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

Ignacio Alvarez Illan; Amirhessam Tahmassebi; Javier Ramirez; Juan M. Gorriz; Simon Y. Foo; Katja Pinker-Domenig; Anke Meyer-Baese

doi:10.1117/12.2304588

14 May 2018 Machine learning for accurate differentiation of benign and malignant breast tumors presenting as non-mass enhancement

Ignacio Alvarez Illan, Amirhessam Tahmassebi, Javier Ramirez, Juan M. Gorriz, Simon Y. Foo, Katja Pinker-Domenig, Anke Meyer-Baese

Author Affiliations +

Proceedings Volume 10669, Computational Imaging III; 106690W (2018) https://doi.org/10.1117/12.2304588
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

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.

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Ignacio Alvarez Illan, Amirhessam Tahmassebi, Javier Ramirez, Juan M. Gorriz, Simon Y. Foo, Katja Pinker-Domenig, and Anke Meyer-Baese "Machine learning for accurate differentiation of benign and malignant breast tumors presenting as non-mass enhancement", Proc. SPIE 10669, Computational Imaging III, 106690W (14 May 2018); https://doi.org/10.1117/12.2304588

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