Deep Learning-Enabled Automated Quality Control for Liver MR Elastography: Initial Results.

Background: Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient-related factors.

Purpose: To report initial results on automatic classification of liver MRE image quality using various deep learning (DL) architectures.

Study Type: Retrospective, single center, IRB-approved human study.

The Society for Immunotherapy of Cancer Perspective on Tissue-Based Technologies for Immuno-Oncology Biomarker Discovery and Application.

With immuno-oncology becoming the standard of care for a variety of cancers, identifying biomarkers that reliably classify patient response, resistance, or toxicity becomes the next critical barrier towards improving care. Multi-parametric, multi-omics, and computational platforms generating an unprecedented depth of data are poised to usher in the discovery of increasingly robust biomarkers for enhanced patient selection and personalized treatment approaches. Deciding which developing technologies to implement in clinical settings ultimately, applied either alone or in combination, relies on weighing pros and cons, from minimizing patient sampling to maximizing data outputs, and assessing reproducibility and representativeness of findings, while lessening data fragmentation towards harmonization.

Prediction of hepatocellular carcinoma response to radiation segmentectomy using an MRI-based machine learning approach.

Purpose: To evaluate the value of pre-treatment MRI-based radiomics in patients with hepatocellular carcinoma (HCC) for the prediction of response to Yttrium 90 radiation segmentectomy.

Methods: This retrospective study included 154 patients (38 female; mean age 66.8 years) who underwent contrast-enhanced MRI prior to radiation segmentectomy.