Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients

Daniel M. Lang, Jan C. Peeken, Stephanie E. Combs, Jan J. Wilkens, Stefan Bartzsch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Head and neck cancer patients can experience significant side effects from therapy. Accurate risk stratification allows for proper determination of therapeutic dose and minimization of therapy induced damage to healthy tissue. Radiomics models have proven their power for detection of useful tumors characteristics that can be used for patient prognosis. We studied the ability of deep learning models for segmentation of gross tumor volumes (GTV) and prediction of a risk score for progression free survival based on positron emission tomography/computed tomography (PET/CT) images. A 3D Unet-like architecture was trained for segmentation and achieved a Dice similarity score of 0.705 on the test set. A transfer learning approach based on video clip data, allowing for full utilization of 3 dimensional information in medical imaging data was used for prediction of a tumor progression free survival score. Our approach was able to predict progression risk with a concordance index of 0.668 on the test data. For clinical application further studies involving a larger patient cohort are needed.

Original languageEnglish
Title of host publicationHead and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsVincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
PublisherSpringer Science and Business Media Deutschland GmbH
Pages150-159
Number of pages10
ISBN (Print)9783030982522
DOIs
StatePublished - 2022
Event2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13209 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2021, held in conjunction with 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

Keywords

  • Deep learning
  • Head neck
  • Survival analysis

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