Background: Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workfows and facilitate clinical transferability. Results: Out of 278 identifed papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n=12) or soft-tissue (n=37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n=2, 4%). The intraclass correlation coefcient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to diferent scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions: The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the feld of radiomics from a preclinical research area to the clinical stage.

CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies / S. Gitto, R. Cuocolo, D. Albano, F. Morelli, L.C. Pescatori, C. Messina, M. Imbriaco, L.M. Sconfienza. - In: INSIGHTS INTO IMAGING. - ISSN 1869-4101. - 12:1(2021), pp. 68.1-68.14. [10.1186/s13244-021-01008-3]

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

S. Gitto
Primo
;
F. Morelli;L.C. Pescatori;C. Messina;L.M. Sconfienza
Ultimo
2021

Abstract

Background: Feature reproducibility and model validation are two main challenges of radiomics. This study aims to systematically review radiomic feature reproducibility and predictive model validation strategies in studies dealing with CT and MRI radiomics of bone and soft-tissue sarcomas. The ultimate goal is to promote achieving a consensus on these aspects in radiomic workfows and facilitate clinical transferability. Results: Out of 278 identifed papers, forty-nine papers published between 2008 and 2020 were included. They dealt with radiomics of bone (n=12) or soft-tissue (n=37) tumors. Eighteen (37%) studies included a feature reproducibility analysis. Inter-/intra-reader segmentation variability was the theme of reproducibility analysis in 16 (33%) investigations, outnumbering the analyses focused on image acquisition or post-processing (n=2, 4%). The intraclass correlation coefcient was the most commonly used statistical method to assess reproducibility, which ranged from 0.6 and 0.9. At least one machine learning validation technique was used for model development in 25 (51%) papers, and K-fold cross-validation was the most commonly employed. A clinical validation of the model was reported in 19 (39%) papers. It was performed using a separate dataset from the primary institution (i.e., internal validation) in 14 (29%) studies and an independent dataset related to diferent scanners or from another institution (i.e., independent validation) in 5 (10%) studies. Conclusions: The issues of radiomic feature reproducibility and model validation varied largely among the studies dealing with musculoskeletal sarcomas and should be addressed in future investigations to bring the feld of radiomics from a preclinical research area to the clinical stage.
Artificial intelligence; Radiomics; Sarcoma; Texture analysis
Settore MED/36 - Diagnostica per Immagini e Radioterapia
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/848583
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