Journal of clinical ultrasound : JCU · 2025
Ultrasound-Based Transfer Learning Model to Assist Partially Cystic Thyroid Nodule Diagnosis.
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Zhang Qibo, Sun Zhaohui, Wang Yudong, Zhang Chuanpeng, Zou Ying, Shi Yan
Аннотация
PURPOSE: A transfer learning model based on ultrasound was established to predict the malignant probability of partially cystic thyroid nodule (PCTN) preoperatively, providing clinicians with a non-invasive primary screening method.
METHODS: 258 PCTNs of 258 patients from January 2020 to January 2024 were analyzed retrospectively. The dataset was randomly divided into a training set and a test set in a ratio of 8:2. Five different pre-trained models were chosen for transfer learning, including EfficientNet, Inception_v3, Mobilenet_v3, SqueezeNet, and VGG19. The area under the curve (AUC), accuracy, sensitivity, and specificity of the training and test cohorts were calculated. Grad-Class Activation Map (Grad-CAM) was drawn to interpret the results visually. All the ultrasound images were reviewed by two radiologists; multivariate logistic analyses explored the independent risk factors for malignant PCTN. The diagnostic effectiveness of transfer learning models and radiologists was compared.
RESULTS: Inception_v3 model achieved the highest AUC of 0.9243 (95% CI: 0.8849-0.9439) in predicting the malignancy of PCTN in the training cohort, with an accuracy of 85.19%, sensitivity of 85.26%, and specificity of 85.00%. The diagnostic efficiency of the Inception_v3 model was better than that obtained by multivariate logistic regression analysis with AUC of 0.8247 (95% CI: 0.7579-0.8915) in the training cohort, with an accuracy of 83.33%, a sensitivity of 68.00%, and a specificity of 71.80%. Red or warm-colored regions in Grad-CAM represented that these features were more important to model decisions, while blue or cool-colored regions represented those features that were less important.
CONCLUSION: Ultrasound-based transfer learning model could predict the malignant probability of PCTN noninvasively before surgery, especially the Inception_v3 model, to assist clinical decision-making.
METHODS: 258 PCTNs of 258 patients from January 2020 to January 2024 were analyzed retrospectively. The dataset was randomly divided into a training set and a test set in a ratio of 8:2. Five different pre-trained models were chosen for transfer learning, including EfficientNet, Inception_v3, Mobilenet_v3, SqueezeNet, and VGG19. The area under the curve (AUC), accuracy, sensitivity, and specificity of the training and test cohorts were calculated. Grad-Class Activation Map (Grad-CAM) was drawn to interpret the results visually. All the ultrasound images were reviewed by two radiologists; multivariate logistic analyses explored the independent risk factors for malignant PCTN. The diagnostic effectiveness of transfer learning models and radiologists was compared.
RESULTS: Inception_v3 model achieved the highest AUC of 0.9243 (95% CI: 0.8849-0.9439) in predicting the malignancy of PCTN in the training cohort, with an accuracy of 85.19%, sensitivity of 85.26%, and specificity of 85.00%. The diagnostic efficiency of the Inception_v3 model was better than that obtained by multivariate logistic regression analysis with AUC of 0.8247 (95% CI: 0.7579-0.8915) in the training cohort, with an accuracy of 83.33%, a sensitivity of 68.00%, and a specificity of 71.80%. Red or warm-colored regions in Grad-CAM represented that these features were more important to model decisions, while blue or cool-colored regions represented those features that were less important.
CONCLUSION: Ultrasound-based transfer learning model could predict the malignant probability of PCTN noninvasively before surgery, especially the Inception_v3 model, to assist clinical decision-making.