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.
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.
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.
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.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
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
Identifiers
41138009What OpenQuestion holds
Registered trials
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.