Evidence map›Paper›PMID 35716369›Full record

ArticleNeuro-oncology2023

Clinical significance and molecular annotation of cellular morphometric subtypes in lower-grade gliomas discovered by machine learning.

Xiao-Ping Liu, Xiaoqing Jin, Saman Seyed Ahmadian, Xu Yang, Su-Fang Tian, Yu-Xiang Cai, Kuldeep Chawla, Antoine M Snijders, Yankai Xia, Paul J van Diest and 5 more

Open access · greenAbstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuro-oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 pooled it
7.5field-weighted citation impact, top 2% of its field
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

34 citing papers in PubMed, 2 syntheses or guidelines pooled it, 56 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Trial
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Value of artificial intelligence in neuro-oncology.The Lancet. Digital health · 2025
    Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. 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

15 authors at 6 institutions in 3 countries.

Xiao-Ping LiuBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Xiaoqing JinBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Saman Seyed AhmadianDepartment of Pathology, Stanford University Medical Center, Stanford, California, USA.
Xu YangBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Su-Fang TianDepartment of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Yu-Xiang CaiDepartment of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Kuldeep ChawlaBerkeley Biomedical Data Science Center, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Antoine M SnijdersBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Yankai XiaKey Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.ORCID 0000-0003-0484-4035
Paul J van DiestDepartment of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0003-0658-2745
William A WeissDepartments of Neurology, Neurological Surgery, and Pediatrics, University of California, San Francisco, San Francisco, California, USA.
Jian-Hua MaoBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Zhi-Qiang LiDepartment of Neurosurgery, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Hannes VogelDepartment of Pathology, Stanford University Medical Center, Stanford, California, USA.
Hang ChangBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.ORCID 0000-0002-3773-6818
Lawrence Berkeley National Laboratory · USWuhan University · CNStanford Medicine · USNanjing Medical University · CNUniversity Medical Center Utrecht · NLUniversity of California, San Francisco · US

Funding

Molecular and Therapeutic Basis of Morphometric Aberrations in Brain TumorsR01CA184476 · NCI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI CHANG, HANG · 2015 to 2019
$2.0M
NCI NIH HHS R01 CA184476
6 · The paper itself

Abstract

backgroundLower-grade gliomas (LGG) are heterogeneous diseases by clinical, histological, and molecular criteria. We aimed to personalize the diagnosis and therapy of LGG patients by developing and validating robust cellular morphometric subtypes (CMS) and to uncover the molecular signatures underlying these subtypes.

methodsCellular morphometric biomarkers (CMBs) were identified with artificial intelligence technique from TCGA-LGG cohort. Consensus clustering was used to define CMS. Survival analysis was performed to assess the clinical impact of CMBs and CMS. A nomogram was constructed to predict 3- and 5-year overall survival (OS) of LGG patients. Tumor mutational burden (TMB) and immune cell infiltration between subtypes were analyzed using the Mann-Whitney U test. The double-blinded validation for important immunotherapy-related biomarkers was executed using immunohistochemistry (IHC).

resultsWe developed a machine learning (ML) pipeline to extract CMBs from whole-slide images of tissue histology; identifying and externally validating robust CMS of LGGs in multicenter cohorts. The subtypes had independent predicted OS across all three independent cohorts. In the TCGA-LGG cohort, patients within the poor-prognosis subtype responded poorly to primary and follow-up therapies. LGGs within the poor-prognosis subtype were characterized by high mutational burden, high frequencies of copy number alterations, and high levels of tumor-infiltrating lymphocytes and immune checkpoint genes. Higher levels of PD-1/PD-L1/CTLA-4 were confirmed by IHC staining. In addition, the subtypes learned from LGG demonstrate translational impact on glioblastoma (GBM).

conclusionsWe developed and validated a framework (CMS-ML) for CMS discovery in LGG associated with specific molecular alterations, immune microenvironment, prognosis, and treatment response.

Indexed as

Brain NeoplasmsGliomaArtificial IntelligenceClinical RelevanceHumansMachine LearningTumor Microenvironmentcellular morphometric biomarkerscellular morphometric subtypesglioblastomaimmunohistochemistrylower-grade gliomanomogramoverall survivalstacked predictive sparse decomposition

Identifiers

PMID35716369
PMCPMC9825346
OpenAlexW4283078991

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.