Evidence map›Paper›PMID 39812752›Full record

ArticleInsights into imaging2025

Evaluating the feasibility of AI-predicted bpMRI image features for predicting prostate cancer aggressiveness: a multi-center study.

Kexin Wang, Ning Luo, Zhaonan Sun, Xiangpeng Zhao, Lilan She, Zhangli Xing, Yuntian Chen, Chunlei He, Pengsheng Wu, Xiangpeng Wang and 1 more

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In one paragraph

Article in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
–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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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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

11 authors.

Kexin Wang *School of Basic Medical Sciences, Capital Medical University, Beijing, 100069, China.
Ning Luo *Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Zhaonan SunDepartment of Radiology, Peking University First Hospital, Beijing, 100034, China.
Xiangpeng ZhaoDepartment of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Lilan SheDepartment of Radiology, Fujian Medical University Union Hospital, No. 29, Xin Quan Road, Gulou District, Fuzhou, 350001, Fujian Province, China.
Zhangli XingDepartment of Radiology, Fujian Medical University Union Hospital, No. 29, Xin Quan Road, Gulou District, Fuzhou, 350001, Fujian Province, China.
Yuntian ChenDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
Chunlei HeDepartment of Radiology, West China Hospital, Sichuan University, Chengdu, 610041, China.
Pengsheng WuBeijing Smart Tree Medical Technology Co. Ltd., No. 97, Changping Road, Shahe Town, Changping District, Beijing, 102200, China.
Xiangpeng WangBeijing Smart Tree Medical Technology Co. Ltd., No. 97, Changping Road, Shahe Town, Changping District, Beijing, 102200, China.
ZiXuan KongDepartment of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China. Drzxkong@163.com.ORCID http://orcid.org/0009-0007-1135-660X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa). MATERIALS AND

methodsA total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development. The study evaluated five prediction methods, including (1) A clinical-imaging model of clinical features and image features of suspected PCa lesions selected by AI algorithm, (2) the PIRADS category, (3) a conventional radiomics model, (4) a deep-learning bases radiomics model, and (5) biopsy pathology.

resultsIn the externally validated dataset, the deep learning-based radiomics model showed the highest area under the curve (AUC 0.700 to 0.791). It exceeded the clinical-imaging model (AUC 0.597 to 0.718), conventional radiomic model (AUC 0.566 to 0.632), PIRADS score (AUC 0.554 to 0.613), and biopsy pathology (AUC 0.537 to 0.578). The AUC predicted by the model did not show a statistically significant difference among the three externally verified hospitals (p > 0.05).

conclusionDeep-learning radiomics models utilizing AI-extracted image features from bpMRI images can potentially be used to predict PCa aggressiveness, demonstrating a generalized ability for external validation. CRITICAL RELEVANCE STATEMENT: Predicting the aggressiveness of prostate cancer (PCa) is important for formulating the best treatment plan for patients. The radiomic model based on deep learning is expected to provide an objective and non-invasive method for evaluating the aggressiveness of PCa. KEY POINTS: Predicting the aggressiveness of PCa is important for patients to obtain the best treatment options. The deep learning-based radiomics model can predict the aggressiveness of PCa with high accuracy. The model has good universality when tested on multiple external datasets.

Indexed as

Deep learningGleason scoreMultiparametric magnetic resonance imagingProstate cancerRadiomics

Identifiers

PMID39812752
PMCPMC11735704

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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.