Frontiers in endocrinology · 2025
Ultrasonic radiomics in predicting pathologic type for thyroid cancer: a preliminary study using radiomics features for predicting medullary thyroid carcinoma.
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Zhang Dai, Yang Fan, Hou Wenjing, Wang Ying, Mu Jiali, Wang Hailing, Wei Xi
Аннотация
INTRODUCTION: Medullary thyroid carcinoma (MTC) is aggressive and difficult to distinguish from papillary thyroid carcinoma (PTC) using traditional ultrasound. Objective to establish a standard-based ultrasound imaging model for preoperative differentiation of MTC from PTC.
METHODS: A retrospective study was conducted on the case data of 213 thyroid cancer patients (82 MTC, 90 lesions; 131 PTC, 135 lesions) from the Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital. We constructed clinical model, radiomics model and comprehensive model by executing machine learning algorithms based on baseline clinical, pathological characteristics and ultrasound image data, respectively.
RESULTS: The study showed that the comprehensive model observed the highest diagnostic efficacy in differentiating MTC from PTC with AUC, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 0.93, 0.88, 0.82, 0.77, 0.91, 85.8%. Delong test results showed that the comprehensive model was significantly better than the clinical model (Z=-3.791, P<0.001) and the radiomics model (Z=-2.017, P=0.044). Calibration curves indicated the comprehensive model and the radiomics model exhibited better stability than the clinical model. Decision curves analysis (DCA) demonstrated that the comprehensive model had the highest clinical net benefit.
DISCUSSIONS: Radiomics model is effective in identifying MTC and PTC preoperatively, and the comprehensive model is better. This approach can aid in identifying the pathologic types of thyroid nodule before clinical operation, supporting personalized medicine in the decision-making process.
METHODS: A retrospective study was conducted on the case data of 213 thyroid cancer patients (82 MTC, 90 lesions; 131 PTC, 135 lesions) from the Department of Diagnostic and Therapeutic Ultrasonography, Tianjin Medical University Cancer Institute and Hospital. We constructed clinical model, radiomics model and comprehensive model by executing machine learning algorithms based on baseline clinical, pathological characteristics and ultrasound image data, respectively.
RESULTS: The study showed that the comprehensive model observed the highest diagnostic efficacy in differentiating MTC from PTC with AUC, sensitivity, specificity, positive predictive value, negative predictive value and accuracy of 0.93, 0.88, 0.82, 0.77, 0.91, 85.8%. Delong test results showed that the comprehensive model was significantly better than the clinical model (Z=-3.791, P<0.001) and the radiomics model (Z=-2.017, P=0.044). Calibration curves indicated the comprehensive model and the radiomics model exhibited better stability than the clinical model. Decision curves analysis (DCA) demonstrated that the comprehensive model had the highest clinical net benefit.
DISCUSSIONS: Radiomics model is effective in identifying MTC and PTC preoperatively, and the comprehensive model is better. This approach can aid in identifying the pathologic types of thyroid nodule before clinical operation, supporting personalized medicine in the decision-making process.