A combined model integrating deep learning, radiomics, and clinical ultrasound features for predicting BRAF V600E mutation in papillary thyroid carcinoma with Hashimoto's thyroiditis

Abstract

Objective: This study aims to develop an integrated model that combines radiomics, deep learning features, and clinical and ultrasound characteristics for predicting BRAF V600E mutations in patients with papillary thyroid carcinoma (PTC) combined with Hashimoto's thyroiditis (HT).

Methods: This retrospective study included 717 thyroid nodules from 672 patients with PTC combined with HT from four hospitals in China. Deep learning and radiomics were employed to extract deep learning and radiomics features from ultrasound images. Feature selection was performed using Pearson's correlation coefficient, the Minimum Redundancy Maximum Relevance (mRMR) algorithm, and LASSO regression. The optimal algorithm was selected from nine machine learning algorithms for model construction, including the traditional radiomics model (RAD), the deep learning model (DL), and their fusion model (DL_RAD). Additionally, a final combined model was developed by integrating the DL_RAD model with clinical and ultrasound features. Model performance was assessed using AUC, calibration curves, and decision curve analysis (DCA), while SHAP analysis was used to interpret the contribution of each feature to the combined model's output.

Results: The combined model achieved superior diagnostic performance, with AUC values of 0.895, 0.864, and 0.815 in the training, validation, and external test sets, respectively, outperforming the RAD model, DL model, and RAD_DL model. DeLong test results indicated significant differences in the external test set (p<0.05). Further validation through calibration curves and DCA confirmed the model's robust performance. SHAP analysis revealed that RAD_DL signature, aspect ratio, extrathyroidal extension, and gender were key contributors to the model's predictions.

Conclusion: The combined model integrating radiomics, deep learning features, and clinical as well as ultrasound characteristics exhibits excellent diagnostic performance in predicting BRAF V600E mutations in patients with PTC coexisting with HT, highlighting its strong potential for clinical application.

Keywords: BRAF V600E mutation; Hashimoto’s thyroiditis; deep learning; papillary thyroid carcinoma; radiomics; ultrasound.

Figure 1

The inclusion process of the study population.

Figure 2

Workflow diagram for the development and evaluation of predictive models. (A) Image preprocessing, radiomics and deep learning feature extraction, feature selection, feature fusion, and construction of the DL_RAD model. (B) Construction of the Combined model by integrating the DL_RAD signature with clinical and ultrasound features. Additionally, among the nine machine learning algorithms, random forest achieved the best performance. (C) Model evaluation and interpretation. LR, Logistic Regression; SVM, Support Vector Machine; RF, Random Forest; XGB, XGBoost; KNN, K-Nearest Neighbors; LGBM, LightGBM; ET, ExtraTrees; G, Gradient Boosting; MLP, Multilayer Perceptron.

Figure 3

Pearson correlation coefficient network diagram and heatmap. (A) The Pearson feature correlation network illustrated the relationships between each pair of selected features. (B) The Pearson correlation coefficient heatmap indicated that each feature acted as an independent predictor, as no correlation coefficient exceeded 0.5.

Figure 4

Performance evaluation of different models. (A-C) AUC curves for four models (RAD, DL, DL_RAD, Combined) across three sets. (D-F) Calibration curves for the four models across the three sets. (G-I) Decision curves for the four models across the three sets. (J-L) DeLong Test for the four models across the three sets.

Figure 5

(A) The SHAP summary plot illustrates the impact of each feature on the model’s prediction. The features include DL_RAD, aspect ratio, ETE, and gender. Higher SHAP values indicate a greater contribution of the corresponding feature to the prediction outcome. (B) Individual prediction explanation for a specific case, where the ultrasound image is shown on the left and the corresponding SHAP force plot on the right. The DL_RAD prediction value, aspect ratio, ETE, and gender are 0.33, 1.291, −1.231, and −0.403, respectively, contributing +0.13, +0.05, −0.02, and −0.01 to the malignant label decision. Among these, the DL_RAD prediction value and aspect ratio positively support the malignant prediction, whereas extrathyroidal extension and gender exert a negative influence. Summing these contributions with the expected value (E[f(X)] = 0.735) yields a final decision probability of 0.89. SHAP values represent absolute contributions to the predicted probability (i.e., additive changes from the base value), measured in probability units.