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Objective <p>To evaluate hippocampal subfield radiomics for identifying aggressive behavior in hospitalized patients with schizophrenia and to validate the incremental benefit and interpretability of a combined model integrating clinical variables, whole-brain structural MRI information, and hippocampal subfield radiomics.</p> Methods <p>This retrospective single-center cohort included 247 hospitalized patients with schizophrenia, randomly split (7:3) into training and test sets using outcome-stratified sampling. Aggression during hospitalization before discharge was assessed with the Modified Overt Aggression Scale (MOAS); clinically significant aggression was defined as a weighted total score ≥4. Whole-brain gray matter volume (GMV) features and hippocampal subfield radiomics features segmented using the FreeSurfer pipeline were each subjected to maximum relevance minimum redundancy (mRMR) and 10-fold least absolute shrinkage and selection operator (LASSO) for feature selection to derive sMRI-Radscore and Hip-Radscore, respectively. Three extreme gradient boosting (XGBoost) models were compared. Discrimination, incremental value, calibration, and net benefit were evaluated using area under the curve (AUC), net reclassification improvement/integrated discrimination improvement (NRI/IDI), Brier score, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was used to interpret the combined model.</p> Results <p>Five GMV and nine hippocampal subfield radiomics features were retained. sMRI-Radscore showed lower discrimination (AUC = 0.681) than Hip-Radscore (AUC = 0.729). The combined model achieved the highest AUC (0.875), outperforming both single models, with the lowest Brier score (0.119) and higher net benefit across most threshold probabilities. SHAP indicated Hip-Radscore as the top contributor. Hip-Radscore and its key constituent features were significantly associated with aggression severity.</p> Conclusion <p>Hip-Radscore may help identify aggressive behavior risk in hospitalized patients with schizophrenia, and the combined model showed improved discriminative performance. These findings provide imaging-based evidence for early risk stratification and management during hospitalization.</p>

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Hippocampal subfield radiomics improves identification of in-hospital aggressive behavior in schizophrenia: an interpretable machine-learning study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Jin-xin Wang,
  • Ran-chao Wang,
  • Feng-ling Xu,
  • Yang Li,
  • Yu Yang,
  • Yue-feng Li

摘要

Objective

To evaluate hippocampal subfield radiomics for identifying aggressive behavior in hospitalized patients with schizophrenia and to validate the incremental benefit and interpretability of a combined model integrating clinical variables, whole-brain structural MRI information, and hippocampal subfield radiomics.

Methods

This retrospective single-center cohort included 247 hospitalized patients with schizophrenia, randomly split (7:3) into training and test sets using outcome-stratified sampling. Aggression during hospitalization before discharge was assessed with the Modified Overt Aggression Scale (MOAS); clinically significant aggression was defined as a weighted total score ≥4. Whole-brain gray matter volume (GMV) features and hippocampal subfield radiomics features segmented using the FreeSurfer pipeline were each subjected to maximum relevance minimum redundancy (mRMR) and 10-fold least absolute shrinkage and selection operator (LASSO) for feature selection to derive sMRI-Radscore and Hip-Radscore, respectively. Three extreme gradient boosting (XGBoost) models were compared. Discrimination, incremental value, calibration, and net benefit were evaluated using area under the curve (AUC), net reclassification improvement/integrated discrimination improvement (NRI/IDI), Brier score, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) was used to interpret the combined model.

Results

Five GMV and nine hippocampal subfield radiomics features were retained. sMRI-Radscore showed lower discrimination (AUC = 0.681) than Hip-Radscore (AUC = 0.729). The combined model achieved the highest AUC (0.875), outperforming both single models, with the lowest Brier score (0.119) and higher net benefit across most threshold probabilities. SHAP indicated Hip-Radscore as the top contributor. Hip-Radscore and its key constituent features were significantly associated with aggression severity.

Conclusion

Hip-Radscore may help identify aggressive behavior risk in hospitalized patients with schizophrenia, and the combined model showed improved discriminative performance. These findings provide imaging-based evidence for early risk stratification and management during hospitalization.