@inbook{c45e3e8ba1b24f1da0093f208bebb86e,
title = "Comparing Deep Learning and Conventional Machine Learning for Outcome Prediction of Head and Neck Cancer in PET/CT",
abstract = "Prediction of cancer treatment outcomes based on baseline patient characteristics is a challenging but necessary step towards more personalized treatments with the aim of increased survival and quality of life. The HEad and neCK TumOR Segmentation Challenge (HECKTOR) 2021 comprises two major tasks: auto-segmentation of GTVt in FDG-PET/CT images and outcome prediction for oropharyngeal head and neck cancer patients. The present study compared a deep learning regressor utilizing PET/CT images to conventional machine learning methods using clinical factors and radiomics features for the patient outcome prediction task. With a concordance index of 0.64, the conventional machine learning approach trained on clinical factors had the best test performance. Team: Aarhus_Oslo.",
keywords = "Deep learning, Gross tumor volume, Head and neck cancer, Machine learning, Outcome prediction, Radiomics",
author = "Bao-Ngoc Huynh and Jintao Ren and Groendahl, {Aurora Rosvoll} and Oliver Tomic and Korreman, {Stine Sofia} and Futsaether, {Cecilia Marie}",
year = "2022",
month = mar,
day = "13",
doi = "10.1007/978-3-030-98253-9_30",
language = "English",
isbn = "978-3-030-98252-2",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "318--326",
booktitle = "Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings",
address = "Netherlands",
}