Self-supervised Multi-modality Image Feature Extraction for the Progression Free Survival Prediction in Head and Neck Cancer

Baoqiang Ma*, Jiapan Guo, Alessia De Biase, Nikos Sourlos, Wei Tang, Peter van Ooijen, Stefan Both, Nanna Maria Sijtsema

*Bijbehorende auteur voor dit werk

OnderzoeksoutputAcademicpeer review

4 Citaten (Scopus)
62 Downloads (Pure)

Samenvatting

Long-term survival of oropharyngeal squamous cell carcinoma patients (OPSCC) is quite poor. Accurate prediction of Progression Free Survival (PFS) before treatment could make identification of high-risk patients before treatment feasible which makes it possible to intensify or de-intensify treatments for high- or low-risk patients. In this work, we proposed a deep learning based pipeline for PFS prediction. The proposed pipeline consists of three parts. Firstly, we utilize the pyramid autoencoder for image feature extraction from both CT and PET scans. Secondly, the feed forward feature selection method is used to remove the redundant features from the extracted image features as well as clinical features. Finally, we feed all selected features to a DeepSurv model for survival analysis that outputs the risk score on PFS on individual patients. The whole pipeline was trained on 224 OPSCC patients. We have achieved a average C-index of 0.7806 and 0.7967 on the independent validation set for task 2 and task 3. The C-indices achieved on the test set are 0.6445 and 0.6373, respectively. It is demonstrated that our proposed approach has the potential for PFS prediction and possibly for other survival endpoints.

Originele taal-2English
TitelHead and Neck Tumor Segmentation and Outcome Prediction - 2nd Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Proceedings
RedacteurenVincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
UitgeverijSpringer Science and Business Media Deutschland GmbH
Pagina's308-317
Aantal pagina's10
ISBN van elektronische versie978-3-030-98253-9
ISBN van geprinte versie978-3-030-98252-2
DOI's
StatusPublished - 2022
Evenement2nd 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
Duur: 27-sep.-202127-sep.-2021

Publicatie series

NaamLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13209 LNCS
ISSN van geprinte versie0302-9743
ISSN van elektronische versie1611-3349

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
StadVirtual, Online
Periode27/09/202127/09/2021

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