Evidence map›Paper›PMID 42086700›Full record

ArticleNPJ digital medicine2026

Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma.

Satoshi Takahashi, Masamichi Takahashi, Manabu Kinoshita, Mototaka Miyake, Risa Kawaguchi, Naoki Shinojima, Akitake Mukasa, Kuniaki Saito, Motoo Nagane, Ryohei Otani and 26 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. 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. Review
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

36 authors.

Satoshi Takahashi *Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.
Masamichi Takahashi *Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan. masataka@tokai.ac.jp.
Manabu Kinoshita *Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan.
Mototaka MiyakeDepartment of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan.
Risa KawaguchiGraduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo, Japan.
Naoki ShinojimaDepartment of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
Akitake MukasaDepartment of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
Kuniaki SaitoDepartment of Neurosurgery, Kyorin University School of Medicine, Tokyo, Japan.
Motoo NaganeDepartment of Neurosurgery, Kyorin University School of Medicine, Tokyo, Japan.
Ryohei OtaniDepartment of Neurosurgery, Dokkyo Medical University, Tochigi, Japan.
Fumi HiguchiDepartment of Neurosurgery, Dokkyo Medical University, Tochigi, Japan.
Shota TanakaDepartment of Neurosurgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Nobuhiro HataDepartment of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Kaoru TamuraDepartment of Neurosurgery, Institute of Science Tokyo, Tokyo, Japan.
Kensuke TateishiDepartment of Neurosurgery, Graduate School of Medicine, Yokohama City University, Yokohama, Japan.
Ryo NishikawaDepartment of Neuro-Oncology/Neurosurgery, Saitama Medical University International Medical Center, Saitama, Japan.
Hideyuki AritaDepartment of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan.
Masahiro NonakaDepartment of Neurosurgery, Kansai Medical University, Hirakata, Japan.
Takehiro UdaDepartment of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan.
Junya FukaiDepartment of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan.
Yoshiko OkitaDepartment of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan.
Naohiro TsuyuguchiDepartment of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan.
Yonehiro KanemuraDepartment of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan.
Fumiyasu TsushimaDepartment of Diagnostic Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.
Shingo KakedaDepartment of Diagnostic Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan.
Toshiaki AkashiDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan.
Toshiaki TaokaDepartment of Innovative Biomedical Visualization (iBMV), Graduate School of Medicine, Nagoya University, Nagoya, Japan.
Yoshiyuki WatanabeDepartment of Radiology, Shiga University of Medical Science, Otsu, Japan.
Kei YamadaDepartment of Radiology , Kyoto Prefectural University of Medicine, Kyoto, Japan.
Toshinori HiraiDepartment of Diagnostic Radiology, Kumamoto University Graduate School of Life Sciences, Kumamoto, Japan.
Minako AzumaDepartment of Radiology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan.
Takashi YoshiuraDepartment of Radiology, Shiga University of Medical Science, Otsu, Japan.
Jun SeseDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan.
Koichi IchimuraDepartment of Pathology, Kyorin University Faculty of Medicine, Tokyo, Japan.
Yoshitaka NaritaDepartment of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan.
Ryuji HamamotoDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan. rhamamot@ncc.go.jp.

Funding

Cabinet Office, Government of Japan BRIDGE (programs for bridging the gap between R&D and the ideal society (Society 5.0) and generating economic and social value)
6 · The paper itself

Abstract

Predicting isocitrate dehydrogenase (IDH) mutations in gliomas using magnetic resonance imaging (MRI) is clinically important for treatment planning. This study compared two artificial intelligence (AI) models, GliomaDepth-IDH (ResNet34-based) and GliomaVista-IDH (Vision Transformer-based), with 18 physicians (eight neuroradiologists, five neurosurgeons, and five neurosurgery residents) in predicting IDH mutation status. On the Brain Tumor Segmentation Challenge dataset, the GliomaVista-IDH AI model achieved an area under the curve (AUC) value of 0.97, significantly outperforming all physician groups. However, external validation on a Japanese cohort revealed performance degradation: GliomaDepth-IDH declined to an AUC of 0.75 and GliomaVista-IDH to 0.82, with GliomaVista-IDH showing significant calibration issues (Brier score = 0.32). High-performing physicians achieved comparable results (AUC = 0.88) with superior calibration (Brier score = 0.19). Inter-rater reliability analysis revealed substantial variability across physician groups. These findings suggest that AI models can assist many physicians, while experienced practitioners remain competitive with better-calibrated predictions in challenging domains.

Identifiers

PMID42086700
PMCPMC13351068

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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.