Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research. There is a clear need for a complete radiomics checklist for study planning, manuscript writing, and evaluation during the review process to facilitate the repeatability and reproducibility of studies. We here present a documentation standard for radiomic research that can guide authors and reviewers. Our motivation is to improve the quality and reliability and, in turn, the reproducibility of radiomic research. We name the checklist CLEAR (CheckList for EvaluAtion of Radiomics research), to convey the idea of being more transparent. With its 58 items, the CLEAR checklist should be considered a standardization tool providing the minimum requirements for presenting clinical radiomics research. In addition to a dynamic online version of the checklist, a public repository has also been set up to allow the radiomics community to comment on the checklist items and adapt the checklist for future versions. Prepared and revised by an international group of experts using a modified Delphi method, we hope the CLEAR checklist will serve well as a single and complete scientific documentation tool for authors and reviewers to improve the radiomics literature.

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

Background: The factors influencing parents’ willingness to enroll their children in biobanks are poorly understood. This study sought to assess parents’ willingness to enroll their children, and their perceived benefits, concerns, and information needs under different consent and data-sharing scenarios, and to identify factors associated with willingness. Methods: This large, experimental survey of patients at the 11 eMERGE Network sites used a disproportionate stratified sampling scheme to enrich the sample with historically underrepresented groups. Participants were randomized to receive one of three consent and data-sharing scenarios. Results: In total, 90,000 surveys were mailed and 13,000 individuals responded (15.8% response rate). 5737 respondents were parents of minor children. Overall, 55% (95% confidence interval 50–59%) of parents were willing to enroll their youngest minor child in a hypothetical biobank; willingness did not differ between consent and data-sharing scenarios. Lower educational attainment, higher religiosity, lower trust, worries about privacy, and attitudes about benefits, concerns, and information needs were independently associated with less willingness to allow their child to participate. Of parents who were willing to participate themselves, 25% were not willing to allow their child to participate. Being willing to participate but not willing to allow one’s child to participate was independently associated with multiple factors, including race, lower educational attainment, lower annual household income, public health care insurance, and higher religiosity. Conclusions: Fifty-five percent of parents were willing to allow their youngest minor child to participate in a hypothetical biobank. Building trust, protecting privacy, and addressing attitudes may increase enrollment and diversity in pediatric biobanks.

Background: The end-of-life period is a crucial time in lung cancer care. To have a better understanding of the racial-ethnic disparities in health care expenditures, access, and quality, we evaluated these disparities specifically in the end-of-life period for patients with lung cancer in the U.S. Materials and Methods: We used the Surveillance, Epidemiology, and End Results (SEER)-Medicare database to analyze characteristics of lung cancer care among those diagnosed between the years 2000 and 2011. Linear and logistic regression models were constructed to measure racial-ethnic disparities in end-of-life care cost and utilization among non-Hispanic (NH) Asian, NH black, Hispanic, and NH white patients while controlling for other risk factors such as age, sex, and SEER geographic region. Results: Total costs and hospital utilization were, on average, greater among racial-ethnic minorities compared with NH white patients in the last month of life. Among patients with NSCLC, the relative total costs were 1.27 (95% confidence interval [CI], 1.21–1.33) for NH black patients, 1.36 (95% CI, 1.25–1.49) for NH Asian patients, and 1.21 (95% CI, 1.07–1.38) for Hispanic patients. Additionally, the odds of being admitted to a hospital for NH black, NH Asian, and Hispanic patients were 1.22 (95% CI, 1.15–1.30), 1.47 (95% CI, 1.32–1.63), and 1.18 (95% CI, 1.01–1.38) times that of NH white patients, respectively. Similar results were found for patients with SCLC. Conclusion: Minority patients with lung cancer have significantly higher end-of-life medical expenditures than NH white patients, which may be explained by a greater intensity of care in the end-of-life period.