Journal of neuro-oncology · 2025
Multimodal pathomics and clinical features predict postresection permanent hydrocephalus in pediatric medulloblastoma.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
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Zhong Weitao, Li Zelin, Lv Siyuan, Chen Guanqiao, Hao Yi, Wang Qiang, Wang Yu, Zhang Wangming
Аннотация
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.
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.