Potential role of CT-textural features for differentiation between viral interstitial pneumonias, pneumocystis jirovecii pneumonia and diffuse alveolar hemorrhage in early stages of disease: a proof of principle

Abstract

Background Pulmonary involvement is common in several infectious and non-infectious diagnostic settings. Imaging findings consistently overlap and are therefore difficult to differentiate by chest-CT. The aim of this study was to evaluate the role of CT-textural features(CTTA) for discrimination between atypical viral (respiratory-syncitial-virus(RSV) and herpes-simplex-1-virus (HSV1)), fungal (pneumocystis-jirovecii-pneumonia(PJP)) interstitial pneumonias and alveolar hemorrhage. Methods By retrospective single-centre analysis we identified 46 consecutive patients (29 m) with RSV( n = 5), HSV1( n = 6), PJP( n = 21) and lung hemorrhage( n = 14) who underwent unenhanced chest CTs in early stages of the disease between 01/2016 and 02/2017. All cases were confirmed by microbiologic direct analysis of bronchial lavage. On chest-CT-scans, the presence of imaging features like ground-glass opacity(GGO), crazy-paving, air-space consolidation, reticulation, bronchial wall thickening and centrilobular nodules were described. A representative large area was chosen in both lungs and used for CTTA-parameters (included heterogeneity, intensity, average, deviation, skewness). Results Discriminatory CTTA-features were found between alveolar hemorrhage and PJP consisting of differences in mean heterogeneity( p < 0.015) and uniformity of skewness( p < 0.006). There was no difference between CT-textural features of diffuse alveolar hemorrhage and viral pneumonia or PJP and viral pneumonia. Visual HRCT-assessment yielded great overlap of imaging findings with predominance of GGO for PJP and airspace consolidation for pneumonia/alveolar hemorrhage. Significant correlations between HRCT-based imaging findings and CT-textural features were found for all three disease groups. Conclusion CT-textural features showed significant differences in mean heterogeneity and uniformity of skewness. HRCT-based imaging findings correlated with certain CT-textural features showing that the latter have the potential to characterize structural properties of lung parenchyma and related abnormalities. Electronic supplementary material The online version of this article (10.1186/s12880-019-0338-0) contains supplementary material, which is available to authorized users.

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

“…Other studies have explored the feasibility of CTTA to differentiate diffuse pulmonary alterations that appears very similar on visual assessment. Kloth et al proved CTTA able to differentiate active alveolitis and lung fibrosis in patients with systemic sclerosis [ 43 ], as well alveolar hemorrhage from Pneumocystis jirovecii pneumonia [ 44 ], overcoming diagnostic challenges present on mere visual assessment due to overlapping imaging findings between the two entities at early stages. Therefore, CTTA might play a significant role in early differentiation of various pulmonary conditions manifesting with GGO, requiring different treatments and optimizes patient management.…”

Be aware of misdiagnosis tied to COVID-19 focusing: a case report of abciximab-induced alveolar haemorrhage thought to be SARS-CoV-2 in a patient with ST-segment elevation myocardial infarction

“… 4–6 Recently, Kloth et al . 7 compared CT finds of viral pneumonias and DAH, reporting no statistically significant differences for ground-glass opacity, crazy-paving, centrilobular nodules, and parenchymal consolidations between the different subgroups. Kalra et al .…”

Analysis of Image Features and TCM Syndrome Types of Lobar Pneumonia in Children Based on Mean Square Deviation Lung CT Image Registration Algorithm

“…In clinical practice, CT images are usually applied to diagnose lobular pneumonia in children. However, the medical images may cause problems in the quality due to the influence of various objective factors [13].…”