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Enhancing BRAF V600E mutation prediction in thyroid cancer through interpretable deep learning models combining clinical and ultrasound-based radiomics features

   
@article{QIMS155066,
	author = {Lijie Zhang and Chunwang Huang and Zefeng Chen and Yuanlin Ying and Nan Jiang and Xiaozhu Zhong and Fenghuan Chen and Yuping Guo and Siwei Luo},
	title = {Enhancing BRAF V600E mutation prediction in thyroid cancer through interpretable deep learning models combining clinical and ultrasound-based radiomics features},
	journal = {Quantitative Imaging in Medicine and Surgery},
	volume = {16},
	number = {7},
	year = {2026},
	keywords = {},
	abstract = {Background: BRAF V600E mutation, the most prevalent driver alteration in papillary thyroid carcinoma, is associated with aggressive clinicopathological features, including macroscopic extrathyroidal extension, lymph node metastasis, and high-risk histological features. BRAF V600E mutation is determined by tissue biopsy/surgery and gene sequencing, which are invasive and costly. The study aimed to develop an interpretable prediction model based on clinical and ultrasound characteristics via radiomics and deep learning (DL) methods to noninvasively predict the BRAF V600E mutation in patients with thyroid cancer.Methods: A total of 6,703 ultrasound images from 1,257 lesions in 1,202 patients with thyroid cancer were retrospectively collected. Since multiple ultrasound images were available for each lesion, the lesion-level prediction was derived as the average of the image-level outputs. Univariate and multivariate logistic regression were adopted to construct the clinical model. Six machine learning models were compared to identify the optimal one. A ResNet50-32x4d model was fine-tuned to build the DL model. The extreme gradient boosting (XGBoost) algorithm was employed to integrate the optimal radiomics score (radscore), DL scores, and clinical factors for combined model construction. The Shapley additive explanations (SHAP) algorithm and gradient-weighted class activation mapping technique were applied for interpretability.Results: Multivariate analysis identified the significant predictive variables to be sex [odds ratio (OR) =0.61; 95% confidence interval (CI): 0.54–0.69; P},
	issn = {2223-4306},	url = {https://qims.amegroups.org/article/view/155066}
}