Evidence map›Paper›PMID 42798798›Full record

ArticleMedComm2026

Integrating DCE-MRI-Based Dural Drainage Function Indicators into Machine Learning Models for Improved Intracranial Tumor Prognosis.

Lusen Ran, Wenxi Luo, Luyun You, Wenjie Wei, Yuqin He, Shabei Xu, Jiayu Zhu, Fan Long, Xiaopeng Song, Guangyuan Hu and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MedComm, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Lusen Ran *Department of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Wenxi Luo *College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.
Luyun You *Department of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Wenjie WeiDepartment of Oncology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Yuqin HeDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Shabei XuDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Jiayu ZhuCentral Research Institute United Imaging Healthcare Group Shanghai China.
Fan LongCentral Research Institute United Imaging Healthcare Group Shanghai China.
Xiaopeng SongCentral Research Institute United Imaging Healthcare Group Shanghai China.
Guangyuan HuDepartment of Oncology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Xianglin YuanDepartment of Oncology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.ORCID https://orcid.org/0000-0003-4653-5388
Wei WangDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.
Feng LuNational Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China.
Minghuan WangDepartment of Neurology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.ORCID https://orcid.org/0000-0003-2879-5741
YingYing WuDepartment of Oncology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Meningeal lymphatic vessels (mLVs) are crucial in intracranial tumor progression. This study investigated whether incorporating mLVs functional indicators into machine learning models enhances prognostic prediction for intracranial malignant tumors. We prospectively enrolled 246 patients, assessing baseline mLVs function via dynamic contrast-enhanced MRI. After 2.5 years' follow-up, 100 patients (51 survivors, 49 deceased) were finally included. The mean area under the receiver operating characteristic curve (AUROC) of the XGBoost model excluding DCE-MRI-based dural drainage function indicators (DDFIs) was 0.746 (95% CI: 0.621-0.871), which increased to 0.808 (95% CI: 0.733-0.883) with the inclusion of DDFIs. Kaplan-Meier survival curves demonstrated significantly better discrimination when DDFIs were included (

Indexed as

cohort studyintracranial malignant tumorsmachine learningmeningeal lymphatic vesselssurvival

Identifiers

PMID42798798
PMCPMC13613099

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.