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Related Concept Videos

Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection

Ieun Yoon1, Jung Hee Hong2, Joseph Nathanael Witanto3

  • 1Department of Radiology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.

Quantitative Imaging in Medicine and Surgery
|February 23, 2023
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Summary

Computed tomography (CT) cavity volume aids in assessing tuberculosis (TB) infectivity and guiding non-tuberculous mycobacterial pulmonary disease (NTM-PD) treatment. A 3D nnU-Net model accurately quantifies these cavities on CT scans.

Keywords:
Computed tomography (CT)cavitydeep learningnon-tuberculous mycobacterial pulmonary disease (NTM-PD)pulmonary tuberculosis

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Area of Science:

  • Medical Imaging
  • Pulmonary Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Investigating the clinical significance of computed tomography (CT) cavity volume in tuberculosis (TB) and non-tuberculous mycobacterial pulmonary disease (NTM-PD).
  • Evaluating the utility of CT cavity volume for predicting sputum smear positivity in TB and treatment necessity in NTM-PD.

Purpose of the Study:

  • To explore the clinical implications of CT-derived cavity volumes in TB and NTM-PD.
  • To develop and validate a 3D nnU-Net model for automated detection and quantification of pulmonary cavities on CT images.

Main Methods:

  • Retrospective analysis of 206 TB and 186 NTM-PD patients with thin-section chest CT scans.
  • Semi-automatic segmentation of reference cavities and development of a 3D nnU-Net model for automated quantification.
  • Evaluation of CT cavity volume accuracy using ROC curves and nnU-Net performance metrics (sensitivity, false-positive rate, Dice coefficient, ICC).

Main Results:

  • Mean CT cavity volumes were 11.3 cm³ in TB and 16.4 cm³ in NTM-PD, significantly larger in smear-positive TB and NTM-PD requiring treatment.
  • CT cavity volume showed AUCs of 0.701 for TB sputum positivity and 0.834 for NTM-PD treatment necessity.
  • The 3D nnU-Net model achieved high sensitivity (100% per-patient) and accuracy (ICC 0.991 per-patient) in detecting and quantifying cavities.

Conclusions:

  • CT cavity volume is clinically relevant, correlating with TB infectivity and NTM-PD treatment needs.
  • The 3D nnU-Net model effectively automates the detection and quantification of mycobacterial cavities on chest CT.
  • This AI-driven approach can assist clinicians in managing TB and NTM-PD patients.