Online ISSN: 2515-8260

Author : Basha, Hadeer Mohammed Nagy Ahmed, Amal Mohammed Hassan Ebrahim, Inas Mohammed Abdelaziz Elfiki, Mohammad Abd Alkhalek


Diagnostic Performance of Lung-Reporting and Data System with Computed Tomography Imaging in Categorizing Pulmonary Nodules

Hadeer Mohammed Nagy Ahmed, Amal Mohammed Hassan Ebrahim, Inas Mohammed Abdelaziz Elfiki, Mohammad Abd Alkhalek Basha

European Journal of Molecular & Clinical Medicine, 2021, Volume 8, Issue 3, Pages 4250-4258

Background:Many radiologists recognize that there are common lung nodules, that majority of
them are benign, and that not all benign lung nodules need the same management .The Lung
Imaging Reporting and Data System (Lung-RADS( was introduced to create a framework for the
analysis of screen-detected nodules and to enable nodule management further standardized. Aim
of work:To assess the performance of Lung-RADS in categorization of pulmonary nodules using
baseline screening CT scans. Subjects and methods:A prospective comparative study was
conducted in radiodiagnosis department, Zagazig university hospitals on 30 patients referred
from the chest department of Zagazig university hospitals as well as the outpatient clinics for CT
lung screening during the period from August 2018 to May 2019.All patients were subjected to
complete history taking, full clinical examination, MDCT imaging, PET/CT imaging in some
nodules, pathological examination, clinical and imaging follow up according to the criteria of the
nodules after 6 months by CT. Results:Considering only those cases classified as Lung-RADS4X
for predicting malignancy, the Lung-RADS had an accuracy, sensitivity, specificity, PPV, and
NPV of 76.7%, 70.6%,84.6%, 85.7%, and 68.6%, respectively. Considering Lung-RADS4A,
Lung-RADS4B and Lung-RADS4X together as predictors for malignancy, the accuracy,
sensitivity, specificity, PPV, and NPV were 90%, 94.1%, 84.6%, 88.9%, and 91.7%,
respectively.Conclusion:The LUNG–RAD classification method is a useful conceptualising
system that aids in the classification, follow-up, and improvement of the prognosis of malignant
pulmonary nodules.