Artificial Intelligence and Deep Learning of Head and Neck Cancer

Artificial intelligence (AI)-based models have become a growing area of interest in predictive medicine and have the potential to aid physician decision-making to improve patient outcomes. Imaging and radiomics play an increasingly important role in these models. This review summarizes recent developments in the field of radiomics for AI in head and neck cancer. Prediction models for oncologic outcomes, treatment toxicity, and pathological findings have all been created. Exploratory studies are promising; however, validation studies that demonstrate consistency, reproducibility, and prognostic impact remain uncommon. Prospective clinical trials with standardized procedures are required for clinical translation.

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

10.21203/rs.3.rs-185311/v1 ◽

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.

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Interobserver variability in organ at risk delineation in head and neck cancer.

10.21203/rs.3.rs-39575/v2 ◽

2020 ◽

Author(s):

Julie van der Veen ◽

Akos Gulyban ◽

Siri Willems ◽

Frederik Maes ◽

Sandra Nuyts

Keyword(s):

Artificial Intelligence ◽

At Risk ◽

Head And Neck Cancer ◽

Head And Neck ◽

Neck Cancer ◽

Interobserver Variability ◽

Treatment Plan ◽

Consensus Guidelines ◽

International Consensus ◽

Organ At Risk

Abstract Background: In radiotherapy inaccuracy in organ at risk (OAR) delineation can impact treatment plan optimisation and treatment plan evaluation. Brouwer et al. showed significant interobserver variability (IOV) in OAR delineation in head and neck cancer (HNC) and published international consensus guidelines (ICG) for OAR delineation in 2015. The aim of our study was to evaluate IOV in the presence of these guidelines. Methods: HNC radiation oncologists (RO) from each Belgian radiotherapy centre were invited to complete a survey and submit contours for 5 HNC cases. Reference contours (OARref) were obtained by a clinically validated artificial intelligence-tool trained using ICG. Dice similarity coefficients (DSC), mean surface distance (MSD) and 95% Hausdorff distances (HD95) were used for comparison.Results: Fourteen of twenty-two RO (64%) completed the survey and submitted delineations. Thirteen (93%) confirmed the use of delineation guidelines, of which six (43%) used the ICG. The OARs whose delineations agreed best with the OARref were mandible (median DSC 0.9, range [0.8-0.9]; median MSD 1.1mm, range [0.8-8.3], median HD95 3.4mm, range [1.5-38.7]), brainstem (median DSC 0.9 [0.6-0.9]; median MSD 1.5mm [1.1-4.0], median HD95 4.0mm [2.3-15.0]), submandibular glands (median DSC 0.8 [0.5-0.9]; median MSD 1.2mm [0.9-2.5], median HD95 3.1mm [1.8-12.2]) and parotids (median DSC 0.9 [0.6-0.9]; median MSD 1.9mm [1.2-4.2], median HD95 5.1mm [3.1-19.2]). Oral cavity, cochleas, PCMs, supraglottic larynx and glottic area showed more variation. RO who used the consensus guidelines showed significantly less IOV (p=0.008).Conclusion: Although ICG for delineation of OARs in HNC exist, they are only implemented by about half of RO participating in this study, which partly explains the delineation variability. However, this study highlights that guidelines alone do not suffice to eliminate IOV and that more effort needs to be done to accomplish further treatment standardisation, for example with artificial intelligence.

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