This paper summarizes information about breast MRI to be provided to women and referring physicians. After listing contraindications, procedure details are described, stressing the need for correct scheduling and not moving during the examination. The structured report including BI-RADS® categories and further actions after a breast MRI examination are discussed. Breast MRI is a very sensitive modality, significantly improving screening in high-risk women. It also has a role in clinical diagnosis, problem solving, and staging, impacting on patient management. However, it is not a perfect test, and occasionally breast cancers can be missed. Therefore, clinical and other imaging findings (from mammography/ultrasound) should also be considered. Conversely, MRI may detect lesions not visible on other imaging modalities turning out to be benign (false positives). These risks should be discussed with women before a breast MRI is requested/performed. Because breast MRI drawbacks depend upon the indication for the examination, basic information for the most important breast MRI indications is presented. Seventeen notes and five frequently asked questions formulated for use as direct communication to women are provided. The text was reviewed by Europa Donna–The European Breast Cancer Coalition to ensure that it can be easily understood by women undergoing MRI. Key Points • Information on breast MRI concerns advantages/disadvantages and preparation to the examination • Claustrophobia, implantable devices, allergic predisposition, and renal function should be checked • Before menopause, scheduling on day 7–14 of the cycle is preferred • During the examination, it is highly important that the patient keeps still • Availability of prior examinations improves accuracy of breast MRI interpretation

OBJECTIVE. Our aim was to compare the effectiveness of mammography and MRI in the detection of multifocal, multicentric breast cancer.SUBJECTS AND METHODS. Ninety patients with planned mastectomies (nine bilateral) underwent mammography and dynamic gadolinium-enhanced MRI. Off-site reviewers aware of the entry criterion (planned mastectomy) evaluated both examinations for the presence of malignant foci, recording the density pattern on mammography. The gold standard was pathologic examination of the whole excised breast (slice thickness, 5 mm).RESULTS. Of 99 breasts, pathologic findings revealed 52 unifocal, 29 multifocal, and 18 multicentric cancers for a total of 188 malignant foci (158 invasive and 30 in situ). Overall sensitivity was 66% (124/188) for mammography and 81% (152/188) for MRI (p < 0.001); 72% (113/158) and 89% (140/158) for invasive foci (p < 0.001); and 37% (11/30) and 40% (12/30) for in situ foci (p > 0.05, not significant), respectively. Mammography and MRI missed 64 and 36 malignant f...

It has been proposed that magnetic resonance imaging (MRI) be used to guide breast cancer surgery by differentiating residual tumor from pathologic complete response (pCR) after neoadjuvant chemotherapy. This meta-analysis examines MRI accuracy in detecting residual tumor, investigates variables potentially affecting MRI performance, and compares MRI with other tests. A systematic literature search was undertaken. Hierarchical summary receiver operating characteristic (HSROC) models were used to estimate (relative) diagnostic odds ratios ([R]DORs). Summary sensitivity (correct identification of residual tumor), specificity (correct identification of pCR), and areas under the SROC curves (AUCs) were derived. All statistical tests were two-sided. Forty-four studies (2050 patients) were included. The overall AUC of MRI was 0.88. Accuracy was lower for “standard” pCR definitions (referent category) than “less clearly described” (RDOR = 2.41, 95% confidence interval [CI] = 1.11 to 5.23) or “near-pCR” definitions (RDOR = 2.60, 95% CI = 0.73 to 9.24; P = .03.) Corresponding AUCs were 0.83, 0.90, and 0.91. Specificity was higher when negative MRI was defined as contrast enhancement less than or equal to normal tissue (0.83, 95% CI = 0.64 to 0.93) vs no enhancement (0.54, 95% CI = 0.39 to 0.69; P = .02), with comparable sensitivity (0.83, 95% CI = 0.69 to 0.91; vs 0.87, 95% CI = 0.80 to 0.92; P = .45). MRI had higher accuracy than mammography (P = .02); there was only weak evidence that MRI had higher accuracy than clinical examination (P = .10). No difference in MRI and ultrasound accuracy was found (P = .15). MRI accurately detects residual tumor after neoadjuvant chemotherapy. Accuracy was lower when pCR was more rigorously defined, and specificity was lower when test negativity thresholds were more stringent; these definitions require standardization. MRI is more accurate than mammography; however, studies comparing MRI and ultrasound are required.

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