Evidence map›Paper›PMID 42340465›Full record

ArticleAbdominal radiology (New York)2026

Optimal MRI sequence for predicting extraprostatic extension in prostate cancer: comparison of LCC and MaxRADD measurements.

Gorun Ilanjian, Jade Matthey, Fabio Porões, Massimo Valerio, Adrien Richemond, Julien Dagher, Arnas Rakauskas, Clarisse Dromain, Mario Jreige, Naïk Vietti-Violi

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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10 authors.

Gorun IlanjianRadiology department, University Hospital of Lausanne, Lausanne, Switzerland. gorun.ilanjian@chuv.ch.
Jade MattheyRadiology department, University Hospital of Lausanne, Lausanne, Switzerland.
Fabio PorõesRadiology department, University Hospital of Lausanne, Lausanne, Switzerland.
Massimo ValerioUrology Department, University Hospital of Geneva, Geneva, Switzerland.
Adrien RichemondUNIL, University of Lausanne, Lausanne, Switzerland.
Julien DagherPathology Department, University Hospital of Lausanne, Lausanne, Switzerland.
Arnas RakauskasUrology Department, University Hospital of Lausanne, Lausanne, Switzerland.
Clarisse DromainRadiology department, University Hospital of Lausanne, Lausanne, Switzerland.
Mario JreigeNuclear Medecine Department, University Hospital of Lausanne, Lausanne, Switzerland.
Naïk Vietti-VioliRadiology department, University Hospital of Lausanne, Lausanne, Switzerland. Naik.Vietti-Violi@chuv.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAccurate preoperative identification of extraprostatic extension (EPE) in prostate cancer (PCa) is important for treatment strategies. This retrospective study aimed to determine which MRI sequence provides the most accurate measurement of length of capsular contact (LCC) and maximum lesion radial distance (MaxRADD) for predicting EPE.

methodsWe included 63 patients who underwent radical prostatectomy for biopsy-confirmed PCa and had preoperative multiparametric MRI (mpMRI) from 2018 to 2020. Two radiologists measured LCC and MaxRADD on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and dynamic contrast-enhanced (DCE) sequences. LCC was evaluated using 10 mm and 15 mm thresholds based on Prostate Imaging-Reporting and Data System (PI-RADS) and European Society of Urogenital Radiology (ESUR) guidelines. Histopathology was the reference standard. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated.

results63 patients with 105 evaluable lesions were included. Significant differences were observed between the 10 mm and 15 mm LCC thresholds for predicting EPE. At 10 mm, DCE demonstrated the highest sensitivity (90.1%) and a 80.7% specificity. At 15 mm, DCE achieved higher specificity (94.7%) with 85% sensitivity. ROC analysis identified 14 mm on DCE as the optimal LCC threshold, reaching 90% sensitivity, 96.6% specificity (AUC:0.93). MaxRADD showed variable diagnostic performance, with the highest AUC on DCE (0.83), T2WI (0.80), DWI (0.78), and ADC (0.74), without significant differences. Combining LCC and MaxRADD slightly improved AUC (0.96 vs. 0.93 for LCC alone, p = 0.270). MaxRADD performed best on DCE at an 11 mm threshold (sensitivity 65%, specificity 88.5%).

conclusionDCE provides the highest diagnostic performance for predicting EPE using LCC measurements, with a 14 mm LCC and 11 mm MaxRADD threshold yielding the best overall accuracy. Combining LCC and MaxRADD numerically increased AUC (0.96 vs. 0.93), however without significant differences in EPE prediction (p = 0.270).

Indexed as

Dynamic contrast-enhancedExtra-prostatic extensionLength capsular contactMaximal lesion radial distanceMultiparametric MRIProstate cancer

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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.