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

Lang DM, Peeken JC, Combs SE, Wilkens JJ, Bartzsch S (2022)

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Publication Type: Conference contribution

Publication year: 2022

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 13209 LNCS

Pages Range: 150-159

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Virtual, Online

ISBN: 9783030982522

DOI: 10.1007/978-3-030-98253-9_14

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.

Involved external institutions

How to cite

APA:

Lang, D.M., Peeken, J.C., Combs, S.E., Wilkens, J.J., & Bartzsch, S. (2022). Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients. In Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 150-159). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Lang, Daniel M., et al. "Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients." Proceedings of the 2nd 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 Ed. Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge, Springer Science and Business Media Deutschland GmbH, 2022. 150-159.

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