Preoperative Multiparametric MRI-Based Tumour-Periprostatic Adipose Tissue Interface Characterisation for Extraprostatic Extension Prediction in Prostate Cancer

Early human evidence such as case series or small samples is exploring possible benefits.

• Level A · Stronger Clinical Evidence
• Level B · Emerging clinical evidence with positive signals
• Level C · Early human research exploring benefits
• Level D · Scientific groundwork from lab and animal studies
• Emerging · Emerging topic under active research

Abstract (original English)

Objective To evaluate the independent predictive value of tumour-periprostatic adipose tissue (PPAT) interface features on preoperative multiparametric magnetic resonance imaging (mpMRI) for extraprostatic extension (EPE) in prostate cancer and to compare discrimination and clinical net benefit with a baseline clinical model. Methods This single-centre retrospective cohort included patients who underwent radical prostatectomy with mpMRI completed within 8 weeks. On a single axial slice at maximum tumour diameter, five simplified interface features were measured using standard PACS tools: contact length, contact angle, T2 signal intensity ratio, interface apparent diffusion coefficient (3-mm annular zone) and capsular integrity score (0-2 scale). A baseline clinical model (prostate-specific antigen [PSA], PSA density, PI-RADS and biopsy Gleason score) and a combined model (baseline variables plus LASSO-selected interface features) were constructed. Bootstrap internal validation (1000 iterations) with bias correction was performed. Discrimination was assessed using the area under the curve (AUC), and calibration curves and decision curve analysis evaluated accuracy and net clinical benefit. Results A total of 240 patients were included, with an EPE prevalence of 34.2% (82/240). The combined model achieved a bias-corrected AUC of 0.823 (95% confidence interval [CI]: 0.768-0.878),

Evidence level

Early human evidence such as case series or small samples is exploring possible benefits.

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