Determining tumor cellularity in digital slides using ResNet

Shazia Akbar; Mohammad Peikari; Sherine Salama; Sharon Nofech-Mozes; Anne L. Martel

doi:10.1117/12.2292813

6 March 2018 Determining tumor cellularity in digital slides using ResNet

Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, Anne L. Martel

Proceedings Volume 10581, Medical Imaging 2018: Digital Pathology; 105810U (2018) https://doi.org/10.1117/12.2292813
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

The residual cancer burden index is a powerful prognostic factor which is used to measure neoadjuvant therapy response in invasive breast cancers. Tumor cellularity is one component of the residual cancer burden index and is currently measured manually through eyeballing. As such it is subject to inter- and intra-variability and is currently restricted to discrete values. We propose a method for automatically determining tumor cellularity in digital slides using deep learning techniques. We train a series of ResNet architectures to output both discrete and continuous values and compare our outcomes with scores acquired manually by an expert pathologist. Our configurations were validated on a dataset of image patches extracted from digital slides, each containing various degrees of tumor cellularity. Results showed that, in the case of discrete values, our models were able to distinguish between regions-of-interest containing tumor and healthy cells with over 97% test accuracy rates. Overall, we achieved 76% accuracy over four predefined tumor cellularity classes (no tumor/tumor; low, medium and high tumor cellularity). When computing tumor cellularity scores on a continuous scale, ResNet showed good correlations with manually-identified scores, showing potential for computing reproducible scores consistent with expert opinion using deep learning techniques.

Citation Download Citation

Shazia Akbar, Mohammad Peikari, Sherine Salama, Sharon Nofech-Mozes, and Anne L. Martel "Determining tumor cellularity in digital slides using ResNet", Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810U (6 March 2018); https://doi.org/10.1117/12.2292813

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