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Background <p>Accurate prediction of human epidermal growth factor receptor 2 (HER2) status is critical for personalized treatment strategies in gastric cancer (GC). This study aimed to develop and validate HER2 status prediction models integrating clinical and radiomics features to enhance predictive accuracy.</p> Methods <p>We retrospectively analyzed data from GC patients to identify independent predictors of HER2 status using multivariate logistic regression (LR). A radiomics model was constructed using five machine learning classifiers, with feature selection and Rad-score calculation. The combined model incorporated clinical predictors and Rad-score via LR. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).</p> Results <p>Pathological T stage, pathologic N stage, and CEA levels were identified as independent predictors of HER2 status (all <i>p</i> &lt; 0.05). The support vector machine (SVM)-based radiomics model achieved the highest AUC (0.823) in the testing set. The combined model demonstrated superior predictive performance in both the training (AUC = 0.889) and testing (AUC = 0.826) cohorts compared to clinical model alone.</p> Conclusions <p>The integration of CT-based radiomics with clinical factors significantly improved the prediction of HER2 status in GC, outperforming models based on either data type alone.</p> Trial registration <p>Not applicable (retrospective study).</p>

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Comparison of CT-based radiomics models for the prediction of human epidermal growth factor receptor 2 status in gastric cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月9日

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  • Xiaolian Wang,
  • Situ Xiong,
  • Pei Huang,
  • Yingying Qiu,
  • Zonghuo Wang,
  • Hao Liu,
  • Bing Fan,
  • Wentao Dong

摘要

Background

Accurate prediction of human epidermal growth factor receptor 2 (HER2) status is critical for personalized treatment strategies in gastric cancer (GC). This study aimed to develop and validate HER2 status prediction models integrating clinical and radiomics features to enhance predictive accuracy.

Methods

We retrospectively analyzed data from GC patients to identify independent predictors of HER2 status using multivariate logistic regression (LR). A radiomics model was constructed using five machine learning classifiers, with feature selection and Rad-score calculation. The combined model incorporated clinical predictors and Rad-score via LR. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

Results

Pathological T stage, pathologic N stage, and CEA levels were identified as independent predictors of HER2 status (all p < 0.05). The support vector machine (SVM)-based radiomics model achieved the highest AUC (0.823) in the testing set. The combined model demonstrated superior predictive performance in both the training (AUC = 0.889) and testing (AUC = 0.826) cohorts compared to clinical model alone.

Conclusions

The integration of CT-based radiomics with clinical factors significantly improved the prediction of HER2 status in GC, outperforming models based on either data type alone.

Trial registration

Not applicable (retrospective study).