Multi-level analysis of spatio-temporal features in non-mass enhancing breast tumors

Amirhessam Tahmassebi; Dat Ngo; Antonio Garcia; Encarnacin Castillo; Diego P. Morales; Katja Pinker-Domenig; Mark Lobbes; Anke Meyer-Bäse

doi:10.1117/12.2304928

14 May 2018 Multi-level analysis of spatio-temporal features in non-mass enhancing breast tumors

Amirhessam Tahmassebi, Dat Ngo, Antonio Garcia, Encarnacin Castillo, Diego P. Morales, Katja Pinker-Domenig, Mark Lobbes, Anke Meyer-Bäse

Author Affiliations +

Proceedings Volume 10662, Smart Biomedical and Physiological Sensor Technology XV; 106620H (2018) https://doi.org/10.1117/12.2304928
Event: SPIE Commercial + Scientific Sensing and Imaging, 2018, Orlando, FL, United States

Abstract

Diagnostically challenging breast tumors and Non-Mass-Enhancing (NME) lesions are often characterized by spatial and temporal heterogeneity, thus difficult to detect and classify. Differently from mass enhancing tumors they have an atypical temporal enhancement behavior that does not enable a straight-forward lesion classification into benign or malignant. The poorly defined margins do not support a concise shape description thus impacting morphological characterizations. A multi-level analysis strategy capturing the features of Non-Mass- Like-Enhancing (NMLEs) is shown to be superior to other methods relying only on morphological and kinetic information. In addition to this, the NMLE features such as NMLE distribution types and NMLE enhancement pattern, can be employed in radomics analysis to make robust models in the early prediction of the response to neo-adjuvant chemotherapy in breast cancer. Therefore, this could predict treatment response early in therapy to identify women who do not benefit from cytotoxic therapy.

Conference Presentation

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Amirhessam Tahmassebi, Dat Ngo, Antonio Garcia, Encarnacin Castillo, Diego P. Morales, Katja Pinker-Domenig, Mark Lobbes, and Anke Meyer-Bäse "Multi-level analysis of spatio-temporal features in non-mass enhancing breast tumors", Proc. SPIE 10662, Smart Biomedical and Physiological Sensor Technology XV, 106620H (14 May 2018); https://doi.org/10.1117/12.2304928

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