Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients

Autor: Ma, Baoqiang, Li, Yan, Chu, Hung, Tang, Wei, De la O Arévalo, Luis Ricardo, Guo, Jiapan, van Ooijen, Peter, Both, Stefan, Langendijk, Johannes Albertus, van Dijk, Lisanne V., Sijtsema, Nanna Maria, Andrearczyk, Vincent, Oreiller, Valentin, Depeursinge, Adrien, Hatt, Mathieu

Publikováno v: Lecture Notes in Computer Science ISBN: 9783031274190
Head and Neck Tumor Segmentation and Outcome Prediction-3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings, 240-254
STARTPAGE=240;ENDPAGE=254;TITLE=Head and Neck Tumor Segmentation and Outcome Prediction-3rd Challenge, HECKTOR 2022, Held in Conjunction with MICCAI 2022, Proceedings

Aim: The development and evaluation of deep learning (DL) and radiomics based models for recurrence-free survival (RFS) prediction in oropharyngeal squamous cell carcinoma (OPSCC) patients based on clinical features, positron emission tomography (PET

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8228b87bc9894214eb3e5071d3f21217
https://doi.org/10.1007/978-3-031-27420-6_24