Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions

Author:

Tagliafico Alberto Stefano12ORCID,Calabrese Massimo1,Brunetti Nicole13ORCID,Garlaschi Alessandro1,Tosto Simona1,Rescinito Giuseppe1,Zoppoli Gabriele14ORCID,Piana Michele56,Campi Cristina56ORCID

Affiliation:

1. Dipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, Italy

2. Dipartimento di Scienze della Salute (DISSAL), Università di Genova, Via L.B. Alberti 2, 16132 Genova, Italy

3. Dipartimento di Medicina Sperimentale (DIMES), Università di Genova, Via L.B. Alberti 2, 16132 Genova, Italy

4. Dipartimento di Medicina Interna (DIMI), Università di Genova, v.le Benedetto XV 6, 16132 Genova, Italy

5. Dipartimento di Matematica (DIMA), Università di Genova, Via Dodecaneso 35, 16146 Genova, Italy

6. Life Science Computational Laboratory (LISCOMP), IRCCS Ospedale Policlinico San Martino, 16132 Genova, Italy

Abstract

Radiomics and artificial intelligence have been increasingly applied in breast MRI. However, the advantages of using radiomics to evaluate lesions amenable to MR-guided vacuum-assisted breast biopsy (MR-VABB) are unclear. This study includes patients scheduled for MR-VABB, corresponding to subjects with MRI-only visible lesions, i.e., with a negative second-look ultrasound. The first acquisition of the multiphase dynamic contrast-enhanced MRI (DCE-MRI) sequence was selected for image segmentation and radiomics analysis. A total of 80 patients with a mean age of 55.8 years ± 11.8 (SD) were included. The dataset was then split into a training set (50 patients) and a validation set (30 patients). Twenty out of the 30 patients with a positive histology for cancer were in the training set, while the remaining 10 patients with a positive histology were included in the test set. Logistic regression on the training set provided seven features with significant p values (<0.05): (1) ‘AverageIntensity’, (2) ‘Autocorrelation’, (3) ‘Contrast’, (4) ‘Compactness’, (5) ‘StandardDeviation’, (6) ‘MeanAbsoluteDeviation’ and (7) ‘InterquartileRange’. AUC values of 0.86 (95% C.I. 0.73–0.94) for the training set and 0.73 (95% C.I. 0.54–0.87) for the test set were obtained for the radiomics model. Radiological evaluation of the same lesions scheduled for MR-VABB had AUC values of 0.42 (95% C.I. 0.28–0.57) for the training set and 0.4 (0.23–0.59) for the test set. In this study, a radiomics logistic regression model applied to DCE-MRI images increased the diagnostic accuracy of standard radiological evaluation of MRI suspicious findings in women scheduled for MR-VABB. Confirming this performance in large multicentric trials would imply that using radiomics in the assessment of patients scheduled for MR-VABB has the potential to reduce the number of biopsies, in suspicious breast lesions where MR-VABB is required, with clear advantages for patients and healthcare resources.

Funder

Italian Ministry of Health

Publisher

MDPI AG

Subject

Clinical Biochemistry

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