Detection of prostate cancer index lesions with multiparametric magnetic resonance imaging (mp-MRI) using whole-mount histological sections as the reference standard

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Abstract

Objective

To evaluate the sensitivity of multiparametric magnetic resonance imaging (mp-MRI) for detecting prostate cancer foci, including the largest (index) lesions.

Patients and Methods

In all, 115 patients with biopsy confirmed prostate cancer underwent mp-MRI before radical prostatectomy. A single expert radiologist recorded all prostate cancer foci including the index lesion ‘blinded’ to the pathologist's biopsy report. Stained whole-mount histological sections were used as the reference standard. All lesions were contoured by an experienced uropathologist who assessed their volume and pathological Gleason score. All lesions with a volume of >0.5 mL and/or pathological Gleason score of >6 were defined as clinically significant prostate cancer. Multivariate analysis was used to ascertain the characteristics of lesions identified by MRI.

Results

In all, 104 of 115 index lesions were correctly diagnosed by mp-MRI (sensitivity 90.4%; 95% confidence interval [CI] 83.5–95.1%), including 98/105 clinically significant index lesions (93.3%; 95% CI 86.8–97.3%), among which three of three lesions had a volume of <0.5 mL and Gleason score of >6. Overall, mp-MRI detected 131/206 lesions including 13 of 68 ‘insignificant’ prostate cancers. The multivariate logistic regression modelling showed that pathological Gleason score (odds ratio [OR] 11.7, 95% CI 2.3–59.8; P = 0.003) and lesion volume (OR 4.24, 95% CI 1.3–14.7; P = 0.022) were independently associated with the detection of index lesions at MRI.

Conclusions

This study shows that mp-MRI has a high sensitivity for detecting clinically significant prostate cancer index lesions, while having disappointing results for the detection of small-volume, low Gleason score prostate cancer foci. Thus, mp-MRI could be used to stratify patients according to risk, allowing better treatment selection.

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