Evidence map›Paper›PMID 41612348›Full record

ArticleJournal of translational medicine2026

Radiomics-based gradient boosting model on contrast-enhanced MRI for non-invasive prediction of epidermal growth factor receptor expression and therapeutic response to EGFR-targeted antibody-drug conjugates in high-grade glioma organoid models.

Chengbo Tan, Yujing Zhou, Shuang Li, Bin Dong, Fangjing Yu, Changchuan Bai, Linli Zhang, Yue Wang, Meiqing Lou, Xiangqian Qi and 2 more

Abstract read
In one paragraph

Article in Journal of translational 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. 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

12 authors.

Chengbo Tan *Department of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yujing Zhou *Department of Radiology Department, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
Shuang Li *Department of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
Bin DongDepartment of Neurosurgery, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
Fangjing YuDepartment of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
Changchuan BaiDepartment of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China.
Linli ZhangDepartment of Endocrinology, Shanghai Geriatric Medical Center, Shanghai, China.
Yue WangDepartment of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Meiqing LouDepartment of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiangqian QiDepartment of Neurosurgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. qixiangqian@163.com.
Xiaojie WangDepartment of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China. wxj860408@163.com.
Xiaonan CuiDepartment of Oncology, The First Affiliated Hospital to Dalian Medical University, Dalian, Liaoning, China. cxn23@sina.com.ORCID 0000-0001-9130-3813

Funding

National Natural Science Foundation of China No. 81901780Science and Technology Commission of Shanghai Municipality No. 20S11906000
6 · The paper itself

Abstract

backgroundEpidermal growth factor (EGF) and its receptor EGF(EGFR) play crucial roles in glioblastoma (GBM) prognosis. However, non-invasive assessment of their expression remains challenging. This study aimed to determine whether radiomics features extracted from contrast-enhanced MRI could predict EGFR expression in high-grade gliomas (HGG) and to explore their associations with immune infiltration and therapeutic response of EGFR-Targeted antibody drug conjugates(EGFR-ADCs).

methodsWe extracted radiomic features from contrast-enhanced MRI of 298 GBM patients from The Cancer Imaging Archive (TCIA) and matched them with RNA-seq data from The Cancer Genome Atlas (TCGA). Feature selection was performed using minimum redundancy maximum relevance (mRMR) and recursive feature elimination (RFE). Machine learning models were built to predict EGF/EGFR expression. Radiogenomic associations were validated by immune infiltration analysis. Patient-Derived Tumor-Like Cell Clusters (PTC) were used to compare the antitumor efficacy of EGFR- ADCs and temozolomide.

resultsElevated EGF/EGFR expression correlated with poor prognosis and increased infiltration of M2 macrophages, regulatory T cells, and CD4⁺ memory T cells. Pathway analysis demonstrated significant enrichment of the mechanistic target of rapamycin (mTOR) and Mitogen-Activated Protein Kinase (MAPK) signaling cascades. Radiomics-based prediction models achieved robust performance (AUC > 0.85) in stratifying EGFR expression status. In EGFR-positive tumor tissues, EGFR-ADCs exerted antitumor efficacy similar to that of temozolomide.

conclusionsEGF/EGFR expression is associated with immunosuppressive microenvironments and adverse outcomes in HGG. Radiomics may provide a non-invasive approach for estimating EGFR expression, although model performance requires external validation and EGFR-ADCs showed partial inhibitory activity within the tested range, though potency remains to be defined.These findings suggest a framework into radiogenomic stratification and targeted therapy in GBM.

Indexed as

Brain NeoplasmsContrast MediaErbB ReceptorsGliomaMagnetic Resonance ImagingModels, BiologicalRadiomicsFemaleHumansMachine LearningMaleMiddle AgedNeoplasm GradingTreatment OutcomeContrast MediaEGFR protein, humanErbB ReceptorsAntibody-drug conjugateEGFRGlioblastomaOrganoid modelRadiomics

Identifiers

PMID41612348
PMCPMC12924216

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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.