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

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Paper

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The authors' code

JavaScript · 32 lines · 834 B · no license

  1. const Path = require('path');
  2. const Chalk = require('chalk');
  3. const FileSystem = require('fs');
  4. const Vite = require('vite');
  5. const compileTs = require('./private/tsc');
  7. function buildRenderer() {
  8. return Vite.build({
  9. configFile: Path.join(__dirname, '..', 'vite.config.js'),
  10. base: './',
  11. mode: 'production'
  12. });
  13. }
  15. function buildMain() {
  16. const mainPath = Path.join(__dirname, '..', 'src', 'main');
  17. return compileTs(mainPath);
  18. }
  20. FileSystem.rmSync(Path.join(__dirname, '..', 'build'), {
  21. recursive: true,
  22. force: true,
  23. })
  25. console.log(Chalk.blueBright('Transpiling renderer & main...'));
  27. Promise.allSettled([
  28. buildRenderer(),
  29. buildMain(),
  30. ]).then(() => {
  31. console.log(Chalk.greenBright('Renderer & main successfully transpiled! (ready to be built with electron-builder)'));
  32. });

build.js at commit 2cf8d9e, no license · at the source

Overview

Authors: Satoshi Takahashi1,2, Masamichi Takahashi3,4, Manabu Kinoshita5, Mototaka Miyake6, Risa Kawaguchi7, Naoki Shinojima8, Akitake Mukasa8, Kuniaki Saito9, Motoo Nagane9, Ryohei Otani10,11, Fumi Higuchi10, Shota Tanaka12,13, Nobuhiro Hata14, Kaoru Tamura15, Kensuke Tateishi16, Ryo Nishikawa17, Hideyuki Arita18, Masahiro Nonaka19,20, Takehiro Uda21, Junya Fukai22
and 16 other authorsYoshiko Okita20,23, Naohiro Tsuyuguchi21,24, Yonehiro Kanemura20,25, Fumiyasu Tsushima26, Shingo Kakeda26, Toshiaki Akashi27, Toshiaki Taoka28, Yoshiyuki Watanabe29, Kei Yamada30, Toshinori Hirai31, Minako Azuma32, Takashi Yoshiura29,33, Jun Sese1,34, Koichi Ichimura35, Yoshitaka Narita3, Ryuji Hamamoto1,2
35 affiliations
  1. Division of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, Japan
  2. AI Medical Engineering Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
  3. Department of Neurosurgery and Neuro-Oncology, National Cancer Center Hospital, Tokyo, Japan
  4. Department of Neurosurgery, Tokai University School of Medicine, Isehara, Japan
  5. Department of Neurosurgery, Asahikawa Medical University, Asahikawa, Japan
  6. Department of Diagnostic Radiology, National Cancer Center Hospital, Tokyo, Japan
  7. Graduate School of Pharmaceutical Sciences, the University of Tokyo, Tokyo, Japan
  8. Department of Neurosurgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
  9. Department of Neurosurgery, Kyorin University School of Medicine, Tokyo, Japan
  10. Department of Neurosurgery, Dokkyo Medical University, Tochigi, Japan
  11. Department of Neurosurgery, Tokyo Metropolitan Komagome Hospital, Tokyo, Japan
  12. Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
  13. Department of Neurological Surgery, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama, Japan
  14. Department of Neurosurgery, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
  15. Department of Neurosurgery, Institute of Science Tokyo, Tokyo, Japan
  16. Department of Neurosurgery, Graduate School of Medicine, Yokohama City University, Yokohama, Japan
  17. Department of Neuro-Oncology/Neurosurgery, Saitama Medical University International Medical Center, Saitama, Japan
  18. Department of Neurosurgery, Osaka University Graduate School of Medicine, Suita, Japan
  19. Department of Neurosurgery, Kansai Medical University, Hirakata, Japan
  20. Department of Neurosurgery, NHO Osaka National Hospital, Osaka, Japan
  21. Department of Neurosurgery, Osaka Metropolitan University Graduate School of Medicine, Osaka, Japan
  22. Department of Neurological Surgery, Wakayama Medical University School of Medicine, Wakayama, Japan
  23. Department of Neurosurgery, Osaka International Cancer Institute, Osaka, Japan
  24. Department of Neurosurgery, Naniwaikuno Hospital, Osaka, Japan
  25. Department of Biomedical Research and Innovation, Institute for Clinical Research, NHO Osaka National Hospital, Osaka, Japan
  26. Department of Diagnostic Radiology, Hirosaki University Graduate School of Medicine, Hirosaki, Japan
  27. Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
  28. Department of Innovative Biomedical Visualization (iBMV), Graduate School of Medicine, Nagoya University, Nagoya, Japan
  29. Department of Radiology, Shiga University of Medical Science, Otsu, Japan
  30. Department of Radiology, Kyoto Prefectural University of Medicine, Kyoto, Japan
  31. Department of Diagnostic Radiology, Kumamoto University Graduate School of Life Sciences, Kumamoto, Japan
  32. Department of Radiology, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan
  33. Department of Radiology, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
  34. Humanome Lab Inc., Tokyo, Japan
  35. Department of Pathology, Kyorin University Faculty of Medicine, Tokyo, Japan
Institutions: National Cancer Centre Japan (Japan); RIKEN Center for Advanced Intelligence Project (Japan); Tokai University (Japan); Asahikawa Medical University (Japan); The University of Tokyo (Japan); Kumamoto University (Japan); Kyorin University (Japan); Tokyo Metropolitan Komagome Hospital (Japan); Dokkyo Medical University (Japan); Okayama University (Japan); Kyushu University (Japan); Institute of Science Tokyo (Japan); Yokohama City University (Japan); Saitama International Medical Center (Japan); Saitama Medical University (Japan); The University of Osaka (Japan); Kansai Medical University (Japan); Osaka National Hospital (Japan); Osaka Metropolitan University (Japan); Wakayama Medical University (Japan); Osaka International Cancer Institute (Japan); Hirosaki University (Japan); Juntendo University (Japan); Nagoya University (Japan); Shiga University of Medical Science (Japan); Kyoto Prefectural University of Medicine (Japan); University of Miyazaki (Japan); Kagoshima University (Japan)
Journal: NPJ digital medicine, volume 9, issue 1, article 528
Dates: received 1 January 2026; accepted 20 April 2026; published online 5 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41746-026-02695-2 · PMID 42086700 · PMCID PMC13351068 · OpenAlex W7160319861
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other condition (population)
Methods: Connectivity, Statistics, Machine learning
Keywords: Cancer, Computational biology and bioinformatics, Medical research, Neurology, Neuroscience, Oncology
Topic: Glioma Diagnosis and Treatment (Genetics, Medicine), according to OpenAlex
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))
Citations: not cited yet (Europe PMC); 45 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above.

