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.

The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R−) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R− lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies.