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

OBJECTIVE. The objective of our study was to establish the sensitivity and specificity for prostate cancer detection using a combined 1H MR spectroscopy and diffusion-weighted MRI approach.SUBJECTS AND METHODS. Forty-two men (mean age ± SD, 69.3 ± 4.7 years) with prostate cancer were studied using endorectal T2-weighted imaging, 2D chemical shift imaging (CSI), and isotropic apparent diffusion coefficient (ADC) maps. Regions of interest (ROIs) were drawn around the entire gland, central gland, and peripheral zone tumor, diagnostically defined as low signal intensity on T2-weighted images within a sextant that was biopsy-positive for tumor. Lack of susceptibility artifact on a gradient-echo B0 map through the slice selected for CSI and no high signal intensity on external array T1-weighted images confirmed the absence of significant hemorrhage after biopsy. CSI voxels were classified as nonmalignant or as tumor (ROI included ≥ 30% or 3 70% tumor). Choline-citrate (Cho/Cit) ratios and average ADCs were calc...

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the dataset compared to sample size, making adequate diversity of the data, cross-validation and external validation essential to mitigate the risks of spurious associations and overfitting. • Use of radiomic signatures within clinical trials requires multistep standardisation of image acquisition, image analysis and data mining processes. • Biological correlation may be established after clinical validation but is not mandatory.

Objectives: We studied patients managed by active surveillance to determine whether there was a difference over time in apparent diffusion coefficients (ADCs) derived from diffusion-weighted MRI in those who progressed to radical treatment (progressors, n = 17) compared with those who did not (non-progressors, n = 33). Methods: 50 consecutive patients (Stage T1/2a, Gleason grade ≤ 3+4, prostate-specific antigen (PSA) <15 ng ml–1, <50% cores positive) were imaged endorectally (baseline and 1–3 years follow-up) with T2 weighted (T2W) and echo-planar diffusion-weighted MRI sequences. Regions of interest drawn on ADC maps with reference to the T2W images yielded ADCall (b = 0–800), ADCfast (b = 0–300) and ADCslow (b = 300–800) for whole prostate (minus tumour) and tumour (low signal-intensity peripheral zone lesion in biopsy-positive octant). Results: Tumour and whole prostate ADCall and ADCfast were significantly reduced over time in progressors (p = 0.03 and 0.03 for tumours, respectively; p = 0.02 and 0.00...