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Purpose <p>Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydrocephalus risk stratification.</p> Methods <p>We analyzed patients under 18 years old with medulloblastoma treated at Zhujiang Hospital from January 2015 to September 2024, randomly split 7:3 into training and validation sets. Using ResNet-18 model, we extracted quantitative histopathological features from H&amp;E-stained slides. We developed three logistic regression models to predict postoperative persistent hydrocephalus: (1) clinical model, (2) pathomic model, and (3) multimodal model (clinical + pathomics features). Model performance was evaluated using ROC and precision-recall curves. Risk group stratification was assessed with Kaplan–Meier plots and log-rank tests.</p> Results <p>A total of 90 patients was included in the study, 26(28.8%) patients required CSF diversion due to permanent hydrocephalus following tumor resection. On the validation set, the multimodal model achieved the highest predictive performance with an AUC of 0.849 (95% CI: 0.622-1.000) and average precision of 0.816 (95% CI: 0.535-1.000). The multimodal model improved risk-group stratification (3-year hydrocephalus-free survival for predicted high-risk: 37.7% versus low-risk: 89.5%, <i>P</i> &lt; 0.001).</p> Conclusion <p>Integrating deep learning-derived pathomic features with clinical variables significantly improves postoperative hydrocephalus risk stratification in pediatric medulloblastoma patients, facilitating personalized management strategies and ultimately enhancing patient outcomes.</p>

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Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma

  • 期刊论文

  • 发表日期：

    2025年10月31日

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  • Weitao Zhong,
  • Zelin Li,
  • Siyuan Lv,
  • Guanqiao Chen,
  • Yi Hao,
  • Qiang Wang,
  • Yu Wang,
  • Wangming Zhang

摘要

Purpose

Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydrocephalus risk stratification.

Methods

We analyzed patients under 18 years old with medulloblastoma treated at Zhujiang Hospital from January 2015 to September 2024, randomly split 7:3 into training and validation sets. Using ResNet-18 model, we extracted quantitative histopathological features from H&E-stained slides. We developed three logistic regression models to predict postoperative persistent hydrocephalus: (1) clinical model, (2) pathomic model, and (3) multimodal model (clinical + pathomics features). Model performance was evaluated using ROC and precision-recall curves. Risk group stratification was assessed with Kaplan–Meier plots and log-rank tests.

Results

A total of 90 patients was included in the study, 26(28.8%) patients required CSF diversion due to permanent hydrocephalus following tumor resection. On the validation set, the multimodal model achieved the highest predictive performance with an AUC of 0.849 (95% CI: 0.622-1.000) and average precision of 0.816 (95% CI: 0.535-1.000). The multimodal model improved risk-group stratification (3-year hydrocephalus-free survival for predicted high-risk: 37.7% versus low-risk: 89.5%, P < 0.001).

Conclusion

Integrating deep learning-derived pathomic features with clinical variables significantly improves postoperative hydrocephalus risk stratification in pediatric medulloblastoma patients, facilitating personalized management strategies and ultimately enhancing patient outcomes.