Deep learning to predict the proportion of positive cells in CMYC-stained tissue microarrays of diffuse large B-cell lymphoma

Abstract

CMYC positivity is an important prognostic factor for diffuse large B-cell lymphoma. However, manual quantification of CMYC can be subjective and may show intra- and inter-observer variability. Therefore, we sought to develop an automated method to quantify CMYC. Our method applies attention-based multiple instance learning to regress the proportion of CMYC positive tumor cells from pathologist-scored tissue microarray cores. The results of our experiments indicate a high Pearson correlation of 0.8421+/-0.1268. Additionally, we show that regardless of cross-validation methodology, this correlation remains relatively stable. When utilizing a standard clinical threshold of 40% for positivity, our method results in a sensitivity and specificity of 0.7600 and 0.9595. Finally, using clinical outcomes, we found that regressions provided more significant and robust stratification when compared to pathologist scoring. We conclude that proportion of positive stain can be regressed using attention-based multiple instance learning.