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,< Rashika Mishra>,,< Kevin Cederberg>,,<¿Ü> | ¾ÆÁø

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Pathological estimation of tumor necrosis after chemotherapy is essential for
patients with osteosarcoma. This study reports the first fully automated tool to
assess viable and necrotic tumor in osteosarcoma, employing advances in
histopathology digitization and automated learning. We selected 40 digitized whole
slide images representing the heterogeneity of osteosarcoma and chemotherapy
response. With the goal of labeling the diverse regions of the digitized tissue into
viable tumor, necrotic tumor, and non-tumor, we trained 13 machinelearning
models and selected the top performing one (a Support Vector Machine) based on
reported accuracy. We also developed a deep-learning architecture and trained it
on the same data set. We computed the receiver-operator characteristic for
discrimination of nontumor from tumor followed by conditional discrimination of
necrotic from viable tumor and found our models performing exceptionally well. We
then used the trained models to identify regions of interest on image-tiles
generated from test whole slide images. The classification output is visualized as a
tumor-prediction map, displaying the extent of viable and necrotic tumor in the
slide image. Thus, we lay the foundation for a complete tumor assessment pipeline
from original histology images to tumor-prediction map generation. The proposed
pipeline can also be adopted for other types of tumor.

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Á¦ 2Æí : ¿¬±¸³í¹®
Viable and necrotic tumor assessment from whole slide images of
osteosarcoma using machine-learning and deep-learning models

1. Introduction 51
2. Materials and methods 53
3. Machine-learning 56
4. Deep-learning 58
5. Results 59
6. Analyzing feature importance 60
7. Discussion 65
8. References 66