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Comput Med Imaging Graph. 2019 Jan;71:1-8. doi: 10.1016/j.compmedimag.2018.10.006. Epub 2018 Nov 3.

Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.

Author information

1
Biomedical Informatics Program, Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, United States. Electronic address: yohan.sumathipala@nih.gov.
2
Division of Pulmonary and Critical Care Medicine, Stanford University School of Medicine, Stanford, CA, United States. Electronic address: shafiq@jhmi.edu.
3
Program in Immunology, Stanford University School of Medicine, Stanford, CA, United States. Electronic address: ebongen@stanford.edu.
4
Department of Computer Science, Stanford University School of Engineering, Stanford, CA, United States. Electronic address: connorb3@stanford.edu.
5
Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States. Electronic address: david.paik@stanford.edu.

Abstract

Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule management a growing public health problem. Biopsy and pathologic analysis of suspicious nodules is necessary to ensure accurate diagnosis and appropriate intervention. Biopsy techniques vary as do the specialists that perform them and the ways lung nodule patients are referred and triaged. The largest dichotomy is between minimally invasive biopsy (MIB) and surgical biopsy (SB). Cases of unsuccessful MIB preceding a SB can result in considerable delay in definitive care with potentially an adverse impact on prognosis besides potentially avoidable healthcare expenditures. An automated method that predicts the optimal biopsy method for a given lung nodule could save time and healthcare costs by facilitating referral and triage patterns. To our knowledge, no such method has been published. Here, we used CT image features and radiologist-annotated semantic features to predict successful MIB in a way that has not been described before. Using data from the Lung Image Database Consortium image collection (LIDC-IDRI), we trained a logistic regression model to determine whether a MIB or SB procedure was used to diagnose lung cancer in a patient presenting with lung nodules. We found that in successful MIB cases, the nodules were significantly larger and more spiculated. Our model illustrates that using robust machine learning tools on easily accessible semantic and image data can predict whether a patient's nodule is best biopsied by MIB or SB. Pending further validation and optimization, clinicians could use our publicly accessible model to aid clinical decision-making.

KEYWORDS:

CT; LIDC-IDRI; Logistic regression; Lung biopsy; Lung cancer; Machine learning; NLST; Predicting biopsy method; Random forest; Semantic features

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