TY  - JOUR
AU  - Starke, Sebastian
AU  - Zwanenburg-Bezemer, Alexander Adriaan
AU  - Leger, Karoline
AU  - Lohaus, Fabian
AU  - Linge, Annett
AU  - Kalinauskaite, Goda
AU  - Tinhofer, Inge
AU  - Guberina, Nika
AU  - Guberina, Maja
AU  - Balermpas, Panagiotis
AU  - Grün, Jens von der
AU  - Ganswindt, Ute
AU  - Belka, Claus
AU  - Peeken, Jan C
AU  - Combs, Stephanie E
AU  - Boeke, Simon
AU  - Zips, Daniel
AU  - Richter, Christian
AU  - Troost, Esther
AU  - Krause, Mechthild
AU  - Baumann, Michael
AU  - Löck, Steffen
TI  - Multitask Learning with Convolutional Neural Networks and Vision Transformers Can Improve Outcome Prediction for Head and Neck Cancer Patients.
JO  - Cancers
VL  - 15
IS  - 19
SN  - 2072-6694
CY  - Basel
PB  - MDPI
M1  - DKFZ-2023-02064
SP  - 4897
PY  - 2023
AB  - Neural-network-based outcome predictions may enable further treatment personalization of patients with head and neck cancer. The development of neural networks can prove challenging when a limited number of cases is available. Therefore, we investigated whether multitask learning strategies, implemented through the simultaneous optimization of two distinct outcome objectives (multi-outcome) and combined with a tumor segmentation task, can lead to improved performance of convolutional neural networks (CNNs) and vision transformers (ViTs). Model training was conducted on two distinct multicenter datasets for the endpoints loco-regional control (LRC) and progression-free survival (PFS), respectively. The first dataset consisted of pre-treatment computed tomography (CT) imaging for 290 patients and the second dataset contained combined positron emission tomography (PET)/CT data of 224 patients. Discriminative performance was assessed by the concordance index (C-index). Risk stratification was evaluated using log-rank tests. Across both datasets, CNN and ViT model ensembles achieved similar results. Multitask approaches showed favorable performance in most investigations. Multi-outcome CNN models trained with segmentation loss were identified as the optimal strategy across cohorts. On the PET/CT dataset, an ensemble of multi-outcome CNNs trained with segmentation loss achieved the best discrimination (C-index: 0.29, 95
KW  - Cox proportional hazards (Other)
KW  - convolutional neural network (Other)
KW  - discrete-time survival models (Other)
KW  - head and neck cancer (Other)
KW  - loco-regional control (Other)
KW  - multitask learning (Other)
KW  - progression-free survival (Other)
KW  - survival analysis (Other)
KW  - tumor segmentation (Other)
KW  - vision transformer (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:37835591
C2  - pmc:PMC10571894
DO  - DOI:10.3390/cancers15194897
UR  - https://inrepo02.dkfz.de/record/284753
ER  -