Literature DB >> 31682155

Radiomic analysis for response assessment in advanced head and neck cancers, a distant dream or an inevitable reality? A systematic review of the current level of evidence.

Amrita Guha1,2, Steve Connor3, Mustafa Anjari3, Harish Naik4, Musib Siddiqui5, Gary Cook3, Vicky Goh3.   

Abstract

OBJECTIVE: The recent increase in publications on radiomic analysis as means to produce diagnostic and predictive biomarkers in head and neck cancers (HNCC) reveal complicated and often conflicting results. The objective of this paper is to systematically review the published data, and evaluate the current level of evidence accumulated that would determine clinical application.
METHODS: Data sources: Articles in the English language available on the Ovid-MEDLINE and Embase databases were used for the literature search. Study selection:Studies which evaluated the role of radiomics as a predictive or prognostic tool for response assessment in HNCC were included in this review.Study appraisal and synthesis methods: The authors set-out to perform a meta-analysis, however given the small number of studies retrieved that presented adequate data, combined with excessive methodological heterogeneity, we could only perform a structured descriptive systematic review summarizing the key findings. Independent extraction of articles was performed by two authors using predefined data fields and any disagreement was resolved by consensus.
RESULTS: Though most papers concluded that radiomics is an effective predictive and prognostic biomarker in the management of HNCC, significant heterogeneity exists in the study methodology and statistical modelling; thus precluding accurate mathematical comparison or the ability to make clear recommendations going forwards. Moreover, most studies have not been validated and the reproducibility of their results will be a challenge.
CONCLUSION: Until robust external validation studies on the reproducibility and accuracy of radiomic analysis methods on HNCC are carried out, the current level of evidence remains low, with the authors advising caution against hasty implementation of these tools in the multidisciplinary clinic. ADVANCES IN KNOWLEDGE: This review is the first attempt to critically analyze the merits and demerits of currently published literature on tumour heterogeneity studies in HNCC, and identifies specific loop holes that need to be addressed by research groups, for a meaningful clinical translation of this potential biomarker.

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Year:  2019        PMID: 31682155      PMCID: PMC7055439          DOI: 10.1259/bjr.20190496

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  28 in total

1.  Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.

Authors:  Bin Zhang; Xin He; Fusheng Ouyang; Dongsheng Gu; Yuhao Dong; Lu Zhang; Xiaokai Mo; Wenhui Huang; Jie Tian; Shuixing Zhang
Journal:  Cancer Lett       Date:  2017-06-10       Impact factor: 8.679

2.  Computed Tomography Radiomics Predicts HPV Status and Local Tumor Control After Definitive Radiochemotherapy in Head and Neck Squamous Cell Carcinoma.

Authors:  Marta Bogowicz; Oliver Riesterer; Kristian Ikenberg; Sonja Stieb; Holger Moch; Gabriela Studer; Matthias Guckenberger; Stephanie Tanadini-Lang
Journal:  Int J Radiat Oncol Biol Phys       Date:  2017-06-15       Impact factor: 7.038

3.  Post-radiochemotherapy PET radiomics in head and neck cancer - The influence of radiomics implementation on the reproducibility of local control tumor models.

Authors:  Marta Bogowicz; Ralph T H Leijenaar; Stephanie Tanadini-Lang; Oliver Riesterer; Martin Pruschy; Gabriela Studer; Jan Unkelbach; Matthias Guckenberger; Ender Konukoglu; Philippe Lambin
Journal:  Radiother Oncol       Date:  2017-11-06       Impact factor: 6.280

4.  Predictive and prognostic value of CT based radiomics signature in locally advanced head and neck cancers patients treated with concurrent chemoradiotherapy or bioradiotherapy and its added value to Human Papillomavirus status.

Authors:  Dan Ou; Pierre Blanchard; Silvia Rosellini; Antonin Levy; France Nguyen; Ralph T H Leijenaar; Ingrid Garberis; Philippe Gorphe; François Bidault; Charles Ferté; Charlotte Robert; Odile Casiraghi; Jean-Yves Scoazec; Philippe Lambin; Stephane Temam; Eric Deutsch; Yungan Tao
Journal:  Oral Oncol       Date:  2017-06-26       Impact factor: 5.337

5.  The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration.

Authors:  Alessandro Liberati; Douglas G Altman; Jennifer Tetzlaff; Cynthia Mulrow; Peter C Gøtzsche; John P A Ioannidis; Mike Clarke; P J Devereaux; Jos Kleijnen; David Moher
Journal:  PLoS Med       Date:  2009-07-21       Impact factor: 11.069

6.  External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma.

Authors:  Ralph T H Leijenaar; Sara Carvalho; Frank J P Hoebers; Hugo J W L Aerts; Wouter J C van Elmpt; Shao Hui Huang; Biu Chan; John N Waldron; Brian O'sullivan; Philippe Lambin
Journal:  Acta Oncol       Date:  2015-08-12       Impact factor: 4.089

7.  Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement.

Authors:  David Moher; Larissa Shamseer; Mike Clarke; Davina Ghersi; Alessandro Liberati; Mark Petticrew; Paul Shekelle; Lesley A Stewart
Journal:  Syst Rev       Date:  2015-01-01

8.  Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics.

Authors:  Bin Zhang; Fusheng Ouyang; Dongsheng Gu; Yuhao Dong; Lu Zhang; Xiaokai Mo; Wenhui Huang; Shuixing Zhang
Journal:  Oncotarget       Date:  2017-08-02

9.  Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma.

Authors:  Fu-Sheng Ouyang; Bao-Liang Guo; Bin Zhang; Yu-Hao Dong; Lu Zhang; Xiao-Kai Mo; Wen-Hui Huang; Shui-Xing Zhang; Qiu-Gen Hu
Journal:  Oncotarget       Date:  2017-08-24

Review 10.  Response assessment after induction chemotherapy for head and neck squamous cell carcinoma: From physical examination to modern imaging techniques and beyond.

Authors:  Remco de Bree; Gregory T Wolf; Bart de Keizer; Iain J Nixon; Dana M Hartl; Arlene A Forastiere; Missak Haigentz; Alessandra Rinaldo; Juan P Rodrigo; Nabil F Saba; Carlos Suárez; Jan B Vermorken; Alfio Ferlito
Journal:  Head Neck       Date:  2017-08-17       Impact factor: 3.147

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  6 in total

1.  Studying local tumour heterogeneity on MRI and FDG-PET/CT to predict response to neoadjuvant chemoradiotherapy in rectal cancer.

Authors:  Niels W Schurink; Simon R van Kranen; Maaike Berbee; Wouter van Elmpt; Frans C H Bakers; Sander Roberti; Joost J M van Griethuysen; Lisa A Min; Max J Lahaye; Monique Maas; Geerard L Beets; Regina G H Beets-Tan; Doenja M J Lambregts
Journal:  Eur Radiol       Date:  2021-02-10       Impact factor: 5.315

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

Authors:  Roxana Irina Iancu; A D Zara; C C Mirestean; D P T Iancu
Journal:  Maedica (Bucur)       Date:  2021-09

Review 3.  Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment.

Authors:  Nina J Wesdorp; Tessa Hellingman; Elise P Jansma; Jan-Hein T M van Waesberghe; Ronald Boellaard; Cornelis J A Punt; Joost Huiskens; Geert Kazemier
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-12-16       Impact factor: 9.236

4.  Assessment of clinical radiosensitivity in patients with head-neck squamous cell carcinoma from pre-treatment quantitative ultrasound radiomics.

Authors:  Laurentius Oscar Osapoetra; Archya Dasgupta; Daniel DiCenzo; Kashuf Fatima; Karina Quiaoit; Murtuza Saifuddin; Irene Karam; Ian Poon; Zain Husain; William T Tran; Lakshmanan Sannachi; Gregory J Czarnota
Journal:  Sci Rep       Date:  2021-03-17       Impact factor: 4.379

5.  Early Prediction of Planning Adaptation Requirement Indication Due to Volumetric Alterations in Head and Neck Cancer Radiotherapy: A Machine Learning Approach.

Authors:  Vasiliki Iliadou; Ioannis Kakkos; Pantelis Karaiskos; Vassilis Kouloulias; Kalliopi Platoni; Anna Zygogianni; George K Matsopoulos
Journal:  Cancers (Basel)       Date:  2022-07-22       Impact factor: 6.575

6.  Site-Specific Variation in Radiomic Features of Head and Neck Squamous Cell Carcinoma and Its Impact on Machine Learning Models.

Authors:  Xiaoyang Liu; Farhad Maleki; Nikesh Muthukrishnan; Katie Ovens; Shao Hui Huang; Almudena Pérez-Lara; Griselda Romero-Sanchez; Sahir Rai Bhatnagar; Avishek Chatterjee; Marc Philippe Pusztaszeri; Alan Spatz; Gerald Batist; Seyedmehdi Payabvash; Stefan P Haider; Amit Mahajan; Caroline Reinhold; Behzad Forghani; Brian O'Sullivan; Eugene Yu; Reza Forghani
Journal:  Cancers (Basel)       Date:  2021-07-24       Impact factor: 6.639

  6 in total

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