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

Computed tomography is the first-line imaging modality for suspected pancreatic cancer. Magnetic resonance cholangiopancreatography is a second-line modality for suspected pancreatic cancer and is usually reserved for equivocal cases. Both computed tomography and MR are highly sensitive in the detection of pancreatic cancer, with up to 96% and 93.5% sensitivity, respectively. Computed tomography is superior to MR in the assessment of tumor resectability, with accuracy rates of up to 86.8% and 78.9%, respectively. Close attention to secondary signs of pancreatic cancer, such as pancreatic duct dilatation, abrupt pancreatic duct caliber change, and parenchymal atrophy, are critical in the diagnosis of pancreatic cancer. Emerging techniques such as radiomics and molecular imaging have the potential of identifying malignant precursors and lead to earlier disease diagnosis. The results of these promising techniques need to be validated in larger clinical studies.

Computer-aided diagnosis significantly reduces the low-dose CT screening false-positive rate and increases the positive predictive value of lung nodule evaluation. Purpose To test whether computer-aided diagnosis (CAD) approaches can increase the positive predictive value (PPV) and reduce the false-positive rate in lung cancer screening for small nodules compared with human reading by thoracic radiologists. Materials and Methods A matched case-control sample of low-dose computed tomography (CT) studies in 186 participants with 4–20-mm noncalcified lung nodules who underwent biopsy in the National Lung Screening Trial (NLST) was selected. Variables used for matching were age, sex, smoking status, chronic obstructive pulmonary disease status, body mass index, study year of the positive screening test, and screening results. Studies before lung biopsy were randomly split into a training set (70 cancers plus 70 benign controls) and a validation set (20 cancers plus 26 benign controls). Image features from within and outside dominant nodules were extracted. A CAD algorithm developed from the training set and a random forest classifier were applied to the validation set t...

The difference in circumferential strain between the endocardial and epicardial left ventricular myocardium is greatest in individuals with overt hypertrophic cardiomyopathy, but may also be abnorm... PurposeTo evaluate myocardial strain and circumferential transmural strain difference (cTSD; the difference between epicardial and endocardial circumferential strain) in a genotyped cohort with hyp...