Is it possible to model the risk of malignancy of focal abnormalities found at prostate multiparametric MRI?
OBJECTIVE
To evaluate whether focal abnormalities (FAs) depicted by prostate MRI could be characterised using simple semiological features.


METHODS
134 patients who underwent T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI at 1.5 T before prostate biopsy were prospectively included. FAs visible at MRI were characterised by their shape, the degree of signal abnormality (0 = normal to 3 = markedly abnormal) on individual MR sequences, and a subjective score (SS(1) = probably benign to SS(3) = probably malignant). FAs were then biopsied under US guidance.


RESULTS
56/233 FAs were positive at biopsy. The subjective score significantly predicted biopsy results (P < 0.01). As compared to SS(1) FAs, the odds ratios (OR) of malignancy of SS(2) and SS(3) FAs were 9.9 (1.8-55.9) and 163.8 (11.5-2331). Unlike FAs' shape, a simple combination of MR signal abnormalities (into "low-risk", "intermediate" and "high-risk" groups) significantly predicted biopsy results (P < 0.008). As compared to "low risk" FAs, the OR of malignancy of "intermediate" and "high-risk" FAs were 4.5 (1.1-18.4) and 52.7 (6.8-407) in the overall population and 5.4 (1.1-27.2) and 118.2 (6.1-2301) in PZ.


CONCLUSIONS
A simple combination of signal abnormalities of individual MR sequences can significantly stratify the risk of malignancy of FAs, holding promise of a more standardised interpretation of MRI by readers with varying experience.


CONCLUSIONS
• Using multiparameter(mp)-MRI, experienced uroradiologists can stratify the malignancy risk of prostatic lesions • The shape of prostatic focal abnormalities in the peripheral zone does not help predicting malignancy. • A simple combination of findings at mp-MRI can help less-experienced radiologists.
Hospices Civils de Lyon, Department of Urinary and Vascular Radiology, Hôpital Edouard Herriot, Lyon, France. olivier.rouviere@netcourrier.com
Olivier
O
Rouvière
Matthieu
M
Papillard
Nicolas
N
Girouin
Romain
R
Boutier
Muriel
M
Rabilloud
Benjamin
B
Riche
Florence
F
Mège-Lechevallier
Marc
M
Colombel
Albert
A
Gelet
D003198:Computer Simulation; D005602:France / Q000453:epidemiology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015995:Prevalence; D016016:Proportional Hazards Models; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000453:epidemiology*; D015203:Reproducibility of Results; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity