Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

ABSTRACT: 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 machine-learning 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 discriminatio

SUBMITTER: Arunachalam HB 

PROVIDER: S-EPMC6469748 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature