Scholarlink

Background <p>Calcification 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.</p> Methods <p>A 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.</p> Results <p>Among the 6886 thyroid nodules included in this study, 4433 were benign and 2453 were malignant. DL<sub>AM−CFF</sub>—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. DL<sub>AM−CFF</sub> was compared with DL<sub>CFF</sub> (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 DL<sub>AM−CFF</sub> (<i>P</i> &lt; 0.05).</p> Conclusions <p>The “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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A “calcification”-enhanced deep learning approach for precise differentiation of thyroid nodules

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月24日

查看全文
  • Xinying Zhu,
  • Chen Chen,
  • Yahan Zhou,
  • Lingyan Zhou,
  • Qingquan He,
  • Xiao Zhang,
  • Jincao Yao,
  • Chenke Xu,
  • Dong Xu

摘要

Background

Calcification 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.

Methods

A 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.

Results

Among 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).

Conclusions

The “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.