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Breast MRI radiomics for the pre-treatment prediction of response to neoadjuvant chemotherapy in node-positive breast cancer patients
Author(s): Karen Drukker; Iman El-Bawab; Alexandra Edwards; Christopher Doyle; John Papaioannou; Kirti Kulkarni; Maryellen L. Giger
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Paper 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).

Paper Details

Date Published: 13 March 2019
PDF: 10 pages
Proc. SPIE 10950, Medical Imaging 2019: Computer-Aided Diagnosis, 109502N (13 March 2019); doi: 10.1117/12.2513561
Show Author Affiliations
Karen Drukker, The Univ. of Chicago (United States)
Iman El-Bawab, The Univ. of Chicago (United States)
Alexandra Edwards, The Univ. of Chicago (United States)
Christopher Doyle, The Univ. of Chicago (United States)
John Papaioannou, The Univ. of Chicago (United States)
Kirti Kulkarni, The Univ. of Chicago (United States)
Maryellen L. Giger, The Univ. of Chicago (United States)


Published in SPIE Proceedings Vol. 10950:
Medical Imaging 2019: Computer-Aided Diagnosis
Kensaku Mori; Horst K. Hahn, Editor(s)

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