Artificial Intelligence in PET/CT Is about to Make Whole-Body Tumor Burden Measurements a Clinical Reality
Author:
Affiliation:
1. From the Department of Radiology, University of Minnesota, 420 Delaware St SE, Mayo Bldg, MMC 292, Minneapolis, MN 55455.
Publisher
Radiological Society of North America (RSNA)
Subject
Radiology Nuclear Medicine and imaging
Link
http://pubs.rsna.org/doi/pdf/10.1148/radiol.2019192425
Reference9 articles.
1. 18F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks
2. Baseline Metabolic Tumor Volume Predicts Outcome in High–Tumor-Burden Follicular Lymphoma: A Pooled Analysis of Three Multicenter Studies
3. Optimal FDG PET/CT volumetric parameters for risk stratification in patients with locally advanced non-small cell lung cancer: results from the ACRIN 6668/RTOG 0235 trial
4. PET-Derived Quantitative Metrics for Response and Prognosis in Lymphoma
5. Assessment of Total Lesion Glycolysis by 18F FDG PET/CT Significantly Improves Prognostic Value of GEP and ISS in Myeloma
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