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Predicting Malignant Nodules from Screening CT Scans.

Samuel Hawkins1, Hua Wang2, Ying Liu2

  • 1Department of Computer Sciences and Engineering, University of South Florida, Tampa, Florida.

Journal of Thoracic Oncology : Official Publication of the International Association for the Study of Lung Cancer
|July 17, 2016
PubMed
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This summary is machine-generated.

Quantitative analysis of baseline lung cancer screening CT scans, known as radiomics, can predict future cancer development. This approach shows promise in assessing cancer risk from screening images.

Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging Analysis

Background:

  • Lung cancer screening using low-dose computed tomography (LDCT) aims to detect cancer early.
  • Predicting the future development of lung cancer from screening images remains a challenge.

Purpose of the Study:

  • To evaluate if radiomics analysis of baseline LDCT images can predict the subsequent emergence of lung cancer.
  • To assess the predictive performance of radiomics compared to existing methods.

Main Methods:

  • Utilized public data from the National Lung Screening Trial (ACRIN 6684).
  • Extracted image features from pulmonary nodules in screening subjects.
  • Employed a random forests classifier with 23 stable features to predict cancer development.
Keywords:
Computed tomographyLung cancerMachine learningPredictionRadiomicsScreening

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Main Results:

  • Radiomics models predicted cancerous nodules 1 and 2 years in advance with accuracies of 80% (AUC 0.83) and 79% (AUC 0.75), respectively.
  • Radiomics outperformed the Lung Imaging Reporting and Data System (Lung-RADS) and volume-only assessments.
  • The McWilliams risk assessment model showed comparable performance.

Conclusions:

  • Radiomics analysis of baseline LDCT scans can effectively assess the risk of future lung cancer development.
  • This quantitative imaging approach offers a valuable tool for lung cancer screening interpretation.