Evidence map›Paper›PMID 41531733›Full record

ArticleiScience2026

Fully automated Res3DNet model to predict IDH mutation of gliomas from whole-brain MRI free of tumor segmentation.

Zhengyang Zhu, Yuying Liu, Yang Song, Jianan Zhou, Sixuan Chen, Meiping Ye, Zhiqiang Zhang, Jiaming Lu, Xin Li, Xinru Xu and 11 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

21 authors.

Zhengyang ZhuDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Yuying LiuNational Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, China.
Yang SongDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Jianan ZhouDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Sixuan ChenDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Meiping YeDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Zhiqiang ZhangDepartment of Diagnostic Radiology, Affiliated Jinling Hospital, Medical School of Nanjing University, Nanjing 210002, China.
Jiaming LuDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Xin LiDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Xinru XuDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Cong LongDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Linqing FuDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Yajing ZhuDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Xu YangDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Weiping LiDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Yijun BaiDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Shunshun DuDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Huiquan YangDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Xin ZhangDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.
Wu-Jun LiNational Key Laboratory for Novel Software Technology, Department of Computer Science and Technology, Nanjing University, Nanjing, China.
Bing ZhangDepartment of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate prediction of IDH mutation status in gliomas is critical for guiding diagnosis, prognosis, and treatment planning. We enrolled 2,537 preoperative MRI of glioma patients (mean age 55.91 ± 14.79, 1,063 females) from 11 different datasets, consisting of 1,382 patients (mean age 58.26 ± 14.38, 548 females) in training set, 346 patients (mean age 57.43 ± 14.04, 141 females) in internal validation set, and 809 patients (mean age 53.92 ± 14.04, 374 females) in external test set, including 242 patients from The Cancer Genome Archive (TCGA) dataset. A fully automated Res3DNet model was established for isocitrate dehydrogenase (IDH) gene prediction. Four radiologists also read images from TCGA test dataset as a comparison with the deep learning model. Our Res3DNet model achieved AUCs of 0.946 (internal validation), 0.872 (external test), and 0.912 (TCGA test), with corresponding accuracies of 0.925, 0.806, and 0.840, respectively, outperforming ResNet model, I3D model, transformer model, and four radiologists.

Indexed as

artificial intelligenceneurologyoncology

Identifiers

PMID41531733
PMCPMC12794414

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.