A radiomics pipeline dedicated to Breast MRI: validation on a multi-scanner phantom study

• Published in: Magma (New York, N.Y.) N.Y.), 2021-06, Vol.34 (3), p.355-366
• Main Authors: Saint Martin, Marie-Judith, Orlhac, Fanny, Akl, Pia, Khalid, Fahad, Nioche, Christophe, Buvat, Irène, Malhaire, Caroline, Frouin, Frédérique
• Format: Article
• Language: English
• Subjects: Basic Science - Image analysis; Biomedical Engineering and Bioengineering; Computer Appl. in Life Sciences; Health Informatics; Imaging; Life Sciences; Medicine; Medicine & Public Health; Radiology; Research Article; Solid State Physics
• ISSN: 0968-5243; 1352-8661
• DOI: 10.1007/s10334-020-00892-y

Objective Quantitative analysis in MRI is challenging due to variabilities in intensity distributions across patients, acquisitions and scanners and suffers from bias field inhomogeneity. Radiomic studies are impacted by these effects that affect radiomic feature values. This paper describes a dedicated pipeline to increase reproducibility in breast MRI radiomic studies. Materials and methods T1, T2, and T1-DCE MR images of two breast phantoms were acquired using two scanners and three dual breast coils. Images were retrospectively corrected for bias field inhomogeneity and further normalised using Z score or histogram matching. Extracted radiomic features were harmonised between coils by the ComBat method. The whole pipeline was assessed qualitatively and quantitatively using statistical comparisons on two series of radiomic feature values computed in the gel mimicking the normal breast tissue or in dense lesions. Results Intra and inter-acquisition variabilities were strongly reduced by the standardisation pipeline. Harmonisation by ComBat lowered the percentage of radiomic features significantly different between the three coils from 87% after bias field correction and MR normalisation to 3% in the gel, while preserving or improving performance of lesion classification in the phantoms. Discussion A dedicated standardisation pipeline was developed to reduce variabilities in breast MRI, which paves the way for robust multi-scanner radiomic studies but needs to be assessed on patient data.