Evidence map›Paper›PMID 41420667›Full record

ArticleDiscover oncology2025

Innovative AI model for bladder cancer diagnosis.

Lei Jiang, Wenyu Ge, Ruijiao Feng, Liu Ji, Jingru Huo, Shijie Li, Tingting Fan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2025. 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
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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

7 authors.

Lei Jiang *Department of Pathology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Wenyu Ge *Harbin Institute of Technology, Heilongjiang Provincial Hospital, Harbin, China.
Ruijiao FengHeilongjiang University of Chinese Medicine, Harbin, China.
Liu JiDepartment of Cardiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Jingru HuoDepartment of Pathology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
Shijie LiDepartment of Interventional Radiology, Harbin Medical University Cancer Hospital, Harbin, China.
Tingting FanDepartment of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150001, China. tingtingfan2020@163.com.

Funding

Heilongjiang provincial scientific research project of traditional Chinese Medicine ZHY2024-041Postdoctoral Scientific Research Development Fund of Heilongjiang Province LBH-Z24304scientific research of the Heilongjiang Provincial Health Commission 20240404050045the scientific research of the Heilongjiang Provincial Health Commission 20240909010037
6 · The paper itself

Abstract

objectivesBladder cancer is one of the most common malignancies of the urinary system, and early diagnosis and treatment are crucial for improving patient prognosis.

methodsIn this study, we developed an artificial intelligence (AI) model for bladder cancer diagnosis using CT imaging data from our hospital and The Cancer Imaging Archive (TCIA). The model was trained and validated using a large dataset of CT images, and its diagnostic accuracy was assessed through various performance metrics. The AI model was constructed using deep learning techniques, which can automatically learn and extract features from CT images. Retrospective CT data from our hospital and TCIA were used to train the AI model. Performance metrics were evaluated, and the Grad-CAM results were reviewed by radiologists.

resultsThe test-set accuracy exceeded 0.90, and Grad-CAM correctly highlighted tumors.

conclusionsThe AI model offers accurate and transparent bladder-cancer detection on routine CT scans and merits prospective validation.

Indexed as

Artificial intelligenceBladder cancerDiagnosis

Identifiers

PMID41420667
PMCPMC12830522

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