Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). Graphical

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.