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ArticleJapanese journal of radiology2026

Biparametric MRI in prostate cancer: utility of whole-prostate and whole-lesion histogram and texture analysis for clinically significant prostate cancer.

Luguang Chen, Pengyi Xing, Tiegong Wang, Xiaoyu Huang, Caixia Fu, Robert Grimm, Chengwei Shao, Jianping Lu

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Article in Japanese journal of radiology, 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
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1 · What the graph read from it

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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.

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Luguang Chen *Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.ORCID http://orcid.org/0000-0002-3742-9439
Pengyi Xing *Department of Radiology, The 989th Hospital of The Joint Logistic Support Force of the Chinese People's Liberation Army, Luoyang, Henan, China.
Tiegong WangDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Xiaoyu HuangDepartment of Information Technology, The Third Hospital Affiliated to Naval Medical University, Shanghai, China.
Caixia FuMR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., Shenzhen, China.
Robert GrimmApplication Predevelopment, Siemens Healthcare, Erlangen, Germany.
Chengwei ShaoDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China. cwshao@sina.com.
Jianping LuDepartment of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China. cjr.lujianping@vip.163.com.

Funding

Guhai Project of Changhai Hospital GH145-09
6 · The paper itself

Abstract

purposeThe purpose is to evaluate the utility of whole-lesion and whole-prostate gland histogram and texture analysis based on biparametric MRI (bp-MRI) for differentiating clinically significant prostate cancer (csPCa) from non-clinically significant prostate cancer (ncsPCa). We further compared the diagnostic performance of these quantitative features with PI-RADS assessment, clinical parameters, and combined models. MATERIALS AND

methodsThis retrospective study enrolled 337 patients (primary cohort, 260; validation cohort, 77) with pathologically proven prostate lesions. All patients underwent preoperative prostate bp-MRI [T2-weighted imaging and apparent diffusion coefficient (ADC) maps]. Histogram and texture features were extracted from both the whole lesion and the whole-prostate gland. Diagnostic models were constructed using multivariate logistic regression, incorporating PI-RADS scores, clinical parameters, and quantitative imaging features. Their performance was evaluated using the area under the receiver operating characteristic curve (AUC) and validated on an internal cohort.

resultsMultiple histogram and texture parameters from both whole-lesion and whole-prostate analyses significantly differed between csPCa and ncsPCa groups (p < 0.05), with ADC-derived features generally outperforming T2WI-derived ones. The combined model integrating texture features, clinical parameters, and PI-RADS (Texture&Clinics&PI-RADS) demonstrated the highest diagnostic performance for both whole-lesion analysis (AUCs: 0.938 for peripheral-zone or transitional-zone (PZ + TZ), 0.894 for peripheral-zone (PZ), 0.971 for transitional-zone (TZ) lesions) and whole-prostate analysis (AUCs: 0.926 for PZ + TZ, 0.804 for PZ, 0.981 for TZ lesions) in the primary cohort. This superior performance was consistently replicated in the validation cohort. Notably, no significant difference in diagnostic efficacy was found between whole-lesion and whole-prostate analyses for TZ lesions.

conclusionBoth whole-lesion and whole-prostate histogram and texture analysis based on bp-MRI are promising non-invasive tools for identifying csPCa. The combination of texture features, clinical parameters, and PI-RADS scores achieved the best diagnostic performance. These findings indicate that whole-lesion and whole-prostate histogram and texture analyses may improve the detection of csPCa above conventional PI-RADS assessment.

Indexed as

Magnetic Resonance ImagingProstatic NeoplasmsAgedHumansImage Interpretation, Computer-AssistedMaleMiddle AgedProstateRetrospective StudiesHistogramMagnetic resonance imagingProstate cancerTexture analysis

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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.