Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study
Top Cited Papers
Open Access
- 12 July 2021
- journal article
- Published by JMIR Publications Inc. in Journal of Medical Internet Research
- Vol. 23 (7), e26151
- https://doi.org/10.2196/26151
Abstract
Background: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. Objective: Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. Methods: The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. Results: We demonstrated the model’s clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model’s generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. Conclusions: Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.Keywords
This publication has 83 references indexed in Scilit:
- Atlas-based automatic segmentation of head and neck organs at risk and nodal target volumes: a clinical validationRadiation Oncology, 2013
- Systematic evaluation of three different commercial software solutions for automatic segmentation for adaptive therapy in head-and-neck, prostate and pleural cancerRadiation Oncology, 2012
- A multi-institution evaluation of deformable image registration algorithms for automatic organ delineation in adaptive head and neck radiotherapyRadiation Oncology, 2012
- 16. The fraction of cancer attributable to lifestyle and environmental factors in the UK in 2010British Journal of Cancer, 2011
- Auto‐segmentation of normal and target structures in head and neck CT images: A feature‐driven model‐based approachMedical Physics, 2011
- Parotid-sparing intensity modulated versus conventional radiotherapy in head and neck cancer (PARSPORT): a phase 3 multicentre randomised controlled trialThe Lancet Oncology, 2011
- Emphasizing Conformal Avoidance Versus Target Definition for IMRT Planning in Head-and-Neck CancerEndocrine, 2010
- The relationship between waiting time for radiotherapy and clinical outcomes: A systematic review of the literatureRadiotherapy and Oncology, 2008
- Trends in postoperative radiotherapy delay and the effect on survival in breast cancer patients treated with conservation surgeryBritish Journal of Cancer, 2004
- Measures of the Amount of Ecologic Association Between SpeciesEcology, 1945