A Review on Application of Machine Learning and Deep Learning Algorithms in Head and Neck Cancer Prediction and Prognosis

2021 ◽  
pp. 59-73
Author(s):  
Deepti ◽  
Susmita Ray
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Neck Cancer ◽  
Learning Algorithms ◽  
Cancer Prediction

scholarly journals A Machine Learning Challenge for Prognostic Modelling in Head and Neck Cancer Using Multi-modal Data

2021 ◽  
Author(s):  
Benjamin Haibe-Kains ◽  
Michal Kazmierski ◽  
Mattea Welch ◽  
Sejin Kim ◽  
Chris McIntosh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Clinical Data ◽  
Neck Cancer ◽  
Evaluation Framework ◽  
Cancer Prognosis ◽  
Survival Prediction ◽  
Precision Oncology

Abstract Accurate prognosis for an individual patient is a key component of precision oncology. Recent advances in machine learning have enabled the development of models using a wider range of data, including imaging. Radiomics aims to extract quantitative predictive and prognostic biomarkers from routine medical imaging, but evidence for computed tomography radiomics for prognosis remains inconclusive. We have conducted an institutional machine learning challenge to develop an accurate model for overall survival prediction in head and neck cancer using clinical data etxracted from electronic medical records and pre-treatment radiological images, as well as to evaluate the true added benefit of radiomics for head and neck cancer prognosis. Using a large, retrospective dataset of 2,552 patients and a rigorous evaluation framework, we compared 12 different submissions using imaging and clinical data, separately or in combination. The winning approach used non-linear, multitask learning on clinical data and tumour volume, achieving high prognostic accuracy for 2-year and lifetime survival prediction and outperforming models relying on clinical data only, engineered radiomics and deep learning. Combining all submissions in an ensemble model resulted in improved accuracy, with the highest gain from a image-based deep learning model. Our results show the potential of machine learning and simple, informative prognostic factors in combination with large datasets as a tool to guide personalized cancer care.

Fully Automated Gross Tumor Volume Delineation From PET in Head and Neck Cancer Using Deep Learning Algorithms

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Isaac Shiri ◽  
Hossein Arabi ◽  
Amirhossein Sanaat ◽  
Elnaz Jenabi ◽  
Minerva Becker ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Neck Cancer ◽  
Gross Tumor Volume ◽  
Tumor Volume ◽  
Learning Algorithms ◽  
Volume Delineation

Patient classification of two-week wait referrals for suspected head and neck cancer: a machine learning approach

2019 ◽  
Vol 133 (10) ◽  
pp. 875-878 ◽  
Author(s):  
J W Moor ◽  
V Paleri ◽  
J Edwards
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Neck Cancer ◽  
Learning Algorithms ◽  
False Negative ◽  
Machine Learning Algorithms ◽  
Patient Classification ◽  
Cancer Symptoms ◽  
Machine Learning Classification

AbstractBackgroundMachine learning algorithms could potentially be used to classify patients referred on the two-week wait pathway for suspected head and neck cancer. Patients could be classified into ‘predicted cancer’ or ‘predicted non-cancer’ groups.MethodsA variety of machine learning algorithms were assessed using the clinical data of 5082 patients. These patients had previously been referred via the two-week wait pathway for suspected head and neck cancer to two separate tertiary referral centres in the UK. Outcomes from machine learning classification were analysed in comparison to known clinical diagnoses.ResultsVariational logistic regression was the most clinically useful technique of those chosen to perform the analysis and patient classification; the proportion of patients correctly classified as having ‘non-cancer’ was 25.8 per cent, with a false negative rate of 1 out of 1000.ConclusionMachine learning algorithms can accurately and effectively classify patients referred with suspected head and neck cancer symptoms.

scholarly journals Deep learning auto-segmentation and automated treatment planning for trismus risk reduction in head and neck cancer radiotherapy

2021 ◽  
Vol 19 ◽  
pp. 96-101
Author(s):  
Maria Thor ◽  
Aditi Iyer ◽  
Jue Jiang ◽  
Aditya Apte ◽  
Harini Veeraraghavan ◽  
...  
Keyword(s):  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Risk Reduction ◽  
Treatment Planning ◽  
Neck Cancer ◽  
Cancer Radiotherapy

Dynamic stochastic deep learning approaches for predicting geometric changes in head and neck cancer

2021 ◽  
Author(s):  
Julia M Pakela ◽  
Martha M Matuszak ◽  
Randall K Ten Haken ◽  
Daniel L McShan ◽  
Issam El Naqa
Keyword(s):  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Neck Cancer ◽  
Learning Approaches ◽  
Dynamic Stochastic ◽  
Geometric Changes

61: Conventional and Machine Learning Analyses to Differentiate Systematic Versus Random Inter-Fractional Changes in a Cohort of 250 Head and Neck Cancer Patients

2021 ◽  
Vol 163 ◽  
pp. S75
Author(s):  
Nabhya Harjai ◽  
Sarah Weppler ◽  
Craig A. Beers ◽  
Lukas V. Dyke ◽  
Colleen Schinkel ◽  
...  
Keyword(s):  
Machine Learning ◽  
Head And Neck Cancer ◽  
Head And Neck ◽  
Cancer Patients ◽  
Neck Cancer

scholarly journals Pretreatment Identification of Head and Neck Cancer Nodal Metastasis and Extranodal Extension Using Deep Learning Neural Networks

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Benjamin H. Kann ◽  
Sanjay Aneja ◽  
Gokoulakrichenane V. Loganadane ◽  
Jacqueline R. Kelly ◽  
Stephen M. Smith ◽  
...  
Keyword(s):  
Neural Networks ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Neck Cancer ◽  
Nodal Metastasis ◽  
Extranodal Extension

scholarly journals Benefits of deep learning for delineation of organs at risk in head and neck cancer

2019 ◽  
Vol 138 ◽  
pp. 68-74 ◽  
Author(s):  
J. van der Veen ◽  
S. Willems ◽  
S. Deschuymer ◽  
D. Robben ◽  
W. Crijns ◽  
...  
Keyword(s):  
At Risk ◽  
Head And Neck Cancer ◽  
Deep Learning ◽  
Head And Neck ◽  
Neck Cancer ◽  
Organs At Risk
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