ArticleDiagnostics (Basel, Switzerland)2022
Different Lung Parenchyma Quantification Using Dissimilar Segmentation Software: A Multi-Center Study for COVID-19 Patients.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 23 citations in OpenAlex.
- Automated CT-Based Quantification of Pulmonary Fibrosis Using Deep Learning-Based Lung Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- CT-derived quantitative imaging biomarkers for hypoxemia risk in repeated lung resection: a retrospective study.BMC medical imaging · 2026Article
- Lung Volume Change Assessment in Moderate and Severe COVID-19 Using CT Volumetry.Diagnostics (Basel, Switzerland) · 2025Article
- Quantitative lung CT indicators have good predictive ability for hypoxemia during one-lung ventilation in radical resection for lung cancer.Journal of anesthesia · 2025Article
- The density histograms-derived computerized integrated index (CII) predicts mortality in idiopathic pulmonary fibrosis.Scientific reports · 2024Article
- How anatomical impairments found on CT affect perfusion percentage assessed by SPECT/CT scan?Annals of nuclear medicine · 2024Article
- Quantification of pulmonary edema using automated lung segmentation on computed tomography in mechanically ventilated patients with acute respiratory distress syndrome.Intensive care medicine experimental · 2024Article
- Comparing Visual and Software-Based Quantitative Assessment Scores of Lungs' Parenchymal Involvement Quantification in COVID-19 Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial intelligence (AI)-assisted chest computer tomography (CT) insights: a study on intensive care unit (ICU) admittance trends in 78 coronavirus disease 2019 (COVID-19) patients.Journal of thoracic disease · 2024Article
- Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education.Diagnostics (Basel, Switzerland) · 2024Review
- Quantification of diffuse parenchymal lung disease in non-small cell lung cancer patients with definitive concurrent chemoradiation therapy for predicting radiation pneumonitis.Thoracic cancer · 2023Article
- Quantitative CT at Follow-Up of COVID-19 Pneumonia: Relationship with Pulmonary Function Tests.Diagnostics (Basel, Switzerland) · 2023Article
- From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 78 COVID-19 Cases.Research square · 2023Article
- Software-Based Assessment of Well-Aerated Lung at CT for Quantification of Predicted Pulmonary Function in Resected NSCLC.Life (Basel, Switzerland) · 2023Article
- Effects of Automatic Deep-Learning-Based Lung Analysis on Quantification of Interstitial Lung Disease: Correlation with Pulmonary Function Test Results and Prognosis.Diagnostics (Basel, Switzerland) · 2022Article
- Radiomics to predict the mortality of patients with rheumatoid arthritis-associated interstitial lung disease: A proof-of-concept study.Frontiers in medicine · 2022Article
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19 authors at 8 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundChest Computed Tomography (CT) imaging has played a central role in the diagnosis of interstitial pneumonia in patients affected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and can be used to obtain the extent of lung involvement in COVID-19 pneumonia patients either qualitatively, via visual inspection, or quantitatively, via AI-based software. This study aims to compare the qualitative/quantitative pathological lung extension data on COVID-19 patients. Secondly, the quantitative data obtained were compared to verify their concordance since they were derived from three different lung segmentation software.
methodsThis double-center study includes a total of 120 COVID-19 patients (60 from each center) with positive reverse-transcription polymerase chain reaction (RT-PCR) who underwent a chest CT scan from November 2020 to February 2021. CT scans were analyzed retrospectively and independently in each center. Specifically, CT images were examined manually by two different and experienced radiologists for each center, providing the qualitative extent score of lung involvement, whereas the quantitative analysis was performed by one trained radiographer for each center using three different software:
resultsThe agreement between radiologists for visual estimation of pneumonia at CT can be defined as good (ICC 0.79, 95% CI 0.73-0.84). The statistical tests show that 3DSlicer overestimates the measures assessed; however, ICC index returns a value of 0.92 (CI 0.90-0.94), indicating excellent reliability within the three software employed. ICC was also performed between each single software and the median of the visual score provided by the radiologists. This statistical analysis underlines that the best agreement is between 3D Slicer "LungCTAnalyzer" and the median of the visual score (0.75 with a CI 0.67-82 and with a median value of 22% of disease extension for the software and 25% for the visual values).
conclusionsThis study provides for the first time a direct comparison between the actual gold standard, which is represented by the qualitative information described by radiologists, and novel quantitative AI-based techniques, here represented by three different commonly used lung segmentation software, underlying the importance of these specific values that in the future could be implemented as consistent prognostic and clinical course parameters.
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