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

• Published in: PloS one Vol. 16; no. 3; p. e0247330
• Main Authors: Lim, Hyun Kyung, Ha, Hong Il, Park, Sun-Young, Han, Junhee
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 04.03.2021; Public Library of Science (PLoS)
• Subjects: Age; Algorithms; Biology and Life Sciences; Biomedical materials; Computer and Information Sciences; Diagnosis; Disease; Drafting software; Editing; Electronic mail; Engineering and Technology; Feature selection; Femur; Fractures; Funding; Health aspects; Hospitals; Learning algorithms; Machine learning; Medical imaging; Medicine and Health Sciences; Metastasis; Methodology; Older people; Osteoporosis; Patients; Performance evaluation; Performance prediction; Physiological aspects; Public health; Radiology; Radiomics; Research and Analysis Methods; Research facilities; Statistical analysis; South Korea; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0247330

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. 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 radiomics features were calculated using 3D volume of interest analysis. Top 10 importance radiomic features were selected by the intraclass correlation coefficient and random forest feature selection. Study cohort was randomly divided into 70% of the samples as the training cohort and the remaining 30% of the sample as the validation cohort. Prediction performance of machine-learning analysis was calculated using diagnostic test and comparison of area under the curve (AUC) of receiver operating characteristic curve analysis was performed between training and validation cohorts. The osteoporosis prevalence of this study cohort was 20.8%. The prediction performance of the machine-learning analysis to diagnose osteoporosis in the training and validation cohorts were as follows; accuracy, 92.9% vs. 92.7%; sensitivity, 86.6% vs. 80.0%; specificity, 94.5% vs. 95.8%; positive predictive value, 78.4% vs. 82.8%; and negative predictive value, 96.7% vs. 95.0%. The AUC to predict osteoporosis in the training and validation cohorts were 95.9% [95% confidence interval (CI), 93.7%-98.1%] and 96.0% [95% CI, 93.2%-98.8%], respectively, without significant differences (P = 0.962). Prediction performance of femoral osteoporosis using machine-learning analysis with radiomics features and APCT showed high validity with more than 93% accuracy, specificity, and negative predictive value.