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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Starke S,Leger S,Zwanenburg A,Leger K,Lohaus F,Linge A,Schreiber A,Kalinauskaite G,Tinhofer I,Guberina N,Guberina M,Balermpas P,von der Grün J,Ganswindt U,Belka C,Peeken JC,Combs SE,Boeke S,Zips D,Richter C,Troostdoi
10.1038/s41598-020-70542-9subject
Has Abstractpub_date
2020-09-24 00:00:00pages
15625issue
1issn
2045-2322pii
10.1038/s41598-020-70542-9journal_volume
10pub_type
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