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Automatic segmentation of the solid core and enclosed vessels in subsolid pulmonary nodules.

Jean-Paul Charbonnier1, Kaman Chung2, Ernst T Scholten2

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Summary

This study introduces an automated method to identify vessels and solid cores in subsolid pulmonary nodules on CT scans. The approach shows promise for improving accuracy in lung cancer screening and clinical practice.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Subsolid pulmonary nodules are crucial in lung cancer screening due to their higher malignancy potential.
  • Accurate measurement of nodule size and solid core is vital for patient management.
  • Vessels within nodules on CT scans can mimic solid components, complicating accurate assessment.

Purpose of the Study:

  • To develop and validate an automated method for identifying vessels and solid cores in subsolid pulmonary nodules.
  • To improve the reliability of quantitative assessments in lung cancer screening.
  • To aid in clinical decision-making for patients with subsolid pulmonary nodules.

Main Methods:

  • A voxel classification-based method was developed to automatically detect vessels and solid cores in subsolid nodules.
  • The method was validated by three expert radiologists on 170 screen-detected subsolid nodules.
  • Inter-observer agreement was assessed for comparison with the automated method's performance.

Main Results:

  • The automated method achieved substantial agreement with experts for vessel detection and moderate agreement for solid core detection.
  • Performance was comparable to inter-observer agreement, highlighting the inherent difficulty of the task.
  • A high percentage of segmentations (92.4% for vessels, 80.6% for solid cores) were deemed usable in clinical practice by experts.

Conclusions:

  • The proposed automated method offers a reliable tool for identifying vessels and solid cores in subsolid pulmonary nodules.
  • This technology can assist radiologists in overcoming challenges posed by intraparenchymal vessels.
  • The method has the potential to enhance the accuracy and consistency of lung nodule characterization in clinical settings.