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• MRI-based volumetric analysis can assess outcomes of PS in extremity VMs. • Volumetric assessment of volume and SI change on T2-WI after PS is feasible. • VM volume change is a reliable indicator of treatment response after PS. • VM volume and SI correlate with clinical outcomes in specific patient groups. To investigate quantitative changes in MRI signal intensity (SI) and lesion volume that indicate treatment response and correlate these changes with clinical outcomes after percutaneous sclerotherapy (PS) of extremity venous malformations (VMs). VMs were segmented manually on pre- and post-treatment T2-weighted MRI using 3D Slicer to assess changes in lesion volume and SI. Clinical outcomes were scored on a 7-point Likert scale according to patient perception of symptom improvement; treatment response (success or failure) was determined accordingly. Eighty-one patients with VMs underwent 125 PS sessions. Treatment success occurred in 77 patients (95 %). Mean (±SD) changes were –7.9 ± 24 cm3 in lesion volume and –123 ± 162 in SI (both, P <.001). Mean reduction in lesion volume was greater in the success group (–9.4 ± 24 cm3) than in the failure group (21 ± 20 cm3) (P =.006). Overall, lesion volume correlated with treatment response (ρ = –0.3, P =.004). On subgroup analysis, volume change correlated with clinical outcomes in children (ρ = –0.3, P =.03), in sodium tetradecyl sulfate–treated lesions (ρ = –0.5, P =.02), and in foot lesions (ρ = –0.6, P =.04). SI change correlated with clinical outcomes in VMs treated in 1 PS session (ρ = –0.3, P =.01) and in bleomycin-treated lesions (ρ = –0.4, P =.04). Change in lesion volume is a reliable indicator of treatment response. Lesion volume and SI correlate with clinical outcomes in specific subgroups. [ABSTRACT FROM AUTHOR]

On October 5, 2020, the Medical Image Computing and Computer Assisted Intervention Society (MICCAI) 2020 conference hosted a virtual panel discussion with members of the Machine Learning Steering Subcommittee of the Radiological Society of North America. The MICCAI Society brings together scientists, engineers, physicians, educators, and students from around the world. Both societies share a vision to develop radiologic and medical imaging techniques through advanced quantitative imaging biomarkers and artificial intelligence. The panel elaborated on how collaborations between radiologists and machine learning scientists facilitate the creation and clinical success of imaging technology for radiology. This report presents structured highlights of the moderated dialogue at the panel. Keywords: Back-Propagation, Artificial Neural Network Algorithms, Machine Learning Algorithms © RSNA, 2021., Competing Interests: Disclosures of Conflicts of Interest: J.M. institution received grant from GE Healthcare; author receives royalties from GE Healthcare for licensing of pneumothorax detection algorithm; author paid for development of educational presentations from UCSF Postgraduate Medical Education; author’s spouse has been employed by AbbVie and Annexon Biosciences; author is associate editor of Radiology: Artificial Intelligence. J.K.C. institution received grants from GE, NIH, NSF, and Genentech; author received travel accommodations from IBM; author is deputy editor of Radiology: Artificial Intelligence. A.F. disclosed no relevant relationships. M.G.L. author is consultant to the National Institutes of Health for grant review; author is co-founder of PediaMetrix; author’s institution received grants from National Institutes of Health, National Science Foundation, Department of Defense, and Philips Healthcare; author has stock/stock options in PediaMetrix., (2021 by the Radiological Society of North America, Inc.)