1738 - 1751 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
MDPI
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194779285&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194779285 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: Jun2026 vid: 18 iid: 11 pid: 97109 pub: MDPI artinfo: ui: 194779285 194779285 194779285 10.3390/cancers18111738 194779285 ppf: 1738 ppct: 13 formats: tig: atl: Improved Preoperative Diagnosis of Medullary Thyroid Carcinoma Using Dual-Mode Ultrasound Radiomics. aug: au: Gao, Luying Li, Naishi Xia, Yu Ma, Liyuan An, Yuang Ji, Jiang Gu, Jionghui Zhang, Dingyue Luo, Nengwen Cao, Yang Fan, Yijian Jiang, Yuxin affil: Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China sug: subj: Sensitivity and Specificity Radiologists Radiomics Validity Preoperative Care Thyroid Neoplasms Ultrasonography Neoplasms, Ductal, Lobular, and Medullary Ultrasonography Artificial Intelligence Ultrasonography, Doppler, Color Human Retrospective Design Record Review Image Interpretation, Computer Assisted Machine Learning Decision Making, Clinical Spatial Perception Random Forest Support Vector Machine Logistic Regression Intraclass Correlation Coefficient Data Analysis Software ROC Curve Evaluation Confidence Intervals Calibration Evaluation Task Performance and Analysis Probability Thyroid Neoplasms Blood Supply Funding Source ab: Simple Summary: Medullary thyroid carcinoma (MTC) is a rare but aggressive type of thyroid cancer. Accurate preoperative diagnosis using standard ultrasound is highly challenging and relies heavily on the doctor's experience, often leading to misdiagnosis or inadequate surgery. To address this, we developed a non-invasive artificial intelligence (AI) method. Our results demonstrated that this radiomics model showed good discriminative ability in the diagnosis and stratification of MTC. By overcoming the limitations of subjective evaluation and equipment differences, this AI tool shows potential as a non-invasive adjunctive tool. Background: Preoperative diagnosis of medullary thyroid carcinoma (MTC) is clinically challenging due to sonographic overlap with other thyroid tumors. To address this, we aimed to develop a multi-vendor, multimodal radiomic framework for accurate MTC identification, comparing its diagnostic performance with that of experienced radiologists. Methods: This retrospective study included 467 pathologically confirmed thyroid nodules (94 MTCs, 373 non-MTCs) acquired across multiple ultrasound platforms. The dataset was randomly partitioned into training (80%) and internal testing (20%) sets. In total, 2250 radiomic features were extracted from grayscale and color Doppler images, followed by Z-score normalization to mitigate batch effects. A robust feature selection strategy (LASSO and recursive feature elimination) identified optimal signatures for developing machine learning classifiers (SVM, LR, RF). The optimal model was further validated on an independent, balanced cohort (n = 60; comprising 12 cases each of MTC, papillary carcinoma, follicular carcinoma, follicular adenoma, and nodular goiter) and compared with experienced radiologists across seven classification tasks. Results: The RF model achieved an AUC of 0.993 in distinguishing MTC from papillary carcinoma. The LR model showed an AUC of 0.991 for identifying MTC from all other nodules. In the independent validation cohort, the models maintained superior discriminatory ability, showing better diagnostic performance compared to the image interpretation by radiologists (AUC 0.993 vs. 0.488, p < 0.001). Conclusions: The proposed multi-vendor, multimodal radiomic system demonstrated good discriminative ability in the diagnosis and stratification of MTC. By integrating grayscale and Doppler ultrasound features while overcoming scanner variability, this model shows potential as a non-invasive adjunctive tool. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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