Evidence map›Paper›PMID 42282124›Full record

ArticleJournal of the National Cancer Center2026

Construction and external validation of radiomics models to detect primary prostate cancer with machine learning: a multicenter study based on

Jiaxian Chen, Xin Jiang, Guangjie Yang, Yongxiang Tang, Lin Qi, Minfeng Chen, Shuo Hu, Xiaomei Gao, Yu Gan, Mingxin Zhang and 2 more

Abstract read
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In one paragraph

Article in Journal of the National Cancer Center, 2026. 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

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jiaxian ChenDepartment of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Xin JiangDepartment of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Guangjie YangDepartment of Nuclear Medicine, Affiliated Hospital of Qingdao University, Qingdao, China.
Yongxiang TangDepartment of PET Center, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Lin QiDepartment of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Minfeng ChenDepartment of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Shuo HuDepartment of PET Center, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Xiaomei GaoDepartment of Pathology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Yu GanDepartment of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.
Mingxin ZhangDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, China.
Shouzhen ChenDepartment of Urology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Yi CaiDepartment of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Recent studies have shown that Methods: This study enrolled consecutive patients with suspected PCa who underwent systematic biopsy (SB) and PSMA PET/CT-targeted biopsy (TB) after Results: Overall, 609 patients were recruited, including 195 patients with benign prostate diseases (BPD), 30 patients with clinically insignificant prostate cancer (ciPCa, Gleason score [GS] = 3 + 3), and 384 patients with clinically significant prostate cancer (csPCa, GS ≥ 3 + 4). For csPCa prediction, the radiomics model developed by the eXtreme Gradient Boosting (XGBoost) algorithm demonstrated the best performance. After integrating with SUVmax, the combined model achieved the highest AUC of 0.921 in internal validation cohort. External validation cohort 1 and 2 also showed promising results with AUC of 0.906 and 0.898, respectively. For PCa prediction, the XGBoost algorithm combined with SUVmax also peformed well in three validation cohorts with the AUC ranging from 0.860 to 0.918. Conclusions: This is the largest multicenter study to date, providing a noninvasive and quantitative method based on

Indexed as

68Ga-PSMA PET/CTDiagnosisMachine learningMulticenterRadiomics

Identifiers

PMID42282124
PMCPMC13250527

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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.