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Investigation of Low-Dose CT Lung Cancer Screening Scan "Over-Range" Issue Using Machine Learning Methods.

Donglai Huo1, Mark Kiehn2, Ann Scherzinger3

  • 1Department of Radiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA. Donglai.Huo@ucdenver.edu.

Journal of Digital Imaging
|May 19, 2019
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Summary
This summary is machine-generated.

Machine learning accurately controls low-dose computed tomography (CT) lung cancer screening scan length. This AI tool helps eliminate unnecessary radiation dose by optimizing scan parameters for high-risk patients.

Keywords:
Artificial neural networkCT doseCT lung cancer screeningConvolutional neural networkMachine learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Low-dose computed tomography (CT) is crucial for lung cancer screening in high-risk individuals.
  • Scan length is a significant factor influencing radiation dose in CT examinations.
  • Optimizing scan parameters is essential for effective and safe lung cancer screening.

Purpose of the Study:

  • To investigate the impact of scan length on CT dose in lung cancer screening.
  • To develop and validate a machine learning model for accurate lung segmentation and boundary determination.
  • To assess the extent of scan length "over-range" in low-dose CT lung screening exams.

Main Methods:

  • A UNET-based neural network was developed for lung segmentation in CT scout images.
  • The model was trained on chest X-ray and CT scout images, achieving high Intersection over Union (IOU) and Dice coefficients.
  • A validated model was used to analyze 770 low-dose CT lung screening exams to determine scan length variations.

Main Results:

  • The machine learning model demonstrated high accuracy in lung segmentation and boundary determination (average 4.7% difference).
  • The average "desired" scan length was 252 mm, with a significant average "over-range" of 58.5 mm (24%).
  • Scan length "over-range" was found to be dependent on technologist and patient weight, but independent of other acquisition parameters.

Conclusions:

  • Machine learning offers an effective solution for quality control of scan length in low-dose CT lung screening.
  • Accurate control of scan length can lead to the elimination of unnecessary patient radiation dose.
  • This approach can enhance the safety and efficiency of lung cancer screening programs.