Prediction of pathologic femoral fractures in patients with lung cancer using machine learning algorithms: Comparison of computed tomography-based radiological features with clinical features versus without clinical features. (27th June 2017)

• Record Type: Journal Article
• Authors: Oh, Eunsun; Seo, Sung Wook; Yoon, Young Cheol; Kim, Dong Wook; Kwon, Sunyoung; Yoon, Sungroh
• Is Part Of: Journal of orthopaedic surgery. Volume 25:Number 2(2017)
• Publication Date: 2017-06-27
• Subjects: femoral metastasis -- machine learning algorithm -- pathologic fractures -- predictive analytics; Orthopedic surgery -- Periodicals; Orthopedics; Orthopedic surgery; Periodicals; 617.3
• DOI: 10.1177/2309499017716243
• Languages: English
• ISSNs: 1022-5536

Abstract

Purpose: The purpose of this article is to compare the predictive power of two models trained with computed tomography (CT)-based radiological features and both CT-based radiological and clinical features for pathologic femoral fractures in patients with lung cancer using machine learning algorithms. Methods: Between January 2010 and December 2014, 315 lung cancer patients with metastasis to the femur were included. Among them, 84 patients who underwent CT scan and were followed up for more than 3 months were enrolled. We examined clinical and radiological risk factors affecting pathologic fracture through logistic regression. Predictive analysis was performed using five different supervised learning algorithms. The power of predictive model trained with CT-based radiological features was compared to those trained with both CT-based radiological and clinical features. Results: In multivariate logistic regression, female sex (odds ratio = 0.25, p = 0.0126), osteolysis (odds ratio = 7.62, p = 0.0239), and absence of radiation therapy (odds ratio = 10.25, p = 0.0258) significantly increased the risk of pathologic fracture in proximal femur. The predictive model trained with both CT-based radiological and clinical features showed the highest area under the receiver operating characteristic curve (0.80 ± 0.14, p < 0.0001) through gradient boosting algorithm. Conclusion: We believe that machine learning algorithms may be useful in the prediction of pathologic femoral fracture, which are multifactorial problem.