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Lang, D.M. ; Peeken, J.C. ; Combs, S.E. ; Wilkens, J.J.* ; Bartzsch, S.

Deep learning based GTV delineation and progression free survival risk score prediction for head and neck cancer patients.

In: (HECKTOR 2021: Head and Neck Tumor Segmentation and Outcome Prediction, 27 September 2021, Virtual, Online). Berlin [u.a.]: Springer, 2022. 150-159 (Lect. Notes Comput. Sc. ; 13209 LNCS)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Konferenzbeitrag
Korrespondenzautor
Schlagwörter Deep Learning ; Head Neck ; Survival Analysis
ISSN (print) / ISBN 0302-9743
e-ISSN 1611-3349
Konferenztitel HECKTOR 2021: Head and Neck Tumor Segmentation and Outcome Prediction
Konferzenzdatum 27 September 2021
Konferenzort Virtual, Online
Zeitschrift Lecture Notes in Computer Science
Quellenangaben Band: 13209 LNCS, Heft: , Seiten: 150-159 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin [u.a.]
Nichtpatentliteratur Publikationen
Institut(e) Institute of Radiation Medicine (IRM)