Machine learning-based CT radiomics features for the prediction of pulmonary metastasis in osteosarcoma.

ABSTRACT:

Objective

This study aims to build machine learning-based CT radiomic features to predict patients developing metastasis after osteosarcoma diagnosis.

Methods and materials

This retrospective study has included 81 patients with a histopathological diagnosis of osteosarcoma. The entire dataset was divided randomly into training (60%) and test sets (40%). A data augmentation technique for the minority class was performed in the training set, along with feature's selection and model's training. The radiomic features were extracted from CT's image of the local osteosarcoma. Three frequently used machine learning models tried to predict patients with lung metastases (MT) and those without lung metastases (non-MT). According to the higher area under the curve (AUC), the best class

SUBMITTER: Pereira HM 

PROVIDER: S-EPMC8764920 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature