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Without adaptation, the model demonstrated substantial agreement with the original reporting radiologists for all three datasets (site 1 FFDM linearly weighted Cohen κ [κ<br /><br /> ] = 0.75 [95% CI 0.74, 0.76]; site 1 SM κ<br /><br /> = 0.71 [95% CI 0.64, 0.78]; site 2 SM κ<br /><br /> = 0.72 [95% CI 0.70, 0.75]). With adaptation, performance improved for site 2 (site 1 κ<br /><br /> = 0.72 [95% CI 0.66, 0.79], 0.71 vs 0.72, <br /><br /> = .80; site 2 κ<br /><br /> = 0.79 [95% CI 0.76, 0.81], 0.72 vs 0.79, <br /><br /> &lt; .001) by using only 500 SM images from that site.<br /><br /> A BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved by using few SM images.<br /><br /> Published under a CC BY 4.0 license.<br /><br />A BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved by using few SM images.Supplemental material is available for this article.Published under a CC BY 4.0 license.Artificial intelligence and machine learning (AI-ML) have taken center stage in medical imaging. To develop as leaders in AI-ML, radiology residents may seek a formative data science experience. The authors piloted an elective Data Science Pathway (DSP) for 4th-year residents at the authors' institution in collaboration with the MGH &amp; BWH Center for Clinical Data Science (CCDS). [https://www.selleckchem.com/products/blasticidin-s-hcl.html selleck chemical] The goal of the DSP was to provide an introduction to AI-ML through a flexible schedule of educational, experiential, and research activities. The study describes the initial experience with the DSP tailored to the AI-ML interests of three senior radiology residents. The authors also discuss logistics and curricular design with common core elements and shared mentorship. Residents were provided dedicated, full-time immersion into the CCDS work environment. In the initial DSP pilot, residents were successfully integrated into AI-ML projects at CCDS. Residents were exposed to all aspects of AI-ML application development, including data curation, model design, quality control, and clinical testing. Core concepts in AI-ML were taught through didactic sessions and daily collaboration with data scientists and other staff. Work during the pilot period led to 12 accepted abstracts for presentation at national meetings. The DSP is a feasible, well-rounded introductory experience in AI-ML for senior radiology residents. Residents contributed to model and tool development at multiple stages and were academically productive. Feedback from the pilot resulted in establishment of a formal AI-ML curriculum for future residents. The described logistical, planning, and curricular considerations provide a framework for DSP implementation at other institutions. Supplemental material is available for this article. © RSNA, 2020.<br /><br /> To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI.<br /><br /> Quantification and localization of different adipose tissue compartments derived from whole-body MR images is of high interest in research concerning metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automated segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work, a three-dimensional (3D) densely connected convolutional neural network (DCNet) is proposed to provide robust and objective segmentation. In this retrospective study, 1000 cases (average age, 66 years ± 13 [standard deviation]; 523 women) from the Tuebingen Family Study database and the German Center for Diabetes research database and 300 cases (average age, 53 years ± 11; 152 women) from the German National Cohort (NAKO) database were collected for model training, validation, and testing, witort studies with the proposed DCNet.Supplemental material is available for this article.© RSNA, 2020.<br /><br /> To provide an overview of important factors to consider when purchasing radiology artificial intelligence (AI) software and current software offerings by type, subspecialty, and modality.<br /><br /> Important factors for consideration when purchasing AI software, including key decision makers, data ownership and privacy, cost structures, performance indicators, and potential return on investment are described. For the market overview, a list of radiology AI companies was aggregated from the Radiological Society of North America and the Society for Imaging Informatics in Medicine conferences (November 2016-June 2019), then narrowed to companies using deep learning for imaging analysis and diagnosis. Software created for image enhancement, reporting, or workflow management was excluded. Software was categorized by task (repetitive, quantitative, explorative, and diagnostic), modality, and subspecialty.<br /><br /> A total of 119 software offerings from 55 companies were identified. There were 46 algorithms that currently have Fducate themselves on current product offerings and important factors to consider before purchase and implementation.© RSNA, 2020See also the invited commentary by Sala and Ursprung in this issue.<br /><br /> To determine if quantitative features extracted from pretherapy fluorine 18 fluorodeoxyglucose (<br /><br /> F-FDG) PET/CT estimate prognosis in patients with locally advanced cervical cancer treated with chemoradiotherapy.<br /><br /> In this retrospective study, PET/CT images and outcomes were curated from 154 patients with locally advanced cervical cancer, who underwent chemoradiotherapy from two institutions between March 2008 and June 2016, separated into independent training (<br /><br /> = 78; mean age, 51 years ± 13 [standard deviation]) and testing (<br /><br /> = 76; mean age, 50 years ± 10) cohorts. Radiomic features were extracted from PET, CT, and habitat (subregions with different metabolic characteristics) images that were derived by fusing PET and CT images. Parsimonious sets of these features were identified by the least absolute shrinkage and selection operator analysis and used to generate predictive radiomics signatures for progression-free survival (PFS) and overall survival (OS) estimation. Prognostic validation of the radiomic signatures as independent prognostic markers was performed using multivariable Cox regression, which was expressed as nomograms, together with other clinical risk factors.