Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/6/1007 |
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| author | Alberto Stefano Tagliafico Massimo Calabrese Nicole Brunetti Alessandro Garlaschi Simona Tosto Giuseppe Rescinito Gabriele Zoppoli Michele Piana Cristina Campi |
| author_facet | Alberto Stefano Tagliafico Massimo Calabrese Nicole Brunetti Alessandro Garlaschi Simona Tosto Giuseppe Rescinito Gabriele Zoppoli Michele Piana Cristina Campi |
| author_sort | Alberto Stefano Tagliafico |
| collection | DOAJ |
| description | 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 <i>p</i> 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. |
| first_indexed | 2024-03-11T06:41:44Z |
| format | Article |
| id | doaj.art-561b8f155c3d4a98b0426a6af7bc452a |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T06:41:44Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-561b8f155c3d4a98b0426a6af7bc452a2023-11-17T10:33:04ZengMDPI AGDiagnostics2075-44182023-03-01136100710.3390/diagnostics13061007Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant LesionsAlberto Stefano Tagliafico0Massimo Calabrese1Nicole Brunetti2Alessandro Garlaschi3Simona Tosto4Giuseppe Rescinito5Gabriele Zoppoli6Michele Piana7Cristina Campi8Dipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, ItalyDipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, ItalyDipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, ItalyDipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, ItalyDipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, ItalyDipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, ItalyDipartimento di Radiodiagnostica, IRCCS—Ospedale Policlinico San Martino, Largo Rosanna Benzi 10, 16132 Genvoa, ItalyDipartimento di Matematica (DIMA), Università di Genova, Via Dodecaneso 35, 16146 Genova, ItalyDipartimento di Matematica (DIMA), Università di Genova, Via Dodecaneso 35, 16146 Genova, ItalyRadiomics 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 <i>p</i> 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.https://www.mdpi.com/2075-4418/13/6/1007breast cancerMR-guided vacuum-assisted breast biopsyradiomicsmagnetic resonance imaging |
| spellingShingle | Alberto Stefano Tagliafico Massimo Calabrese Nicole Brunetti Alessandro Garlaschi Simona Tosto Giuseppe Rescinito Gabriele Zoppoli Michele Piana Cristina Campi Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions Diagnostics breast cancer MR-guided vacuum-assisted breast biopsy radiomics magnetic resonance imaging |
| title | Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions |
| title_full | Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions |
| title_fullStr | Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions |
| title_full_unstemmed | Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions |
| title_short | Freehand 1.5T MR-Guided Vacuum-Assisted Breast Biopsy (MR-VABB): Contribution of Radiomics to the Differentiation of Benign and Malignant Lesions |
| title_sort | freehand 1 5t mr guided vacuum assisted breast biopsy mr vabb contribution of radiomics to the differentiation of benign and malignant lesions |
| topic | breast cancer MR-guided vacuum-assisted breast biopsy radiomics magnetic resonance imaging |
| url | https://www.mdpi.com/2075-4418/13/6/1007 |
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