Prostate MRI radiomics: A systematic review and radiomic quality score assessment. Issue 129 (August 2020)

• Record Type: Journal Article
• Authors: Stanzione, Arnaldo; Gambardella, Michele; Cuocolo, Renato; Ponsiglione, Andrea; Romeo, Valeria; Imbriaco, Massimo
• Is Part Of: European journal of radiology. Issue 129(2020)
• Publication Date: 2020-08
• Subjects: Systematic review -- Prostate cancer -- MRI -- Radiomics quality score -- Machine learning; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2020.109095
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: The methodological quality of prostate MRI radiomics studies is overall low. Feature robustness techniques and external datasets for validations are lacking. The radiomics quality score tool might be too stringent and demanding. Increased quality is necessary to implement radiomics in clinical prostate imaging. Abstract: Background: Radiomics have the potential to further increase the value of MRI in prostate cancer management. However, implementation in clinical practice is still far and concerns have been raised regarding the methodological quality of radiomic studies. Therefore, we aimed to systematically review the literature to assess the quality of prostate MRI radiomic studies using the radiomics quality score (RQS). Methods: Multiple medical literature archives (PubMed, Web of Science and EMBASE) were searched to retrieve original investigations focused on prostate MRI radiomic approaches up to the end of June 2019. Three researchers independently assessed each paper using the RQS. Data from the most experienced researcher were used for descriptive analysis. Inter-rater reproducibility was assessed using the intraclass correlation coefficient (ICC) on the total RQS score. Results: 73 studies were included in the analysis. Overall, the average RQS total score was 7.93 ± 5.13 on a maximum of 36 points, with a final average percentage of 23 ± 13%. Among the most critical items, the lack of feature robustness testing strategies and external validation datasets. The ICC resulted poor to moderate, with an average value of 0.57 and 95% Confidence Intervals between 0.44 and 0.69. Conclusions: Current studies on prostate MRI radiomics still lack the quality required to allow their introduction in clinical practice.