Evidence map›Paper›PMID 40553115›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Machine learning-based construction and validation of an radiomics model for predicting ISUP grading in prostate cancer: a multicenter radiomics study based on [68Ga]Ga-PSMA PET/CT.

Honghu Zhang, Xin Jiang, Guangjie Yang, Yongxiang Tang, Lin Qi, Minfeng Chen, Shuo Hu, Xiaomei Gao, Mingxin Zhang, Shouzhen Chen and 1 more

Abstract readMulticenter StudyValidation Study
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

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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

11 authors.

Honghu Zhang *Department of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, Hunan Province, China.
Xin Jiang *Department of Urology, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, China.
Guangjie Yang *Department of Nuclear Medicine, Affiliated Hospital of Qingdao University, Qingdao, 266000, China.
Yongxiang Tang *Department 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, No.87 Xiangya Road, Changsha City, Hunan Province, China.
Minfeng ChenDepartment of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, Hunan Province, 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. lswindy_2012@163.com.
Mingxin ZhangDepartment of Urology, Affiliated Hospital of Qingdao University, Qingdao, 266000, China. dr.zmx@qdu.edu.cn.
Shouzhen ChenDepartment of Urology, Cheeloo College of Medicine, Qilu Hospital, Shandong University, Jinan, China. chenshouzhen@email.sdu.edu.cn.
Yi CaiDepartment of Urology, Disorders of Prostate Cancer Multidisciplinary Team, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, No.87 Xiangya Road, Changsha City, Hunan Province, China. cai-yi@csu.edu.cn.ORCID 0000-0001-7773-2064

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe International Society of Urological Pathology (ISUP) grading of prostate cancer (PCa) is a crucial factor in the management and treatment planning for PCa patients. An accurate and non-invasive assessment of the ISUP grading group could significantly improve biopsy decisions and treatment planning. The use of PSMA-PET/CT radiomics for predicting ISUP has not been widely studied. The aim of this study is to investigate the role of

methodsThis study included 415 PCa patients who underwent

resultsThe best-performing model in the Xiangya Hospital training cohort achieved an AUC of 0.868 (sensitivity 72.7%, specificity 96.0%). Similar trends were seen in the testing cohort and external validation centers (AUCs: 0.860, 0.827, and 0.812). After incorporating PSA and SUVmax, a more robust model was developed, achieving an AUC of 0.892 (sensitivity 77.9%, specificity 96.0%) in the training group.

conclusionsThis study established and validated a radiomics model based on

Indexed as

Edetic AcidImage Processing, Computer-AssistedMachine LearningOligopeptidesPositron Emission Tomography Computed TomographyProstatic NeoplasmsAgedGallium IsotopesGallium RadioisotopesHumansMaleMiddle AgedNeoplasm GradingRadiomicsEdetic Acidgallium 68 PSMA-11Gallium IsotopesGallium RadioisotopesOligopeptidesMachine learning (ML)MulticenterPETProstate cancerProstate specific membrane antigen (PSMA)The international society of urological pathology (ISUP)

Identifiers

PMID40553115

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