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SYSTEMATIC REVIEW
Year : 2020  |  Volume : 1  |  Issue : 1  |  Page : 29-35

A systematic review of methodology in prognostic and prediction modelling with radiomics for non-small cell lung cancer - Part IA systematic review of methodology in prognostic and prediction modelling with radiomics for non-small cell lung cancer – Part I


1 Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands; Philips Research Bangalore, India
2 Department of Radiation Oncology (MAASTRO), GROW – School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands

Correspondence Address:
R Patil
Department of Radiation Oncology (MAASTRO), GROW - School for Oncology and Developmental Biology, Maastricht University Medical Centre (MUMC), Maastricht, The Netherlands Philips Research Bangalore

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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/WKMP-0197.298284

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Objective: The aim of this study was to systematically review the literature to synthesize and investigate repeatability and reproducibility of radiomic features, considered in building prognostic and prediction modelling with respect to NSCLC. Methods: The PubMed database was searched using combinations of the broad Haines and Ingui filters along with a set of text words specific to cancer, radiomics, reproducibility and repeatability. This systematic review was performed by two reviewers working entirely independently, and has been reported in compliance with PRISMA guidelines. Results: Out of 624 unique records, 41 full text articles were subjected to review. The studies were primarily in NSCLC. The imaging modalities were CT, PET and cone-beam CT – no studies addressed MR. Only 7 studies addressed in detail every methodological aspect related to image acquisition, pre-processing and feature extraction. Only few studies have made either the image set or software, or both, openly accessible. Due to heterogeneity in statistical metrics, a meta-analysis of pooled data was not possible. Conclusions: The repeatability and reproducibility of radiomic features are sensitive in varying degrees to processing details such as image acquisition settings, image reconstruction algorithm, image pre-processing and software used to extract radiomic features. Advances in Knowledge: Intra-class and concordance correlations were the most widely used statistical metrics, but arbitrarily selected cut-offs were variable. First-order features were overall more reproducible than shape metrics and textural features. Entropy was consistently reported as one of the most stable first-order features. There was no emergent consensus regarding either shape metrics or textural features.


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