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

he prospect of improved clinical outcomes and more efficient health systems has fueled a rapid rise in the development and evaluation of AI systems over the last decade. Because most AI systems within healthcare are complex interventions designed as clinical decision support systems, rather than autonomous agents, the interactions among the AI systems, their users and the implementation environments are defining components of the AI interventions' overall potential effectiveness. Therefore, bringing AI systems from mathematical performance to clinical utility needs an adapted, stepwise implementation and evaluation pathway, addressing the complexity of this collaboration between two independent forms of intelligence, beyond measures of effectiveness alone 1 . Despite indications that some AI-based algorithms now match the accuracy of human experts within preclinical in silico studies 2 , there

Pathological complete response (pCR) following neoadjuvant chemoradiotherapy or radiotherapy in locally advanced rectal cancer (LARC) is reached in approximately 15-30% of cases, therefore it would be useful to assess if pretreatment 18 F-FDG PET/CT and/or MRI texture features can reliably predict response to neoadjuvant therapy in LARC.Methods: 52 patients were dichotomized as responder (pR+) or non-responder (pR-) according to their pathological tumourtumor regression grade (TRG) as follows: 22 as pR+ (9 with TRG=1, 13 with TRG=2) and 30 as pR-(16 with TRG=3, 13 with TRG=4 and 1 with TRG=5). First order parameters and 21 second order texture parameters derived from the Gray-Level Co-Occurrence matrix were extracted from semiautomatically segmented tumourtumors on T2-w MRI, ADC maps and PET/CT acquisitions. The role of each texture feature in predicting pR+ was assessed with monoparametric and multiparametric models. Results:In the mono-parametric approach PET homogeneity reached the maximum AUC (0.77; sensitivity=72.7% and specificity=76.7%), while PET glycolytic volume and ADC dissimilarity reached the highest sensitivity (both 90.9%). In the multiparametric analysis, a logistic regression model containing 6 second-order texture features (five from PET and one from T2-w MRI) yields the highest predictivity in distinguish between pR+ and pR-patients (AUC=0.86; sensitivity=86% and specificity=83% at the Youden index). Conclusions:If preliminary results of this study arewill be confirmed, pretreatment PET and MRI images could be useful to personalize patient treatment, e.g., avoiding toxicity of neoadjuvant therapy in patients predicted pR-.