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

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

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

Helmholtz Zentrum München German Research Center for Environmental Health
Technical University of Munich
Partner Site Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 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 language	English
Title of host publication	Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings
Editors	Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	150-159
Number of pages	10
ISBN (Print)	9783030982522
DOIs	https://doi.org/10.1007/978-3-030-98253-9_14
State	Published - 2022
Event	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 Duration: 27 Sep 2021 → 27 Sep 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13209 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	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
City	Virtual, Online
Period	27/09/21 → 27/09/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Deep learning
Head neck
Survival analysis

Access to Document

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

Cite this

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 V. Andrearczyk, V. Oreiller, M. Hatt, & A. Depeursinge (Eds.), Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings (pp. 150-159). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13209 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-98253-9_14

Lang, Daniel M. ; Peeken, Jan C. ; Combs, Stephanie E. et al. / Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients. Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings. editor / Vincent Andrearczyk ; Valentin Oreiller ; Mathieu Hatt ; Adrien Depeursinge. Springer Science and Business Media Deutschland GmbH, 2022. pp. 150-159 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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.",

keywords = "Deep learning, Head neck, Survival analysis",

author = "Lang, \{Daniel M.\} and Peeken, \{Jan C.\} and Combs, \{Stephanie E.\} and Wilkens, \{Jan J.\} and Stefan Bartzsch",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 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 ; Conference date: 27-09-2021 Through 27-09-2021",

year = "2022",

doi = "10.1007/978-3-030-98253-9\_14",

language = "English",

isbn = "9783030982522",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "150--159",

editor = "Vincent Andrearczyk and Valentin Oreiller and Mathieu Hatt and Adrien Depeursinge",

booktitle = "Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings",

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Lang, DM, Peeken, JC, Combs, SE , Wilkens, JJ & Bartzsch, S 2022, Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients. in V Andrearczyk, V Oreiller, M Hatt & A Depeursinge (eds), Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13209 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 150-159, 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, 27/09/21. https://doi.org/10.1007/978-3-030-98253-9_14

Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients. / Lang, Daniel M.; Peeken, Jan C.; Combs, Stephanie E. et al.
Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings. ed. / Vincent Andrearczyk; Valentin Oreiller; Mathieu Hatt; Adrien Depeursinge. Springer Science and Business Media Deutschland GmbH, 2022. p. 150-159 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13209 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients

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AU - Peeken, Jan C.

AU - Combs, Stephanie E.

AU - Wilkens, Jan J.

AU - Bartzsch, Stefan

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N2 - 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.

AB - 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.

KW - Deep learning

KW - Head neck

KW - Survival analysis

UR - https://www.scopus.com/pages/publications/85126695158

U2 - 10.1007/978-3-030-98253-9_14

DO - 10.1007/978-3-030-98253-9_14

M3 - Conference contribution

AN - SCOPUS:85126695158

SN - 9783030982522

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 150

EP - 159

BT - Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings

A2 - Andrearczyk, Vincent

A2 - Oreiller, Valentin

A2 - Hatt, Mathieu

A2 - Depeursinge, Adrien

PB - Springer Science and Business Media Deutschland GmbH

T2 - 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

Y2 - 27 September 2021 through 27 September 2021

ER -

Lang DM, Peeken JC, Combs SE , Wilkens JJ, Bartzsch S. Deep Learning Based GTV Delineation and Progression Free Survival Risk Score Prediction for Head and Neck Cancer Patients. In Andrearczyk V, Oreiller V, Hatt M, Depeursinge A, editors, Head and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 150-159. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-98253-9_14