Scholarlink

Objective <p><i>BRAF</i><sup><i>V600E</i></sup> is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, genetic mutation detection relies on invasive procedures. This study aimed to extract radiomic features and utilize deep transfer learning (DTL) from ultrasound images to develop a noninvasive artificial intelligence model for identifying <i>BRAF</i><sup><i>V600E</i></sup> mutations.</p> Materials and methods <p>Regions of interest (ROI) were manually annotated in the ultrasound images, and radiomic and DTL features were extracted. These were used in a joint DTL–radiomics (DTLR) model. Fourteen DTL models were employed, and feature selection was performed using the LASSO regression. Eight machine learning methods were used to construct predictive models. Model performance was primarily evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The interpretability of the model was visualized using gradient-weighted class activation maps (Grad-CAM).</p> Results <p>Sole reliance on radiomics for identification of <i>BRAF</i><sup><i>V600E</i></sup> mutations had limited capability, but the optimal DTLR model, combined with ResNet152, effectively identified <i>BRAF</i><sup><i>V600E</i></sup> mutations. In the validation set, the AUC, accuracy, sensitivity and specificity were 0.833, 80.6%, 76.2% and 81.7%, respectively. The AUC of the DTLR model was higher than that of the DTL and radiomics models. Visualization using the ResNet152-based DTLR model revealed its ability to capture and learn ultrasound image features related to <i>BRAF</i><sup><i>V600E</i></sup> mutations.</p> Conclusion <p>The ResNet152-based DTLR model demonstrated significant value in identifying <i>BRAF</i><sup><i>V600E</i></sup> mutations in patients with PTC using ultrasound images. Grad-CAM has the potential to objectively stratify BRAF mutations visually. The findings of this study require further collaboration among more centers and the inclusion of additional data for validation.</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

Breaking barriers: noninvasive AI model for BRAFV600E mutation identification

  • 期刊论文

  • 发表日期：

    2025年2月15日

查看全文
  • Fan Wu,
  • Xiangfeng Lin,
  • Yuying Chen,
  • Mengqian Ge,
  • Ting Pan,
  • Jingjing Shi,
  • Linlin Mao,
  • Gang Pan,
  • You Peng,
  • Li Zhou,
  • Haitao Zheng,
  • Dingcun Luo,
  • Yu Zhang

摘要

Objective

BRAFV600E is the most common mutation found in thyroid cancer and is particularly associated with papillary thyroid carcinoma (PTC). Currently, genetic mutation detection relies on invasive procedures. This study aimed to extract radiomic features and utilize deep transfer learning (DTL) from ultrasound images to develop a noninvasive artificial intelligence model for identifying BRAFV600E mutations.

Materials and methods

Regions of interest (ROI) were manually annotated in the ultrasound images, and radiomic and DTL features were extracted. These were used in a joint DTL–radiomics (DTLR) model. Fourteen DTL models were employed, and feature selection was performed using the LASSO regression. Eight machine learning methods were used to construct predictive models. Model performance was primarily evaluated using area under the curve (AUC), accuracy, sensitivity and specificity. The interpretability of the model was visualized using gradient-weighted class activation maps (Grad-CAM).

Results

Sole reliance on radiomics for identification of BRAFV600E mutations had limited capability, but the optimal DTLR model, combined with ResNet152, effectively identified BRAFV600E mutations. In the validation set, the AUC, accuracy, sensitivity and specificity were 0.833, 80.6%, 76.2% and 81.7%, respectively. The AUC of the DTLR model was higher than that of the DTL and radiomics models. Visualization using the ResNet152-based DTLR model revealed its ability to capture and learn ultrasound image features related to BRAFV600E mutations.

Conclusion

The ResNet152-based DTLR model demonstrated significant value in identifying BRAFV600E mutations in patients with PTC using ultrasound images. Grad-CAM has the potential to objectively stratify BRAF mutations visually. The findings of this study require further collaboration among more centers and the inclusion of additional data for validation.