Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma
Year: 2022
Type: conference-paper
Institution: The University of Texas MD Anderson Cancer Center
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funders: National Science Foundation, Elekta, University of Texas Health Science Center at Houston, National Institutes of Health, University of Texas MD Anderson Cancer Center, Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institute of Dental and Craniofacial Research, Division of Mathematical Sciences, National Center for Advancing Translational Sciences
Awards: G1042170274, G111615301, G1398991385, G1442369977, G1858909031, G1881506306, G203557288, G2326049341, G2507610090, M D Anderson Cancer Center Head & Neck SPORE, G2816209679, G293715275, G3670257500, G380820829, G4262044161, G6540572259, G671808950, CCSG supplement: HOPE/CARE, G7400934742, QuBBD: Collaborative Research: SMART -- Spatial-Nonspatial Multidimensional Adaptive Radiotherapy Treatment, G7884661002, G842568704, G9000019672, G9000022405, G9000025114, G9000034222, G9000146054, G941367620, G9625747
OpenAlex ID: W4226216133