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

With the development of functional imaging modalities we now have the ability to study the microenvironment of lung cancer and its genomic instability. Radiomics is defined as the use of automated or semi-automated post-processing and analysis of large amounts of quantitative imaging features that can be derived from medical images. The automated generation of these analytical features helps to quantify a number of variables in the imaging assessment of lung malignancy. These imaging features include: tumor spatial complexity, elucidation of the tumor genomic heterogeneity and composition, subregional identification in terms of tumor viability or aggressiveness, and response to chemotherapy and/or radiation. Therefore, a radiomic approach can help to reveal unique information about tumor behavior. Currently available radiomic features can be divided into four major classes: (a) morphological, (b) statistical, (c) regional, and (d) model-based. Each category yields quantitative parameters that reflect specific aspects of a tumor. The major challenge is to integrate radiomic data with clinical, pathological, and genomic information to decode the different types of tissue biology. There are many currently available radiomic studies on lung cancer for which there is a need to summarize the current state of the art.

Increasing use of molecular targeting agents and immune checkpoint inhibitors has increased the frequency and broadened the spectrum of lung toxicity, particularly in patients with cancer. In this position paper from the Fleischner Society, the authors provide diagnostic criteria and management recommendations of drug-related pneumonitis for radiologists, clinicians, clinical trialists, and trial sponsors.

We aimed to analyze the CT findings of ground-glass opacity nodules diagnosed pathologically as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma in order to investigate whether quantitative CT parameters enable distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. We reviewed CT images and pathologic specimens from 191 resected ground-glass opacity nodules with little or no solid component at CT. Nodule size, volume, density, mass, skewness/kurtosis, and CT attenuation values at the 2.5^th–97.5^th percentiles on histogram, and texture parameters (uniformity and entropy) were assessed from CT datasets. Of 191 tumors, 38 were AISs (20%), 61 were MIAs (32%), and 92 (48%) were invasive adenocarcinomas. Multivariate logistic regression analysis helped identify the 75^th percentile CT attenuation value (P = 0.04) and entropy (P<0.01) as independent predictors for invasive adenocarcinoma, with an area under the receiver operating characteristic curve of 0.780. Quantitative analysis of preoperative CT imaging metrics can help distinguish invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma.

Quantitative imaging using radiomics can capture distinct phenotypic differences between tumors and may have predictive power for certain phenotypes according to specific genetic mutations. We aimed to identify the clinicoradiologic predictors of tumors with ALK (anaplastic lymphoma kinase), ROS1 (c-ros oncogene 1), or RET (rearranged during transfection) fusions in patients with lung adenocarcinoma.A total of 539 pathologically confirmed lung adenocarcinomas were included in this retrospective study. The baseline clinicopathologic characteristics were retrieved from the patients' medical records and the ALK/ROS1/RET fusion status was reviewed. Quantitative computed tomography (CT) and positron emission tomography imaging characteristics were evaluated using a radiomics approach. Significant features for the fusion-positive tumor prediction model were extracted from all of the clinicoradiologic features, and were used to calculate diagnostic performance for predicting 3 fusions' positivity. The clinicoradiologic features were compared between ALK versus ROS1/RET fusion-positive tumors to identify the clinicoradiologic similarity between the 2 groups.The fusion-positive tumor prediction model was a combination of younger age, advanced tumor stage, solid tumor on CT, higher values for SUV(max) and tumor mass, lower values for kurtosis and inverse variance on 3-voxel distance than those of fusion-negative tumors (sensitivity and specificity, 0.73 and 0.70, respectively). ALK fusion-positive tumors were significantly different in tumor stage, central location, SUV(max), homogeneity on 1-, 2-, and 3-voxel distances, and sum mean on 2-voxel distance compared with ROS1/RET fusion-positive tumors.ALK/ROS1/RET fusion-positive lung adenocarcinomas possess certain clinical and imaging features that enable good discrimination of fusion-positive from fusion-negative lung adenocarcinomas.