Head and neck cancers

Tadman, Mike; Roberts, Dave

doi:10.1093/med/9780198569244.003.0033

Oxford Handbook of Cancer Nursing 2007

DOI: 10.1093/med/9780198569244.003.0033

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Head and neck cancers

Mike Tadman¹,

Dave Roberts²

Abstract: Introduction 436 Treatment of head and neck cancer 438 Laryngectomy and tracheostomy 440 Nursing management issues 442 • Head and neck (H&N) cancer is the sixth most common cancer worldwide. These cancers account for over 8000 cases per year in England and Wales, and 2700 deaths per year....

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2020

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Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study (Preprint)

Nikolov¹,

Blackwell²,

Zverovitch³

et al. 2020

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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.

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Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study (Preprint)

Nikolov¹,

Blackwell²,

Zverovitch³

et al. 2020

Preprint

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show abstract

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Head and neck cancers

Cited by 1 publication

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Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study (Preprint)

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study (Preprint)

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