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A Novel Method Using Dual-Energy Computed Tomography for Detecting Amiodarone-Induced Lung Injury: A Cohort Pilot

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Dual-energy computed tomography (DECT) aids in diagnosing amiodarone-induced lung disease (AILD) by detecting iodine deposition. This imaging technique shows substantial correlation with clinical assessments, improving diagnosis in challenging cases.

Keywords:
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Area of Science:

  • Radiology
  • Pulmonology
  • Medical Imaging

Background:

  • Amiodarone-induced lung disease (AILD) is a severe complication of amiodarone therapy.
  • Diagnosis of AILD is challenging due to non-specific clinical and imaging findings.
  • Iodine deposition in lung tissue is a hallmark of AILD.

Purpose of the Study:

  • To assess the diagnostic value of dual-energy computed tomography (DECT) in identifying iodine deposition in AILD.
  • To evaluate the correlation between DECT findings and clinical diagnosis of AILD.
  • To determine if DECT can improve the diagnosis of difficult AILD cases.

Main Methods:

  • Retrospective analysis of 81 patients with suspected AILD who underwent DECT.
  • Radiological assessment of AILD probability and iodine deposition by two radiologists.
  • Clinical assessment of AILD probability by two pulmonologists, blinded to DECT findings.
  • Statistical analysis using Cohen's kappa test to assess inter-rater reliability.

Main Results:

  • DECT identified iodine deposition in lung abnormalities and liver in patients with suspected AILD.
  • Ground-glass opacities (GGO) were the most common abnormality and showed significant iodine deposition.
  • A substantial correlation (Cohen's k=0.61) was observed between DECT-based radiological assessment and clinical assessment of AILD.
  • Moderate correlation was noted for iodine deposition in GGO and lung atelectasis.

Conclusions:

  • DECT is valuable for detecting iodine deposition associated with AILD in both lung and liver.
  • Radiological evaluation using DECT demonstrates a substantial correlation with clinical AILD diagnosis.
  • DECT can assist in the diagnosis of challenging AILD cases.