Evidence map›Paper›PMID 42397516›Full record

ArticleInternational urology and nephrology2026

Multiparametric MRI-derived radiomic signatures enable noninvasive prediction of HER2 expression in bladder cancer.

Yali Wang, Tonglei Zhao, Baotai Liang, Lan Zhen, Yiming Ding, Yanji Jiang, Jianping Wu, Yuan Meng, Weipu Mao, Ming Chen

Abstract read
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Article in International urology and nephrology, 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

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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.

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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

10 authors.

Yali Wang *Nanjing Medical University, Nanjing, 211166, China.
Tonglei Zhao *Southeast University School of Medicine, Nanjing, 210003, China.
Baotai LiangSoutheast University School of Medicine, Nanjing, 210003, China.
Lan ZhenSoutheast University School of Medicine, Nanjing, 210003, China.
Yiming DingSoutheast University School of Medicine, Nanjing, 210003, China.
Yanji JiangSoutheast University School of Medicine, Nanjing, 210003, China.
Jianping WuDepartment of Urology, Nanjing Lishui District People's Hospital, Zhongda Hospital Lishui Branch of Southeast University, No.86 Chongwen Road, Yong Yang Street, Lishui District, Nanjing, 211200, China.
Yuan MengDepartment of Urology, Nanjing Lishui District People's Hospital, Zhongda Hospital Lishui Branch of Southeast University, No.86 Chongwen Road, Yong Yang Street, Lishui District, Nanjing, 211200, China. lishumy03@163.com.
Weipu MaoDepartment of Urology, Affiliated Zhongda Hospital of Southeast University, No. 87Dingjiaqiao, Gulou District, Nanjing, 210009, China. maoweipu88@163.com.
Ming ChenNanjing Medical University, Nanjing, 211166, China. mingchenseu@126.com.

Funding

the Jiangsu Provincial Key Discipline and Laboratory Construction Funds of Urology 2023YXZDXK02the National Clinical Key Discipline Construction Funds CZXM-ZK-47
6 · The paper itself

Abstract

objectiveTo assess the value of multiparametric MRI (mpMRI)-derived radiomic signatures and a combined model for non-invasive prediction of human epidermal growth factor receptor 2 (HER2) expression in bladder cancer (BCa).

methodsA total of 113 BCa patients with preoperative pelvic mpMRI were retrospectively enrolled. Radiomic features were extracted from T2WI, DWI, DCE and their combinations. Five machine learning algorithms were used to construct radiomic models. A combined model and a nomogram were developed by integrating radiomic signatures and clinicoradiological variables.

resultsThe T2WI + DWI + DCE-based RandomForest model achieved the best performance, with an AUC of 0.877 in the training cohort and 0.754 in the validation cohort. Age, risk group, and maximum tumor diameter were independent predictors of HER2 overexpression. The combined model yielded AUCs of 0.808 and 0.870 in the training and validation cohorts, respectively.

conclusionmpMRI radiomics can non-invasively predict HER2 expression in BCa. The combined nomogram shows good clinical utility, supporting personalized treatment planning for BCa patients.

Indexed as

Bladder cancerHER2Multiparametric magnetic resonance imagingRadiomicsUrothelial carcinoma

Identifiers

PMID42397516

What OpenQuestion holds

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Registered trials

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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.