In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.

Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study / Stanzione, Arnaldo; Galatola, Roberta; Cuocolo, Renato; Romeo, Valeria; Verde, Francesco; Mainenti, Pier Paolo; Brunetti, Arturo; Maurea, Simone. - In: DIAGNOSTICS. - ISSN 2075-4418. - 12:3(2022), p. 578. [10.3390/diagnostics12030578]

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

Stanzione, Arnaldo;Galatola, Roberta;Cuocolo, Renato
;
Romeo, Valeria;Verde, Francesco;Brunetti, Arturo;Maurea, Simone
2022

Abstract

In this study, we aimed to systematically review the current literature on radiomics applied to cross-sectional adrenal imaging and assess its methodological quality. Scopus, PubMed and Web of Science were searched to identify original research articles investigating radiomics applications on cross-sectional adrenal imaging (search end date February 2021). For qualitative synthesis, details regarding study design, aim, sample size and imaging modality were recorded as well as those regarding the radiomics pipeline (e.g., segmentation and feature extraction strategy). The methodological quality of each study was evaluated using the radiomics quality score (RQS). After duplicate removal and selection criteria application, 25 full-text articles were included and evaluated. All were retrospective studies, mostly based on CT images (17/25, 68%), with manual (19/25, 76%) and two-dimensional segmentation (13/25, 52%) being preferred. Machine learning was paired to radiomics in about half of the studies (12/25, 48%). The median total and percentage RQS scores were 2 (interquartile range, IQR = −5–8) and 6% (IQR = 0–22%), respectively. The highest and lowest scores registered were 12/36 (33%) and −5/36 (0%). The most critical issues were the absence of proper feature selection, the lack of appropriate model validation and poor data openness. The methodological quality of radiomics studies on adrenal cross-sectional imaging is heterogeneous and lower than desirable. Efforts toward building higher quality evidence are essential to facilitate the future translation into clinical practice.
2022
Radiomics in Cross-Sectional Adrenal Imaging: A Systematic Review and Quality Assessment Study / Stanzione, Arnaldo; Galatola, Roberta; Cuocolo, Renato; Romeo, Valeria; Verde, Francesco; Mainenti, Pier Paolo; Brunetti, Arturo; Maurea, Simone. - In: DIAGNOSTICS. - ISSN 2075-4418. - 12:3(2022), p. 578. [10.3390/diagnostics12030578]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/874830
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