Evidence map›Paper›PMID 42253972›Full record

ArticleFrontiers in immunology2026

AI-based pathomics model predicts regulatory T cell infiltration and radiotherapy response in IDH-wild-type glioblastoma.

Shaoli Peng, Jialei Chen, Xuezhen Wang, Xingfu Wang, Qiuyuan Yue, Hailin Lan, Jingru Zhang, Yuqi Xie, Meiyan Yang, Jiayu Xiao and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 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.

Shaoli Peng *Department of Radiotherapy, Cancer Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Jialei Chen *Department of Radiotherapy, Cancer Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Xuezhen Wang *Department of Radiotherapy, Cancer Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Xingfu Wang *Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Qiuyuan Yue *Department of Radiology, Fujian Cancer Hospital and Fujian Medical University Cancer Hospital, Fuzhou, China.
Hailin LanKey Laboratory of Radiation Biology of Fujian Higher Education Institutions, The First Affiliated Hospital, Fujian Medical University, Fuzhou, China.
Jingru ZhangSchool of Medical Imaging, Fujian Medical University, Fuzhou, China.
Yuqi XieSchool of Medical Imaging, Fujian Medical University, Fuzhou, China.
Meiyan YangSchool of Medical Imaging, Fujian Medical University, Fuzhou, China.
Jiayu XiaoSchool of Medical Imaging, Fujian Medical University, Fuzhou, China.
Chenyan GuoSchool of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.
Yang WangDepartment of Radiation Oncology, Huashan Hospital, Fudan University, Shanghai, China.
Zanyi WuDepartment of Neurosurgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Jinsheng HongDepartment of Radiotherapy, Cancer Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
Mingwei ZhangDepartment of Radiotherapy, Cancer Center, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Regulatory T cells (Tregs) contribute significantly to immune suppression and therapy resistance in isocitrate dehydrogenase (IDH)-wild-type glioblastoma (GBM), a highly aggressive brain tumor with poor prognosis. Methods: In this study, we developed an artificial intelligence (AI)-powered pathomics model to predict Treg infiltration and stratify prognosis in GBM patients undergoing radiotherapy. Using high-dimensional features extracted from hematoxylin and eosin-stained biopsies, we constructed a pathomics score (PS) via gradient boosting after feature selection with Minimum Redundancy Maximum Relevance (mRMR) and Relief algorithms. Results: The model demonstrated strong predictive performance across multi-center cohorts (n > 300), where high PS was significantly associated with elevated Treg levels and reduced overall survival (TCGA: HR = 2.16; validation cohort: HR = 1.706). Gene set enrichment analysis linked high PS to immune-evasive pathways, including Notch and IL-6/JAK/STAT3 signaling, along with increased expression of DNA repair gene RAD50, suggesting a potential association with radiotherapy response. Conclusion: This AI-based pathomics framework offers a robust and interpretable tool for immunoprofiling and outcome prediction, paving the way for precision radiotherapy and Treg-targeted therapeutic strategies in glioblastoma.

Indexed as

Artificial IntelligenceBrain NeoplasmsGlioblastomaLymphocytes, Tumor-InfiltratingT-Lymphocytes, RegulatoryHumansIsocitrate DehydrogenasePrognosisTumor MicroenvironmentIsocitrate Dehydrogenaseartificial intelligenceglioblastomaimmunotherapy biomarkersmachine learningpathomicsradiotherapy responseregulatory T celltumor microenvironment

Identifiers

PMID42253972
PMCPMC13233443

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.