Evidence map›Paper›PMID 42034911›Full record

ArticleNPJ digital medicine2026

Integrating multimodal clinical data with a large model for prostate cancer diagnosis.

Chengbang Wang, Yuan Tian, Shaojie Yin, Xuhong Zhang, Xuedong Wei, Lingfeng Wu, Zhengdong Zhou, Guijian Pang, Yan Wang, Wangjian Wu and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital 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. Review
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

20 authors.

Chengbang Wang *Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yuan Tian *Shanghai Artificial Intelligence Laboratory, Shanghai, China.
Shaojie Yin *Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University, Shanghai, China.
Xuhong Zhang *Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xuedong WeiDepartment of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Lingfeng WuDepartment of Urology, The Affiliated Hospital of Jiaxing University, Jiaxing, China.
Zhengdong ZhouDepartment of Urology, Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, The First people's Hospital of Yancheng, Jiangsu, China.
Guijian PangDepartment of Urology, The First People's Hospital of Yulin (the Sixth Affiliated Hospital of Guangxi Medical University), Yulin, Guangxi, China.
Yan WangDepartment of Urology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai, China.
Wangjian WuDepartment of Urology, The Affiliated Hospital of Jiaxing University, Jiaxing, China.
Shukai ZhaoDepartment of Urology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Ziwei WangDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jiangnan XuDepartment of Urology, Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, The First people's Hospital of Yancheng, Jiangsu, China.
Hao HeDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Minglun LiDepartment of Radiation Oncology, Hospital Lüneburg, Lüneburg, Germany.
Zhankui JiaDepartment of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. jiazhankui@126.com.
Xu GaoDepartment of Urology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai, China. gaoxu.changhai@foxmail.com.
Fubo WangCenter for Genomic and Personalized Medicine, Guangxi key Laboratory for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, School of Life Science, Guangxi Medical University, Nanning, Guangxi, China. wangfubo@gxmu.edu.cn.
Guangtao ZhaiShanghai Artificial Intelligence Laboratory, Shanghai, China. zhaiguangtao@sjtu.edu.cn.
Bin XuDepartment of Urology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. chxb2004@shsmu.edu.cn.

Funding

National Key R&D Program 2024YFC3506202National Natural Science Foundation of China 62225112The Sino-German Center Call for the Mobility Programme 2020 M-0171The Yongjiang Program of Nanning 2021015
6 · The paper itself

Abstract

Accurate prostate cancer (PCa) diagnosis remains difficult because of tumor heterogeneity and the challenge of integrating multimodal clinical information. We developed Prost-LM, a multimodal large language model that jointly embeds MRI-derived features, numerical PSA values, and free-text clinical reports into a unified semantic space to enable deep cross-modal reasoning. Trained and validated on a large multi-center cohort of 3940 patients, Prost-LM achieved strong diagnostic performance, with an internal validation AUC of 0.954 for distinguishing PCa from benign conditions, outperforming MRI-only models (AUC = 0.868, P < 0.001). For detecting clinically significant PCa (Gleason score ≥ 7), Prost-LM reached an AUC of 0.955. Additionally, the model provides interpretable diagnostic decisions to support clinical verification. These results suggest Prost-LM can improve automated PCa diagnosis and support precision oncology through multimodal AI.

Identifiers

PMID42034911
PMCPMC13324722

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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.