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.

Journal: Journal of orthopaedic surgery (Hong Kong)

PMID: 28659051

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.

Authors

Eunsun Oh

1 Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Sung Wook Seo

3 Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Young Cheol Yoon

1 Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Dong Wook Kim

3 Department of Orthopedic Surgery, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Sunyoung Kwon

4 Department of Electrical and Computer Engineering and Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.
Sungroh Yoon

4 Department of Electrical and Computer Engineering and Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea.

Keywords

Adult Aged Algorithms Female Femoral Fractures Fractures, Spontaneous Humans Logistic Models Lung Neoplasms Machine Learning Male Middle Aged Odds Ratio Risk Factors ROC Curve Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (28659051)

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.

Abstract

Authors

Keywords

External Resources

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