Breast lesion classification with multiparametric breast MRI using radiomics and machine learning: A comparison with radiologists’ performance Journal Article
| Authors: | Naranjo, I. D.; Gibbs, P.; Reiner, J. S.; Lo Gullo, R.; Thakur, S. B.; Jochelson, M. S.; Thakur, N.; Baltzer, P. A. T.; Helbich, T. H.; Pinker, K. |
| Article Title: | Breast lesion classification with multiparametric breast MRI using radiomics and machine learning: A comparison with radiologists’ performance |
| Abstract: | This multicenter retrospective study compared the performance of radiomics analysis coupled with machine learning (ML) with that of radiologists for the classification of breast tumors. A total of 93 consecutive women (mean age: 49 ± 12 years) with 104 histopathologically verified enhancing lesions (mean size: 22.8 ± 15.1 mm), classified as suspicious on multiparametric breast MRIs were included. Two experienced breast radiologists assessed all of the lesions, assigning a Breast Imaging Reporting and Database System (BI-RADS) suspicion category, providing a diffusion-weighted imaging (DWI) score based on lesion signal intensity, and determining the apparent diffusion coefficient (ADC). Ten predictive models for breast lesion discrimination were generated using radiomic features extracted from the multiparametric MRI. The area under the receiver operating curve (AUC) and the accuracy were compared using McNemar’s test. Multiparametric radiomics with DWI score and BI-RADS (accuracy = 88.5%; AUC = 0.93) and multiparametric radiomics with ADC values and BI-RADS (accuracy= 88.5%; AUC = 0.96) models showed significant improvements in diagnostic accuracy compared to the multiparametric radiomics (DWI + DCE data) model (p = 0.01 and p = 0.02, respectively), but performed similarly compared to the multiparametric assessment by radiologists (accuracy = 85.6%; AUC = 0.03; p = 0.39). In conclusion, radiomics analysis coupled with the ML of multiparametric MRI could assist in breast lesion discrimination, especially for less experienced readers of breast MRIs. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. |
| Keywords: | adult; major clinical study; histopathology; magnetic resonance imaging; diagnostic accuracy; clinical practice; breast cancer; tumor differentiation; breast neoplasms; retrospective study; radiologist; cancer classification; diffusion weighted imaging; diffusion magnetic resonance imaging; predictive value; receiver operating characteristic; decision support system; work experience; diagnostic test accuracy study; machine learning; breast imaging reporting and data system; multiparametric magnetic resonance imaging; human; female; article; radiomics |
| Journal Title: | Cancers |
| Volume: | 14 |
| Issue: | 7 |
| ISSN: | 2072-6694 |
| Publisher: | MDPI |
| Date Published: | 2022-04-01 |
| Start Page: | 1743 |
| Language: | English |
| DOI: | 10.3390/cancers14071743 |
| PROVIDER: | scopus |
| PMCID: | PMC8997089 |
| PUBMED: | 35406514 |
| DOI/URL: | |
| Notes: | Article -- Export Date: 2 May 2022 -- Source: Scopus |
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