Ultrasound in medicine & biology · 2026
Predictive Value of Radiomics Model Based on Multimodal Ultrasound for Ablation Zone Absorption After Microwave Ablation in T1aN0M0 Papillary Thyroid Carcinoma.
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Jin Chenyang, Song Ying, Gu Yang, Wu Xiaofeng, Hu Mengshang, Sun Mengyao, Zhang Lihong, Huang Ying, Dong Fenglin
Аннотация
OBJECTIVE: To develop a radiomics model that uses grayscale ultrasound (GSUS) and contrast-enhanced ultrasound (CEUS) images, integrated with clinical and radiological characteristics, to forecast ablation zone absorption after microwave ablation in patients with T1aN0M0 papillary thyroid carcinoma (PTC).
METHODS: This research involved a total of 163 patients with 203 T1aN0M0 PTCs. The cohort was randomly divided into training (n = 142) and validation (n = 61) cohorts with a distribution ratio of 7:3. Radiomics features were obtained from GSUS and CEUS images 1 month after ablation. Feature selection used Pearson and Spearman correlation analyses, as well as the least absolute shrinkage and selection operator regression model. Radiomics models based on GSUS, CEUS and their combination were developed using Cox regression. A nomogram was created to forecast the absorption of the ablation zone, incorporating radiomics scores with clinical and radiological factors. Model performance was assessed and compared using concordance index (C-index) values, net reclassification improvement and integrated discrimination improvement metrics.
RESULTS: The radiomics score from GSUS and CEUS emerged as an independent predictor of ablation zone absorption. The combined model, integrating multimodal radiomics scores with clinical-radiological data, demonstrated optimal performance in the training cohort (C-index = 0.891) and the validation cohort (C-index = 0.906), with significant clinical benefits demonstrated via calibration and decision curves.
CONCLUSION: The combined model integrating radiomic features with clinical and radiological characteristics can effectively predict the absorption of the ablation zone after microwave ablation in patients with T1aN0M0 PTC.
METHODS: This research involved a total of 163 patients with 203 T1aN0M0 PTCs. The cohort was randomly divided into training (n = 142) and validation (n = 61) cohorts with a distribution ratio of 7:3. Radiomics features were obtained from GSUS and CEUS images 1 month after ablation. Feature selection used Pearson and Spearman correlation analyses, as well as the least absolute shrinkage and selection operator regression model. Radiomics models based on GSUS, CEUS and their combination were developed using Cox regression. A nomogram was created to forecast the absorption of the ablation zone, incorporating radiomics scores with clinical and radiological factors. Model performance was assessed and compared using concordance index (C-index) values, net reclassification improvement and integrated discrimination improvement metrics.
RESULTS: The radiomics score from GSUS and CEUS emerged as an independent predictor of ablation zone absorption. The combined model, integrating multimodal radiomics scores with clinical-radiological data, demonstrated optimal performance in the training cohort (C-index = 0.891) and the validation cohort (C-index = 0.906), with significant clinical benefits demonstrated via calibration and decision curves.
CONCLUSION: The combined model integrating radiomic features with clinical and radiological characteristics can effectively predict the absorption of the ablation zone after microwave ablation in patients with T1aN0M0 PTC.