Deep learning-based classification of primary bone tumors on radiographs: A preliminary study.

ABSTRACT:

Background

To develop a deep learning model to classify primary bone tumors from preoperative radiographs and compare performance with radiologists.

Methods

A total of 1356 patients (2899 images) with histologically confirmed primary bone tumors and pre-operative radiographs were identified from five institutions' pathology databases. Manual cropping was performed by radiologists to label the lesions. Binary discriminatory capacity (benign versus not-benign and malignant versus not-malignant) and three-way classification (benign versus intermediate versus malignant) performance of our model were evaluated. The generalizability of our model was investigated on data from external test set. Final model performance was compared with interpretation from five radiologists of varyi

SUBMITTER: He Y 

PROVIDER: S-EPMC7689511 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature