Image-based Classification of Tumor Type and Growth Rate using Machine Learning: a preclinical study.

ABSTRACT: Medical images such as magnetic resonance (MR) imaging provide valuable information for cancer detection, diagnosis, and prognosis. In addition to the anatomical information these images provide, machine learning can identify texture features from these images to further personalize treatment. This study aims to evaluate the use of texture features derived from T₁-weighted post contrast scans to classify different types of brain tumors and predict tumor growth rate in a preclinical mouse model. To optimize prediction models this study uses varying gray-level co-occurrence matrix (GLCM) sizes, tumor region selection and different machine learning models. Using a random forest classification model with a GLCM of size 512 resulted in 92%, 91%, and 92% specificity, and 89%, 85%, and

SUBMITTER: Tang TT 

PROVIDER: S-EPMC6715812 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature