Literature DB >> 34981135

Quality assessment of radiomics research in cardiac CT: a systematic review.

Suji Lee1, Kyunghwa Han1, Young Joo Suh2.   

Abstract

OBJECTIVES: To assess the quality of current radiomics research on cardiac CT using radiomics quality score (RQS) and Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) systems.
METHODS: Systematic searches of PubMed and EMBASE were performed to identify all potentially relevant original research articles about cardiac CT radiomics. Fifteen original research articles were selected. Two cardiac radiologists assessed the quality of the methodology adopted in those studies according to the RQS and TRIPOD guidelines. Basic adherence rates for the following six key domains were evaluated: image protocol and reproducibility, feature reduction and validation, biologic/clinical utility, performance index, high level of evidence, and open science.
RESULTS: Among the 15 included articles, six (40%) were about coronary artery disease and six (40%) were about myocardial infarction. The mean RQS was 9.9 ± 7.3 (27.4% of the ideal score of 36), and the basic adherence rate was 44.6%. Fourteen (93.3%) and nine (60%) studies performed feature selection and validation, but only two (13.3%) of them performed external validation. Two studies (13.3%) were prospective, and only one study (6.7%) conducted calibration analysis and stated the potential clinical utility. None of the studies conducted phantom study and cost-effective analysis. The overall adherence rate for TRIPOD was 63%.
CONCLUSION: The quality of radiomics studies in cardiac CT is currently insufficient. A higher level of evidence is required, and analysis of clinical utility and calibration of model performance need to be improved. KEY POINTS: • The quality of science of radiomics studies in cardiac CT is currently insufficient. • No study conducted a phantom study or cost-effective analysis, with further limitations being demonstrated in a high level of evidence for radiomics studies. • Analysis of clinical utility and calibration of model performance need to be improved, and a higher level of evidence is required.
© 2021. The Author(s), under exclusive licence to European Society of Radiology.

Entities:  

Keywords:  Heart; Machine learning; Quality improvement; Tomography, X-ray computed

Mesh:

Year:  2022        PMID: 34981135     DOI: 10.1007/s00330-021-08429-0

Source DB:  PubMed          Journal:  Eur Radiol        ISSN: 0938-7994            Impact factor:   5.315


  1 in total

1.  A systematic review of radiomics in osteosarcoma: utilizing radiomics quality score as a tool promoting clinical translation.

Authors:  Jingyu Zhong; Yangfan Hu; Liping Si; Geng Jia; Yue Xing; Huan Zhang; Weiwu Yao
Journal:  Eur Radiol       Date:  2020-09-02       Impact factor: 5.315

  1 in total
  1 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
  1 in total

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