TY - GEN
T1 - Deep learning to predict the proportion of positive cells in CMYC-stained tissue microarrays of diffuse large B-cell lymphoma
AU - Tavolara, Thomas E.
AU - Niazi, M. Khalid Khan
AU - Jaye, David
AU - Flowers, Christopher
AU - Cooper, Lee
AU - Gurcan, Metin N.
N1 - Funding Information:
The project described was supported in part by U01 CA220401 (PIs: Gurcan, Cooper, Flowers), R01 CA235673 (PI: Puduvalli) from the National Cancer Institute, R01 HL145411 (PI: Beamer) from National Heart Lung and Blood Institute, UL1 TR001420 (PI: McClain) from National Center for Advancing Translational Sciences. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Cancer Institute, National Heart Lung and Blood Institute, National Center for Advancing Translational Sciences, or the National Institutes of Health.
Publisher Copyright:
© 2023 SPIE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - cmyc
KW - deep learning
KW - immunohistochemistry
KW - lymphoma
KW - multiple instance learning
UR - http://www.scopus.com/inward/record.url?scp=85160578802&partnerID=8YFLogxK
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U2 - 10.1117/12.2654489
DO - 10.1117/12.2654489
M3 - Conference contribution
AN - SCOPUS:85160578802
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2023
A2 - Tomaszewski, John E.
A2 - Ward, Aaron D.
PB - SPIE
T2 - Medical Imaging 2023: Digital and Computational Pathology
Y2 - 19 February 2023 through 23 February 2023
ER -