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J Am Coll Radiol. 2019 Jun 22. pii: S1546-1440(19)30697-0. doi: 10.1016/j.jacr.2019.05.045. [Epub ahead of print]

Feeding the Data Monster: Data Science in Head and Neck Cancer for Personalized Therapy.

Author information

1
Faculty of Science, University of Oradea, Oradea, Romania; Cancer Research Institute and School of Health Sciences, University of South Australia, Adelaide, Australia.
2
Cancer Research Institute and School of Health Sciences, University of South Australia, Adelaide, Australia; South Australia Medical Imaging Physics, Adelaide, SA 5000, Australia.
3
Cancer Research Institute and School of Health Sciences, University of South Australia, Adelaide, Australia; School of Physical Sciences, University of Adelaide, North Terrace, Adelaide, Australia. Electronic address: Eva.Bezak@unisa.edu.au.

Abstract

OBJECTIVE:

Head and neck carcinomas are clinically challenging malignancies because of tumor heterogeneities and resilient tumor subvolumes that require individualized treatment planning and delivery for an improved outcome. Although current approaches to diagnosis and therapy have boosted locoregional control, the long-term survival in this patient group remains unchanged over the last decades. A new approach to head and neck cancer management is therefore needed to better identify patient subgroups that are responsive to specific therapies. The aim of this article is to review the current status of knowledge and practice utilizing big data toward personalized therapy in head and neck cancers based on CT and PET imaging modalities.

METHODS:

Literature published in English since 2000 was searched using Medline. Additional articles were retrieved via pearling of identified literature. Publications were reviewed and summarized in tabulated format.

RESULTS:

Studies based on big data in head and neck cancer are limited; however, the field of radiomics is under continuous development and provides valuable input for personalized treatment. Using PET/PET CT biomarkers for patient treatment individualization and response prediction seems promising, especially in regard to detection of hypoxia and clonogenic cancer stem cells. Literature shows that macroscopic changes in medical images (whether structural or functional) are correlated with biologic and biochemical changes within a tumor.

CONCLUSION:

Current trends in data science suggest that the ideal model for decision support in head and neck cancers should be based on human-machine collaboration, namely, on (1) software-based algorithms, (2) physician innovation collaboratives, and (3) clinician mix optimization.

KEYWORDS:

Head and neck cancer; human papilloma virus; outcome prediction; patient stratification; radiomics

PMID:
31238024
DOI:
10.1016/j.jacr.2019.05.045

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