To explore contrast (C) and homogeneity (H) gray-level co-occurrence matrix texture features on T2-weighted (T2w) Magnetic Resonance (MR) images and apparent diffusion coefficient (ADC) maps for predicting prostate cancer (PCa) aggressiveness, and to compare them with traditional ADC metrics for differentiating low- from intermediate/high-grade PCas. The local Ethics Committee approved this prospective study of 93 patients (median age, 65 years), who underwent 1.5 T multiparametric endorectal MR imaging before prostatectomy. Clinically significant (volume ≥0.5 ml) peripheral tumours were outlined on histological sections, contoured on T2w and ADC images, and their pathological Gleason Score (pGS) was recorded. C, H, and traditional ADC metrics (mean, median, 10th and 25th percentile) were calculated on the largest lesion slice, and correlated with the pGS through the Spearman correlation coefficient. The area under the receiver operating characteristic curve (AUC) assessed how parameters differentiate pGS = 6 from pGS ≥ 7. The dataset included 49 clinically significant PCas with a balanced distribution of pGS. The Spearman ρ and AUC values on ADC were: −0.489, 0.823 (mean); −0.522, 0.821 (median); −0.569, 0.854 (10th percentile); −0.556, 0.854 (25th percentile); −0.386, 0.871 (C); 0.533, 0.923 (H); while on T2w they were: −0.654, 0.945 (C); 0.645, 0.962 (H). AUC of H on ADC and T2w, and C on T2w were significantly higher than that of the mean ADC ( p = 0.05). H and C calculated on T2w images outperform ADC parameters in correlating with pGS and differentiating low- from intermediate/high-risk PCas, supporting the role of T2w MR imaging in assessing PCa biological aggressiveness.

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 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.

Purpose: To describe and test a new fully automatic lesion detection system for breast DCE‐MRI.Materials and Methods: Studies were collected from two institutions adopting different DCE‐MRI sequences, one with and the other one without fat‐saturation. The detection pipeline consists of (i) breast segmentation, to identify breast size and location; (ii) registration, to correct for patient movements; (iii) lesion detection, to extract contrast‐enhanced regions using a new normalization technique based on the contrast‐uptake of mammary vessels; (iv) false positive (FP) reduction, to exclude contrast‐enhanced regions other than lesions. Detection rate (number of system‐detected malignant and benign lesions over the total number of lesions) and sensitivity (system‐detected malignant lesions over the total number of malignant lesions) were assessed. The number of FPs was also assessed.Results: Forty‐eight studies with 12 benign and 53 malignant lesions were evaluated. Median lesion diameter was 6 mm (range, 5–15 mm) for benign and 26 mm (range, 5–75 mm) for malignant lesions. Detection rate was 58/65 (89%; 95% confidence interval [CI] 79%–95%) and sensitivity was 52/53 (98%; 95% CI 90%–99%). Mammary median FPs per breast was 4 (1st–3rd quartiles 3–7.25).Conclusion: The system showed promising results on MR datasets obtained from different scanners producing fat‐sat or non–fat‐sat images with variable temporal and spatial resolution and could potentially be used for early diagnosis and staging of breast cancer to reduce reading time and to improve lesion detection. Further evaluation is needed before it may be used in clinical practice. J. Magn. Reson. Imaging 2011;.