2D and 3D convolutional neural networks for outcome modelling of locally advanced head and neck squamous cell carcinoma.
Journal:
Scientific reports
PMID:
32973220
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.
Authors
Keywords
Adult
Aged
Aged, 80 and over
Chemoradiotherapy
Female
Follow-Up Studies
Head and Neck Neoplasms
Humans
Image Processing, Computer-Assisted
Male
Middle Aged
Neoplasm Recurrence, Local
Neural Networks, Computer
Prognosis
Prospective Studies
Retrospective Studies
Squamous Cell Carcinoma of Head and Neck
Survival Rate
Tomography, X-Ray Computed
Tumor Burden