Using Machine Learning to Unravel the Value of Radiographic Features for the Classification of Bone Tumors.

ABSTRACT:

Objectives

To build and validate random forest (RF) models for the classification of bone tumors based on the conventional radiographic features of the lesion and patients' clinical characteristics, and identify the most essential features for the classification of bone tumors.

Materials and methods

In this retrospective study, 796 patients (benign bone tumors: 412 cases, malignant bone tumors: 215 cases, intermediate bone tumors: 169 cases) with pathologically confirmed bone tumors from Nanfang Hospital of Southern Medical University, Foshan Hospital of TCM, and University of Hong Kong-Shenzhen Hospital were enrolled. RF models were built to classify tumors as benign, malignant, or intermediate based on conventional radiographic features and potentially relevant clinical ch

SUBMITTER: Pan D 

PROVIDER: S-EPMC7984886 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature