Prediction of femoral osteoporosis using machine-learning analysis with radiomics features and abdomen-pelvic CT: A retrospective single center preliminary study.

ABSTRACT:

Background

Osteoporosis has increased and developed into a serious public health concern worldwide. Despite the high prevalence, osteoporosis is silent before major fragility fracture and the osteoporosis screening rate is low. Abdomen-pelvic CT (APCT) is one of the most widely conducted medical tests. Artificial intelligence and radiomics analysis have recently been spotlighted. This is the first study to evaluate the prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT.

Materials and methods

500 patients (M: F = 70:430; mean age, 66.5 ± 11.8yrs; range, 50-96 years) underwent both dual-energy X-ray absorptiometry and APCT within 1 month. The volume of interest of the left proximal femur was extracted and 41 radiomic

SUBMITTER: Lim HK 

PROVIDER: S-EPMC7932154 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature