Literature DB >> 33962253

MRI based radiomics in nasopharyngeal cancer: Systematic review and perspectives using radiomic quality score (RQS) assessment.

Gaia Spadarella1, Giuseppina Calareso2, Enrico Garanzini2, Lorenzo Ugga3, Alberto Cuocolo3, Renato Cuocolo4.   

Abstract

BACKGROUND: MRI based radiomics has the potential to better define tumor biology compared to qualitative MRI assessment and support decisions in patients affected by nasopharyngeal carcinoma. Aim of this review was to systematically evaluate the methodological quality of studies using MRI- radiomics for nasopharyngeal cancer patient evaluation.
METHODS: A systematic search was performed in PUBMED, WEB OF SCIENCE and SCOPUS using "MRI, magnetic resonance imaging, radiomic, texture analysis, nasopharyngeal carcinoma, nasopharyngeal cancer" in all possible combinations. The methodological quality of study included ( = 24) was evaluated according to the RQS (Radiomic quality score). Subgroup, for journal type (imaging/clinical) and biomarker (prognostic/predictive), and correlation, between RQS and journal Impact Factor, analyses were performed. Mann-Whitney U test and Spearman's correlation were performed. P value < .05 were defined as statistically significant.
RESULTS: Overall, no studies reported a phantom study or a test re-test for assessing stability in image, biological correlation or open science data. Only 8% of them included external validation. Almost half of articles (45 %) performed multivariable analysis with non-radiomics features. Only 1 study was prospective (4%). The mean RQS was 7.5 ± 5.4. No significant differences were detected between articles published in clinical/imaging journal and between studies with a predictive or prognostic biomarker. No significant correlation was found between total RQS and Impact Factor of the year of publication (p always > 0.05).
CONCLUSIONS: Radiomic articles in nasopharyngeal cancer are mostly of low methodological quality. The greatest limitations are the lack of external validation, biological correlates, prospective design and open science.
Copyright © 2021 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computer-assisted; Head and neck cancer; Image processing; Journal impact factor; Magnetic resonance imaging; Nasopharyngeal neoplasm; Systematic review

Year:  2021        PMID: 33962253     DOI: 10.1016/j.ejrad.2021.109744

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  10 in total

1.  Diagnosis of Idiopathic Pulmonary Fibrosis in High-Resolution Computed Tomography Scans Using a Combination of Handcrafted Radiomics and Deep Learning.

Authors:  Turkey Refaee; Zohaib Salahuddin; Anne-Noelle Frix; Chenggong Yan; Guangyao Wu; Henry C Woodruff; Hester Gietema; Paul Meunier; Renaud Louis; Julien Guiot; Philippe Lambin
Journal:  Front Med (Lausanne)       Date:  2022-06-23

Review 2.  Cardiac CT and MRI radiomics: systematic review of the literature and radiomics quality score assessment.

Authors:  Andrea Ponsiglione; Arnaldo Stanzione; Renato Cuocolo; Raffaele Ascione; Michele Gambardella; Marco De Giorgi; Carmela Nappi; Alberto Cuocolo; Massimo Imbriaco
Journal:  Eur Radiol       Date:  2021-11-23       Impact factor: 7.034

3.  The impact of radiomics for human papillomavirus status prediction in oropharyngeal cancer: systematic review and radiomics quality score assessment.

Authors:  Gaia Spadarella; Lorenzo Ugga; Giuseppina Calareso; Rossella Villa; Serena D'Aniello; Renato Cuocolo
Journal:  Neuroradiology       Date:  2022-04-23       Impact factor: 2.995

Review 4.  Magnetic Resonance Imaging-Based Radiomics for the Prediction of Progression-Free Survival in Patients with Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.

Authors:  Sangyun Lee; Yangsean Choi; Min-Kook Seo; Jinhee Jang; Na-Young Shin; Kook-Jin Ahn; Bum-Soo Kim
Journal:  Cancers (Basel)       Date:  2022-01-27       Impact factor: 6.639

5.  18F-FDG PET Radiomics as Predictor of Treatment Response in Oesophageal Cancer: A Systematic Review and Meta-Analysis.

Authors:  Letizia Deantonio; Maria Luisa Garo; Gaetano Paone; Maria Carla Valli; Stefano Cappio; Davide La Regina; Marco Cefali; Maria Celeste Palmarocchi; Alberto Vannelli; Sara De Dosso
Journal:  Front Oncol       Date:  2022-03-15       Impact factor: 6.244

6.  Radiomics Nomogram Based on Multiple-Sequence Magnetic Resonance Imaging Predicts Long-Term Survival in Patients Diagnosed With Nasopharyngeal Carcinoma.

Authors:  Kai Liu; Qingtao Qiu; Yonghui Qin; Ting Chen; Diangang Zhang; Li Huang; Yong Yin; Ruozheng Wang
Journal:  Front Oncol       Date:  2022-04-07       Impact factor: 5.738

Review 7.  Methodological quality of machine learning-based quantitative imaging analysis studies in esophageal cancer: a systematic review of clinical outcome prediction after concurrent chemoradiotherapy.

Authors:  Zhenwei Shi; Zhen Zhang; Zaiyi Liu; Lujun Zhao; Zhaoxiang Ye; Andre Dekker; Leonard Wee
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-12-23       Impact factor: 10.057

Review 8.  State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma.

Authors:  Anna Castaldo; Davide Raffaele De Lucia; Giuseppe Pontillo; Marco Gatti; Sirio Cocozza; Lorenzo Ugga; Renato Cuocolo
Journal:  Diagnostics (Basel)       Date:  2021-06-30

9.  Development of a Radiotherapy Localisation Computed Tomography-Based Radiomic Model for Predicting Survival in Patients With Nasopharyngeal Carcinoma Treated With Intensity-Modulated Radiotherapy Following Induction Chemotherapy.

Authors:  Xiaoyue Li; Han Chen; Feipeng Zhao; Yun Zheng; Haowen Pang; Li Xiang
Journal:  Cancer Control       Date:  2022 Jan-Dec       Impact factor: 3.302

Review 10.  Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study.

Authors:  Arnaldo Stanzione; Roberta Galatola; Renato Cuocolo; Valeria Romeo; Francesco Verde; Pier Paolo Mainenti; Arturo Brunetti; Simone Maurea
Journal:  Diagnostics (Basel)       Date:  2022-02-24
  10 in total

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