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Machine learning for accurate differentiation of benign and malignant breast tumors presenting as non-mass enhancement
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Paper Abstract

Accurate methods for breast cancer diagnosis are of capital importance for selection and guidance of treatment and optimal patient outcomes. In dynamic contrast enhancing magnetic resonance imaging (DCE-MRI), the accurate differentiation of benign and malignant breast tumors that present as non-mass enhancing (NME) lesions is challenging, often resulting in unnecessary biopsies. Here we propose a new approach for the accurate diagnosis of such lesions with high resolution DCE-MRI by taking advantage of seven robust classification methods to discriminate between malignant and benign NME lesions using their dynamic curves at the voxel level, and test it in a manually delineated dataset. The tested approaches achieve a diagnostic accuracy up to 94% accuracy, sensitivity of 99 % and specificity of 90% respectively, with superiority of high temporal compared to high spatial resolution sequences.

Paper Details

Date Published: 14 May 2018
PDF: 8 pages
Proc. SPIE 10669, Computational Imaging III, 106690W (14 May 2018); doi: 10.1117/12.2304588
Show Author Affiliations
Ignacio Alvarez Illan, Florida State Univ. (United States)
Univ. de Granada (Spain)
Amirhessam Tahmassebi, Florida State Univ. (United States)
Javier Ramirez, Univ. de Granada (Spain)
Juan M. Gorriz, Univ. de Granada (Spain)
Simon Y. Foo, Florida State Univ. (United States)
Katja Pinker-Domenig, Memorial Sloan-Kettering Cancer Ctr. (United States)
Medical Univ. of Vienna (Austria)
Anke Meyer-Baese, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 10669:
Computational Imaging III
Abhijit Mahalanobis; Amit Ashok; Lei Tian; Jonathan C. Petruccelli, Editor(s)

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