Breast MRI radiomics for the pre-treatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients

Karen Drukker; Iman El-Bawab; Alexandra Edwards; Christopher Doyle; John Papaioannou; Kirti Kulkarni; Maryellen L. Giger

doi:10.1117/12.2513561

13 March 2019 Breast MRI radiomics for the pre-treatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients

Karen Drukker, Iman El-Bawab, Alexandra Edwards, Christopher Doyle, John Papaioannou, Kirti Kulkarni, Maryellen L. Giger

Author Affiliations +

Proceedings Volume 10950, Medical Imaging 2019: Computer-Aided Diagnosis; 109502N (2019) https://doi.org/10.1117/12.2513561
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

The purpose of this study was to evaluate breast MRI radiomics in predicting, prior to any treatment, the response to neoadjuvant chemotherapy (NAC) in patients with invasive lymph node-positive breast cancer for 2 tasks: 1) prediction of pathologic complete response and 2) prediction of post- NAC lymph node (LN) status. Our study included 158 patients with 19 showing post-NAC complete pathologic response (pathologic TNM stage T0,N0,MX) and 139 showing incomplete response, 42 patients were post-NAC LN-negative and 116 were post-NAC LN-positive. Only pre-NAC MRIs underwent computer analysis, initialized by an expert breast radiologist indicating index cancers and metastatic axillary sentinel lymph nodes on DCE-MRI images. Forty-nine radiomic features were extracted, both for the primary cancers and for the metastatic sentinel lymph nodes. Since the dataset contained MRIs acquired at 1.5T and at 3.0T, we eliminated features affected by magnet strength as demonstrated in the Mann-Whitney U-test by rejection of the null-hypothesis that samples were selected from populations having the same distribution. ROC analysis was used to assess performance of individual features in the 2 classification tasks. Eighteen features appeared unaffected by magnet strength, of which only a single pre-NAC tumor feature outperformed random guessing in predicting pathologic complete response. On the other hand, 13 and 10 pre-NAC lymph node features were able to predict pathologic complete response and post-NAC LN-status, respectively, with as most promising feature the standard deviation within the LN at the first postcontrast DCE-MRI time-point (areas under the ROC curve: 0.79 (standard error 0.06) and 0.70 (0.05), respectively).

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Karen Drukker, Iman El-Bawab, Alexandra Edwards, Christopher Doyle, John Papaioannou, Kirti Kulkarni, and Maryellen L. Giger "Breast MRI radiomics for the pre-treatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients", Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502N (13 March 2019); https://doi.org/10.1117/12.2513561

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