ArticleCancer imaging : the official publication of the International Cancer Imaging Society2025
A "calcification"-enhanced deep learning approach for precise differentiation of thyroid nodules.
Article in Cancer imaging : the official publication of the International Cancer Imaging Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCalcification is one of the most valuable imaging features in the ultrasound diagnosis of thyroid nodules. A calcification-enhanced deep learning (DL) approach was developed in this study for the automatic detection of thyroid nodules and their intranodular calcifications from ultrasound images. Calcification features were integrated into the modeling process to improve the accuracy of benign-malignant differentiation for thyroid nodules.
methodsA total of 6886 thyroid nodules from 3 hospitals, collected between January 2014 and March 2024, were retrospectively included in this study. These nodules were partitioned into training, validation, and test sets at a ratio of 7:1:2. All nodules had a clearly documented final clinical diagnosis of benign or malignant status. A DL model that integrates intranodular calcification features and nodule imaging features was constructed. The model was trained using the training set, hyperparameters were optimized using the validation set, and final evaluation was conducted on an independent test set. The area under the receiver operating characteristic curve (AUC) was used as the primary evaluation metric.
resultsAmong the 6886 thyroid nodules included in this study, 4433 were benign and 2453 were malignant. DLAM−CFF—a DL model that integrates intranodular calcification features and nodule imaging features—exhibited excellent performance in differentiating benign from malignant thyroid nodules within the independent test cohort. Its sensitivity, specificity, and accuracy were 0.863, 0.864, and 0.864, respectively, with an AUC of 0.925. DLAM−CFF was compared with DLCFF (a DL model relying solely on nodule features) and traditional DL models, including Xception, InceptionResNetV2, DenseNet121, and ResNet50. The results indicated that the AUC values of these comparative models were 0.832, 0.805, 0.821, 0.813, and 0.798, respectively—all of which were significantly lower than those of DLAM−CFF (P < 0.05).
conclusionsThe “calcification”-enhanced DL model proposed in this study not only enables the automatic detection of thyroid nodules and their intranodular calcifications in ultrasound images but also demonstrates excellent diagnostic performance in predicting the benignity or malignancy of thyroid nodules by integrating the overall features of nodules with calcification features.
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