Determining the importance of parameters extracted from multi-parametric MRI in the early prediction of the response to neo-adjuvant chemotherapy in breast cancer

Amirhessam Tahmassebi; Katja Pinker-Domenig; Georg Wengert; Thomas Helbich; Zsuzsanna Bago-Horvath; Anke Meyer-Baese

doi:10.1117/12.2293858

12 March 2018 Determining the importance of parameters extracted from multi-parametric MRI in the early prediction of the response to neo-adjuvant chemotherapy in breast cancer

Amirhessam Tahmassebi, Katja Pinker-Domenig, Georg Wengert, Thomas Helbich, Zsuzsanna Bago-Horvath, Anke Meyer-Baese

Author Affiliations +

Proceedings Volume 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging; 1057818 (2018) https://doi.org/10.1117/12.2293858
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

Neo-adjuvant chemotherapy (NAC) is the treatment of choice in patients with locally advanced breast cancer to reduce tumor burden, and potentially enable breast conservation. Response to treatment is assessed by histopathology from surgical specimen, a pathological complete response (pCR), or a minimal residual disease are associated with an improved disease-free, and overall survival. Early identification of non-responders is crucial as these patients might require different, or more aggressive treatment. Multi-parametric magnetic resonance imaging (mpMRI) using different morphological and functional MRI parameters such as T2-weighted, dynamic contrast-enhanced (DCE) MRI, and diffusion weighted imaging (DWI) has emerged as the method of choice for the early response assessments to NAC. Although, mpMRI is superior to conventional mammography for predicting treatment response, and evaluating residual disease, yet there is still room for improvement. In the past decade, the field of medical imaging analysis has grown exponentially, with an increased numbers of pattern recognition tools, and an increase in data sizes. These advances have heralded the field of radiomics. Radiomics allows the high-throughput extraction of the quantitative features that result in the conversion of images into mineable data, and the subsequent analysis of the data for an improved decision support with response monitoring during NAC being no exception. In this paper, we determine the importance and ranking of the extracted parameters from mpMRI using T2-weighted, DCE, and DWI for prediction of pCR and patient outcomes with respect to metastases and disease-specific death.

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Amirhessam Tahmassebi, Katja Pinker-Domenig, Georg Wengert, Thomas Helbich, Zsuzsanna Bago-Horvath, and Anke Meyer-Baese "Determining the importance of parameters extracted from multi-parametric MRI in the early prediction of the response to neo-adjuvant chemotherapy in breast cancer", Proc. SPIE 10578, Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, 1057818 (12 March 2018); https://doi.org/10.1117/12.2293858

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