23 March 2016 Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images
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Abstract
Early stage estrogen receptor positive (ER+) breast cancer (BCa) treatment is based on the presumed aggressiveness and likelihood of cancer recurrence. The primary conundrum in treatment and management of early stage ER+ BCa is identifying which of these cancers are candidates for adjuvant chemotherapy and which patients will respond to hormonal therapy alone. This decision could spare some patients the inherent toxicity associated with adjuvant chemotherapy. Oncotype DX (ODX) and other gene expression tests have allowed for distinguishing the more aggressive ER+ BCa requiring adjuvant chemotherapy from the less aggressive cancers benefiting from hormonal therapy alone. However these gene expression tests tend to be expensive, tissue destructive and require physical shipping of tissue blocks for the test to be done. Interestingly breast cancer grade in these tumors has been shown to be highly correlated with the ODX risk score. Unfortunately studies have shown that Bloom-Richardson (BR) grade determined by pathologists can be highly variable. One of the constituent categories in BR grading is the quantification of tubules. The goal of this study was to develop a deep learning neural network classifier to automatically identify tubule nuclei from whole slide images (WSI) of ER+ BCa, the hypothesis being that the ratio of tubule nuclei to overall number of nuclei would correlate with the corresponding ODX risk categories. The performance of the tubule nuclei deep learning strategy was evaluated with a set of 61 high power fields. Under a 5-fold cross-validation, the average precision and recall measures were 0:72 and 0:56 respectively. In addition, the correlation with the ODX risk score was assessed in a set of 7513 high power fields extracted from 174 WSI, each from a different patient (At most 50 high power fields per patient study were used). The ratio between the number of tubule and non-tubule nuclei was computed for each WSI. The results suggests that for BCa cases with both low ODX score and low BR grade, the mean tubule nuclei ratio was significantly higher than that obtained for the BCa cases with both high ODX score and high BR grade (p < 0:01). The low ODX and low BR grade cases also presented a significantly higher average tubule nuclei ratio when compared with the rest of the BCa cases (p < 0:05). Finally, the BCa cases that presented both a high ODX and high BR grade show a mean tubule nuclei to total number of nuclei ratio which was significantly smaller than that obtained for the rest of BCa cases (p < 0:01).
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David Romo-Bucheli, Andrew Janowczyk, Eduardo Romero, Hannah Gilmore, Anant Madabhushi, "Automated tubule nuclei quantification and correlation with oncotype DX risk categories in ER+ breast cancer whole slide images", Proc. SPIE 9791, Medical Imaging 2016: Digital Pathology, 979106 (23 March 2016); doi: 10.1117/12.2211368; https://doi.org/10.1117/12.2211368
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