Literature DB >> 34925606

Radiomics in Head and Neck Cancers Radiotherapy. Promises and Challenges.

Roxana Irina Iancu1, A D Zara2, C C Mirestean3, D P T Iancu1.   

Abstract

Radiomics, a subdomain of artificial intelligence, consists in extracting a large volume of data from all medical imaging techniques and correlating them with clinical data in order to build predictive and prognostic models. Radiomics is related to radiogenomics that correlates genetic mutations and molecular and biological characteristics of tissues with information extracted from medical imaging. Both are state-of-the-art fields of translational biomedical research. The ability to predict early patient survival and response to treatment, but also the capacity to identify tumor subtypes non-invasively, could make radiomics a key player with an essential role in personalized oncology. In head and neck cancer radiotherapy, radiomic algorithms can predict not only the response to radiochemotherapy or induction chemotherapy but also the need for planning through adaptive radiotherapy (ART). Radiomics can also predict the risk of severe toxicities, especially that of xerostomia. Given the benefit that a de-escalation of treatment can bring in selected cases to improve the quality of life, radiomics is expected to be part of the therapeutic decision for head and neck cancers in the near future, and may help identify cases where de-escalation of multimodal therapy will not jeopardize the therapeutic benefit.

Entities:  

Year:  2021        PMID: 34925606      PMCID: PMC8643547          DOI: 10.26574/maedica.2020.16.3.482

Source DB:  PubMed          Journal:  Maedica (Bucur)        ISSN: 1841-9038


  24 in total

Review 1.  Radiomics: Principles and radiotherapy applications.

Authors:  I Gardin; V Grégoire; D Gibon; H Kirisli; D Pasquier; J Thariat; P Vera
Journal:  Crit Rev Oncol Hematol       Date:  2019-03-29       Impact factor: 6.312

Review 2.  Prevalence of human papillomavirus in oropharyngeal and nonoropharyngeal head and neck cancer--systematic review and meta-analysis of trends by time and region.

Authors:  Hisham Mehanna; Tom Beech; Tom Nicholson; Iman El-Hariry; Christopher McConkey; Vinidh Paleri; Sally Roberts
Journal:  Head Neck       Date:  2012-01-20       Impact factor: 3.147

Review 3.  Radiomics and radiogenomics in head and neck squamous cell carcinoma: Potential contribution to patient management and challenges.

Authors:  Gema Bruixola; Elena Remacha; Ana Jiménez-Pastor; Delfina Dualde; Alba Viala; Jose Vicente Montón; Maider Ibarrola-Villava; Ángel Alberich-Bayarri; Andrés Cervantes
Journal:  Cancer Treat Rev       Date:  2021-07-26       Impact factor: 12.111

4.  Automated segmentation of the parotid gland based on atlas registration and machine learning: a longitudinal MRI study in head-and-neck radiation therapy.

Authors:  Xiaofeng Yang; Ning Wu; Guanghui Cheng; Zhengyang Zhou; David S Yu; Jonathan J Beitler; Walter J Curran; Tian Liu
Journal:  Int J Radiat Oncol Biol Phys       Date:  2014-10-13       Impact factor: 7.038

Review 5.  Adaptive radiotherapy for head and neck cancer.

Authors:  J Castelli; A Simon; C Lafond; N Perichon; B Rigaud; E Chajon; B De Bari; M Ozsahin; J Bourhis; R de Crevoisier
Journal:  Acta Oncol       Date:  2018-10-05       Impact factor: 4.089

6.  Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer.

Authors:  Chintan Parmar; Patrick Grossmann; Derek Rietveld; Michelle M Rietbergen; Philippe Lambin; Hugo J W L Aerts
Journal:  Front Oncol       Date:  2015-12-03       Impact factor: 6.244

Review 7.  Radiomics in radiation oncology-basics, methods, and limitations.

Authors:  Philipp Lohmann; Khaled Bousabarah; Mauritius Hoevels; Harald Treuer
Journal:  Strahlenther Onkol       Date:  2020-07-09       Impact factor: 3.621

8.  Repeatability and Reproducibility of Radiomic Features: A Systematic Review.

Authors:  Alberto Traverso; Leonard Wee; Andre Dekker; Robert Gillies
Journal:  Int J Radiat Oncol Biol Phys       Date:  2018-06-05       Impact factor: 7.038

9.  Pretreatment Prediction of Adaptive Radiation Therapy Eligibility Using MRI-Based Radiomics for Advanced Nasopharyngeal Carcinoma Patients.

Authors:  Ting-Ting Yu; Sai-Kit Lam; Lok-Hang To; Ka-Yan Tse; Nong-Yi Cheng; Yeuk-Nam Fan; Cheuk-Lai Lo; Ka-Wa Or; Man-Lok Chan; Ka-Ching Hui; Fong-Chi Chan; Wai-Ming Hui; Lo-Kin Ngai; Francis Kar-Ho Lee; Kwok-Hung Au; Celia Wai-Yi Yip; Yong Zhang; Jing Cai
Journal:  Front Oncol       Date:  2019-10-16       Impact factor: 6.244

Review 10.  Combining molecular and imaging metrics in cancer: radiogenomics.

Authors:  Roberto Lo Gullo; Isaac Daimiel; Elizabeth A Morris; Katja Pinker
Journal:  Insights Imaging       Date:  2020-01-03
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  1 in total

1.  Textural features reflecting local activity of the hippocampus improve the diagnosis of Alzheimer's disease and amnestic mild cognitive impairment: A radiomics study based on functional magnetic resonance imaging.

Authors:  Luoyu Wang; Qi Feng; Xiuhong Ge; Fenyang Chen; Bo Yu; Bing Chen; Zhengluan Liao; Biying Lin; Yating Lv; Zhongxiang Ding
Journal:  Front Neurosci       Date:  2022-08-08       Impact factor: 5.152

  1 in total

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