hamamoto-lab/HumanVSAIPub

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: 2cf8d9ee85dfa2cdf17ddf52a3d7303fd6d6ebb5, 30 April 2026
Languages: TypeScript (10), JavaScript (8)
Size: 59 files, 18 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
19 files
At the source: github.com/hamamoto-lab/HumanVSAIPub

Code availability statement

The paper has a code availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41746-026-02695-2.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 18 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1038/s41746-026-02695-2.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 36 authors, 6 keywords, 1 funder, 39 references.

Cite

This paper

Takahashi, S., Takahashi, M., Kinoshita, M., Miyake, M., Kawaguchi, R., Shinojima, N., Mukasa, A., Saito, K., Nagane, M., Otani, R., Higuchi, F., Tanaka, S., Hata, N., Tamura, K., Tateishi, K., Nishikawa, R., Arita, H., Nonaka, M., Uda, T., . . . Hamamoto, R. (2026). Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma. NPJ digital medicine, 9(1), 528. https://doi.org/10.1038/s41746-026-02695-2

BibTeX

@article{takahashi2026comparing,
author = {Takahashi, Satoshi and Takahashi, Masamichi and Kinoshita, Manabu and Miyake, Mototaka and Kawaguchi, Risa and Shinojima, Naoki and Mukasa, Akitake and Saito, Kuniaki and Nagane, Motoo and Otani, Ryohei and Higuchi, Fumi and Tanaka, Shota and Hata, Nobuhiro and Tamura, Kaoru and Tateishi, Kensuke and Nishikawa, Ryo and Arita, Hideyuki and Nonaka, Masahiro and Uda, Takehiro and Fukai, Junya and Okita, Yoshiko and Tsuyuguchi, Naohiro and Kanemura, Yonehiro and Tsushima, Fumiyasu and Kakeda, Shingo and Akashi, Toshiaki and Taoka, Toshiaki and Watanabe, Yoshiyuki and Yamada, Kei and Hirai, Toshinori and Azuma, Minako and Yoshiura, Takashi and Sese, Jun and Ichimura, Koichi and Narita, Yoshitaka and Hamamoto, Ryuji},
title = {{Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma}},
journal = {NPJ digital medicine},
year = {2026},
month = may,
volume = {9},
number = {1},
pages = {528},
publisher = {Nature Publishing Group},
issn = {2398-6352},
doi = {10.1038/s41746-026-02695-2},
url = {https://doi.org/10.1038/s41746-026-02695-2},
pmid = {42086700},
pmcid = {PMC13351068}
}

RIS

TY - JOUR
AU - Takahashi, Satoshi
AU - Takahashi, Masamichi
AU - Kinoshita, Manabu
AU - Miyake, Mototaka
AU - Kawaguchi, Risa
AU - Shinojima, Naoki
AU - Mukasa, Akitake
AU - Saito, Kuniaki
AU - Nagane, Motoo
AU - Otani, Ryohei
AU - Higuchi, Fumi
AU - Tanaka, Shota
AU - Hata, Nobuhiro
AU - Tamura, Kaoru
AU - Tateishi, Kensuke
AU - Nishikawa, Ryo
AU - Arita, Hideyuki
AU - Nonaka, Masahiro
AU - Uda, Takehiro
AU - Fukai, Junya
AU - Okita, Yoshiko
AU - Tsuyuguchi, Naohiro
AU - Kanemura, Yonehiro
AU - Tsushima, Fumiyasu
AU - Kakeda, Shingo
AU - Akashi, Toshiaki
AU - Taoka, Toshiaki
AU - Watanabe, Yoshiyuki
AU - Yamada, Kei
AU - Hirai, Toshinori
AU - Azuma, Minako
AU - Yoshiura, Takashi
AU - Sese, Jun
AU - Ichimura, Koichi
AU - Narita, Yoshitaka
AU - Hamamoto, Ryuji
TI - Comparing artificial intelligence and physician performance in predicting IDH mutation status in glioma
T2 - NPJ digital medicine
J2 - NPJ Digit Med
PY - 2026
DA - 2026/05/05
VL - 9
IS - 1
SP - 528
SN - 2398-6352
PB - Nature Publishing Group
DO - 10.1038/s41746-026-02695-2
UR - https://doi.org/10.1038/s41746-026-02695-2
LA - en
ER -

CSL-JSON

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