Literature DB >> 32980727

Systematic review of sarcomas radiomics studies: Bridging the gap between concepts and clinical applications?

Amandine Crombé1, David Fadli2, Antoine Italiano3, Olivier Saut4, Xavier Buy2, Michèle Kind2.   

Abstract

OBJECTIVES: Sarcomas are a model for intra- and inter-tumoral heterogeneities making them particularly suitable for radiomics analyses. Our purposes were to review the aims, methods and results of radiomics studies involving sarcomas
METHODS: Pubmed and Web of Sciences databases were searched for radiomics or textural studies involving bone, soft-tissues and visceral sarcomas until June 2020. Two radiologists evaluated their objectives, results and quality of their methods, imaging pre-processing and machine-learning workflow helped by the items of the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2), Image Biomarker Standardization Initiative (IBSI) and 'Radiomics Quality Score' (RQS). Statistical analyses included inter-reader agreements, correlations between methodological assessments, scientometrics indices, and their changes over years, and between RQS, number of patients and models performance.
RESULTS: Fifty-two studies were included involving: soft-tissue sarcomas (29/52, 55.8 %), bone sarcomas (15/52, 28.8 %), gynecological sarcomas (6/52, 11.5 %) and mixed sarcomas (2/52, 3.8 %), mostly imaged with MRI (36/52, 69.2 %), for a total of distinct patients. Median RQS was 4.5 (28.4 % of the maximum, range: -7 - 17). Performances of predictive models and number of patients negatively correlated (p = 0.027). None of the studies detailed all the items from the IBSI guidelines. There was a significant increase in studies' impact factors since the establishing of the RQS in 2017 (p = 0.038).
CONCLUSION: Although showing promising results, further efforts are needed to make sarcoma radiomics studies reproducible with an acceptable level of evidence. A better knowledge of the RQS and IBSI reporting guidelines could improve the quality of sarcoma radiomics studies and accelerate clinical applications.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Machine learning; Magnetic resonance imaging; Quality improvement; Radiomics; Sarcoma

Mesh:

Year:  2020        PMID: 32980727     DOI: 10.1016/j.ejrad.2020.109283

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


  8 in total

1.  Quality of science and reporting for radiomics in cardiac magnetic resonance imaging studies: a systematic review.

Authors:  Suyon Chang; Kyunghwa Han; Young Joo Suh; Byoung Wook Choi
Journal:  Eur Radiol       Date:  2022-03-01       Impact factor: 5.315

Review 2.  Radiomics of Musculoskeletal Sarcomas: A Narrative Review.

Authors:  Cristiana Fanciullo; Salvatore Gitto; Eleonora Carlicchi; Domenico Albano; Carmelo Messina; Luca Maria Sconfienza
Journal:  J Imaging       Date:  2022-02-13

Review 3.  Gaps and Opportunities of Artificial Intelligence Applications for Pediatric Oncology in European Research: A Systematic Review of Reviews and a Bibliometric Analysis.

Authors:  Alberto Eugenio Tozzi; Francesco Fabozzi; Megan Eckley; Ileana Croci; Vito Andrea Dell'Anna; Erica Colantonio; Angela Mastronuzzi
Journal:  Front Oncol       Date:  2022-05-31       Impact factor: 5.738

4.  Current progress and quality of radiomic studies for predicting EGFR mutation in patients with non-small cell lung cancer using PET/CT images: a systematic review.

Authors:  Meilinuer Abdurixiti; Mayila Nijiati; Rongfang Shen; Qiu Ya; Naibijiang Abuduxiku; Mayidili Nijiati
Journal:  Br J Radiol       Date:  2021-05-12       Impact factor: 3.629

5.  Quality assessment standards in artificial intelligence diagnostic accuracy systematic reviews: a meta-research study.

Authors:  Shruti Jayakumar; Viknesh Sounderajah; Pasha Normahani; Leanne Harling; Sheraz R Markar; Hutan Ashrafian; Ara Darzi
Journal:  NPJ Digit Med       Date:  2022-01-27

6.  Quality of Radiomics Research on Brain Metastasis: A Roadmap to Promote Clinical Translation.

Authors:  Chae Jung Park; Yae Won Park; Sung Soo Ahn; Dain Kim; Eui Hyun Kim; Seok-Gu Kang; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Korean J Radiol       Date:  2022-01       Impact factor: 3.500

7.  An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics.

Authors:  Jingyu Zhong; Yangfan Hu; Guangcheng Zhang; Yue Xing; Defang Ding; Xiang Ge; Zhen Pan; Qingcheng Yang; Qian Yin; Huizhen Zhang; Huan Zhang; Weiwu Yao
Journal:  Insights Imaging       Date:  2022-08-20

8.  CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies.

Authors:  Salvatore Gitto; Renato Cuocolo; Domenico Albano; Francesco Morelli; Lorenzo Carlo Pescatori; Carmelo Messina; Massimo Imbriaco; Luca Maria Sconfienza
Journal:  Insights Imaging       Date:  2021-06-02
  8 in total

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