For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tumour control (LRC) from treatment-planning computed tomography images. Based on multicentre cohorts for exploration (206 patients) and independent validation (85 patients), multiple deep learning strategies including training of 3D- and 2D-convolutional neural networks (CNN) from scratch, transfer learning and extraction of deep autoencoder features were assessed and compared to a clinical model. Analyses were based on Cox proportional hazards regression and model performances were assessed by the concordance index (C-index) and the model's ability to stratify patients based on predicted hazards of LRC. Among all models, an ensemble of 3D-CNNs achieved the best performance (C-index 0.31) with a significant association to LRC on the independent validation cohort. It performed better than the clinical model including the tumour volume (C-index 0.39). Significant differences in LRC were observed between patient groups at low or high risk of tumour recurrence as predicted by the model (Formula: see text). This 3D-CNN ensemble will be further evaluated in a currently ongoing prospective validation study once follow-up is complete.
%0 Journal Article
%1 Starke2020-wg
%A Starke, Sebastian
%A Leger, Stefan
%A Zwanenburg, Alex
%A Leger, Karoline
%A Lohaus, Fabian
%A Linge, Annett
%A Schreiber, Andreas
%A Kalinauskaite, Goda
%A Tinhofer, Inge
%A Guberina, Nika
%A Guberina, Maja
%A Balermpas, Panagiotis
%A von der Grün, Jens
%A Ganswindt, Ute
%A Belka, Claus
%A Peeken, Jan C
%A Combs, Stephanie E
%A Boeke, Simon
%A Zips, Daniel
%A Richter, Christian
%A Troost, Esther G C
%A Krause, Mechthild
%A Baumann, Michael
%A Löck, Steffen
%D 2020
%I Springer Science and Business Media LLC
%J Sci. Rep.
%K
%N 1
%P 15625
%T 2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma
%V 10
%X For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tumour control (LRC) from treatment-planning computed tomography images. Based on multicentre cohorts for exploration (206 patients) and independent validation (85 patients), multiple deep learning strategies including training of 3D- and 2D-convolutional neural networks (CNN) from scratch, transfer learning and extraction of deep autoencoder features were assessed and compared to a clinical model. Analyses were based on Cox proportional hazards regression and model performances were assessed by the concordance index (C-index) and the model's ability to stratify patients based on predicted hazards of LRC. Among all models, an ensemble of 3D-CNNs achieved the best performance (C-index 0.31) with a significant association to LRC on the independent validation cohort. It performed better than the clinical model including the tumour volume (C-index 0.39). Significant differences in LRC were observed between patient groups at low or high risk of tumour recurrence as predicted by the model (Formula: see text). This 3D-CNN ensemble will be further evaluated in a currently ongoing prospective validation study once follow-up is complete.
@article{Starke2020-wg,
abstract = {For treatment individualisation of patients with locally advanced head and neck squamous cell carcinoma (HNSCC) treated with primary radiochemotherapy, we explored the capabilities of different deep learning approaches for predicting loco-regional tumour control (LRC) from treatment-planning computed tomography images. Based on multicentre cohorts for exploration (206 patients) and independent validation (85 patients), multiple deep learning strategies including training of 3D- and 2D-convolutional neural networks (CNN) from scratch, transfer learning and extraction of deep autoencoder features were assessed and compared to a clinical model. Analyses were based on Cox proportional hazards regression and model performances were assessed by the concordance index (C-index) and the model's ability to stratify patients based on predicted hazards of LRC. Among all models, an ensemble of 3D-CNNs achieved the best performance (C-index 0.31) with a significant association to LRC on the independent validation cohort. It performed better than the clinical model including the tumour volume (C-index 0.39). Significant differences in LRC were observed between patient groups at low or high risk of tumour recurrence as predicted by the model ([Formula: see text]). This 3D-CNN ensemble will be further evaluated in a currently ongoing prospective validation study once follow-up is complete.},
added-at = {2024-09-10T11:56:37.000+0200},
author = {Starke, Sebastian and Leger, Stefan and Zwanenburg, Alex and Leger, Karoline and Lohaus, Fabian and Linge, Annett and Schreiber, Andreas and Kalinauskaite, Goda and Tinhofer, Inge and Guberina, Nika and Guberina, Maja and Balermpas, Panagiotis and von der Gr{\"u}n, Jens and Ganswindt, Ute and Belka, Claus and Peeken, Jan C and Combs, Stephanie E and Boeke, Simon and Zips, Daniel and Richter, Christian and Troost, Esther G C and Krause, Mechthild and Baumann, Michael and L{\"o}ck, Steffen},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/200d0066c414b7aaf8980171feaa9ecfc/scadsfct},
copyright = {https://creativecommons.org/licenses/by/4.0},
interhash = {0ca4e118a18ced261c9c5042bbf0a6e2},
intrahash = {00d0066c414b7aaf8980171feaa9ecfc},
journal = {Sci. Rep.},
keywords = {},
language = {en},
month = sep,
number = 1,
pages = 15625,
publisher = {Springer Science and Business Media LLC},
timestamp = {2024-09-10T15:15:57.000+0200},
title = {{2D} and {3D} convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma},
volume = 10,
year = 2020
}
