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Objective <p>To establish and validate a multimodal model based on radiomics and deep learning for classifying gastric stromal tumors (GSTs) and gastric leiomyomas (GLMs).</p> Methods <p>The study collected clinical case data of 200 patients (137 GSTs and 63 GLMs) hospitalized for gastric submucosal tumors at the First Affiliated Hospital of Soochow University. The collected data included white light endoscopy, ultrasound endoscopic images, computed tomography (CT) images, records of endoscopic intervention, and pathological results. The patients were randomly divided into a training set (134 cases) and a validation set (66 cases) at a ratio of 7:3. A radiomics model was constructed by extracting radiomic features from three-dimensional CT images using XGBoost algorithm, while a deep learning model was developed based on convolutional neural network to extract deep features. Finally, clinical features and the predictions from the models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. Area under the curve (AUC), sensitivity, specificity, false-negative rate (FNR), decision curve analysis net benefit, calibration, and tenfold stratified cross-validation stability assessed model performance.</p> Results <p>In the multimodal model for the classification of GSTs and GLMs, the prediction of the deep learning model was the most important variable, followed by tumor echo homogeneity, age, the prediction of the radiomics model, and major axis of tumor. The multimodal model achieved excellent AUCs (0.927 in the training set, 0.882 in the validation set), outperforming both radiomics and deep learning models. It exhibited favorable calibration, minimal overfitting, a low FNR, and stable performance via tenfold cross-validation.</p> Conclusion <p>Based on the integration of clinical tabular data, radiomics, and deep learning features, the multimodal model could classify GSTs and GLMs, whose performance was better than the single modalities.</p>

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Multimodal models based on radiomics and deep learning in the classification of gastric stromal tumors and gastric leiomyomas

  • 期刊论文

  • 发表日期：

    2026年4月20日

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  • Guoting Xu,
  • Ye Zhou,
  • Lihe Liu,
  • Zhaoyang Zou,
  • Zixiang Zhu,
  • Jinzhou Zhu,
  • Airong Wu

摘要

Objective

To establish and validate a multimodal model based on radiomics and deep learning for classifying gastric stromal tumors (GSTs) and gastric leiomyomas (GLMs).

Methods

The study collected clinical case data of 200 patients (137 GSTs and 63 GLMs) hospitalized for gastric submucosal tumors at the First Affiliated Hospital of Soochow University. The collected data included white light endoscopy, ultrasound endoscopic images, computed tomography (CT) images, records of endoscopic intervention, and pathological results. The patients were randomly divided into a training set (134 cases) and a validation set (66 cases) at a ratio of 7:3. A radiomics model was constructed by extracting radiomic features from three-dimensional CT images using XGBoost algorithm, while a deep learning model was developed based on convolutional neural network to extract deep features. Finally, clinical features and the predictions from the models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. Area under the curve (AUC), sensitivity, specificity, false-negative rate (FNR), decision curve analysis net benefit, calibration, and tenfold stratified cross-validation stability assessed model performance.

Results

In the multimodal model for the classification of GSTs and GLMs, the prediction of the deep learning model was the most important variable, followed by tumor echo homogeneity, age, the prediction of the radiomics model, and major axis of tumor. The multimodal model achieved excellent AUCs (0.927 in the training set, 0.882 in the validation set), outperforming both radiomics and deep learning models. It exhibited favorable calibration, minimal overfitting, a low FNR, and stable performance via tenfold cross-validation.

Conclusion

Based on the integration of clinical tabular data, radiomics, and deep learning features, the multimodal model could classify GSTs and GLMs, whose performance was better than the single modalities.