Comparing Deep Learning and Conventional Machine Learning for Outcome Prediction of Head and Neck Cancer in PET/CT

Bao-Ngoc Huynh, Jintao Ren, Aurora Rosvoll Groendahl, Oliver Tomic, Stine Sofia Korreman*, Cecilia Marie Futsaether*

*Corresponding author af dette arbejde

Publikation: Bidrag til bog/antologi/rapport/proceedingBidrag til bog/antologiForskningpeer review

5 Citationer (Scopus)

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.

OriginalsprogEngelsk
TitelHead and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings : HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings
Antal sider9
UdgivelsesstedCham
ForlagSpringer
Publikationsdato13 mar. 2022
Sider318-326
ISBN (Trykt)978-3-030-98252-2
ISBN (Elektronisk)978-3-030-98253-9
DOI
StatusUdgivet - 13 mar. 2022
NavnLecture Notes in Computer Science
Vol/bind13209
ISSN0302-9743

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