Is it possible to model the risk of malignancy of focal abnormalities found at prostate multiparametric MRI?
prospective_cohort · Level II
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- Record sourced from PubMed, PMID 22227613.
- Also identified by DOI 10.1007/s00330-011-2343-8.
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Abstract
To evaluate whether focal abnormalities (FAs) depicted by prostate MRI could be characterised using simple semiological features. 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. 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. 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. • 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.
Medical subject headings
- Image Interpretation, Computer-Assisted
- Magnetic Resonance Imaging
- Models, Statistical
- Proportional Hazards Models
- Prostatic Neoplasms