Evidence map›Paper›PMID 41171470›Full record

ArticleJournal of neuro-oncology2025

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

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

Abstract readValidation Study
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In one paragraph

Article in Journal of neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Weitao Zhong *Neurosurgery Center, Department of Pediatric Neurosurgery, The National Key Clinical Specialty, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Zelin Li *Neurosurgery Center, Department of Pediatric Neurosurgery, The National Key Clinical Specialty, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Siyuan LvNeurosurgery Center, Department of Pediatric Neurosurgery, The National Key Clinical Specialty, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Guanqiao ChenDepartment of Gynecologic Oncology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan, Hubei, 430071, China.
Yi HaoNeurosurgery Center, Department of Pediatric Neurosurgery, The National Key Clinical Specialty, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Qiang WangNeurosurgery Center, Department of Pediatric Neurosurgery, The National Key Clinical Specialty, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China.
Yu WangDepartment of Pathology, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China. doctorwylh@163.com.
Wangming ZhangNeurosurgery Center, Department of Pediatric Neurosurgery, The National Key Clinical Specialty, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou, 510280, China. wzhang@vip.126.com.

Funding

President Foundation of Zhujiang Hospital, Southern Medical University No. yzjj2023zb23
6 · The paper itself

Abstract

purposePredicting 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.

methodsWe 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.

resultsA 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).

conclusionIntegrating 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.

Indexed as

Cerebellar NeoplasmsHydrocephalusMedulloblastomaNeurosurgical ProceduresPostoperative ComplicationsAdolescentAftercareCerebrospinal Fluid ShuntsChildChild, PreschoolDeep LearningFemaleFollow-Up StudiesHumansLogistic ModelsMaleHydrocephalusMedulloblastomaMultimodalPathomic

Identifiers

PMID41171470

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