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

Autor: Erika Bongen, Yohan Sumathipala, Connor Brinton, Majid Shafiq, David T. Paik
Rok vydání: 2019
Předmět:
Lung Neoplasms
Biopsy
Pilot Projects
Health Informatics
Lung biopsy
Machine learning
computer.software_genre
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
Imaging
Three-Dimensional
0302 clinical medicine
Predictive Value of Tests
Humans
Medicine
Radiology
Nuclear Medicine and imaging
Lung cancer
Lung
Radiological and Ultrasound Technology
medicine.diagnostic_test
business.industry
Solitary Pulmonary Nodule
Nodule (medicine)
medicine.disease
Computer Graphics and Computer-Aided Design
Triage
medicine.anatomical_structure
Nodule biopsy
Radiographic Image Interpretation
Computer-Assisted
Computer Vision and Pattern Recognition
Artificial intelligence
medicine.symptom
Tomography
X-Ray Computed
business
computer
030217 neurology & neurosurgery
Automated method
Zdroj: Computerized Medical Imaging and Graphics. 71:1-8
ISSN: 0895-6111
DOI: 10.1016/j.compmedimag.2018.10.006
Popis: 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.
Databáze: OpenAIRE
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