Interpolation-split: a data-centric deep learning approach with big interpolated data to boost airway segmentation performance.

The morphology and distribution of airway tree abnormalities enable diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. Furthermore, the segmentation of a complete airway tree is challenging as the intensity, scale/size and shape of airway segments and their walls change across generations. The existing classical techniques either provide an undersegmented or oversegmented airway tree, and manual intervention is required for optimal airway tree segmentation. The recent development of deep learning methods provides a fully automatic way of segmenting airway trees; however, these methods usually require high GPU memory usage and are difficult to implement in low computational resource environments. Therefore, in this study, we propose a data-centric deep learning technique with big interpolated data, Interpolation-Split, to boost the segmentation performance of the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway segments at different scales. In terms of average segmentation performance (dice similarity coefficient, DSC), our method (A) achieves 90.55%, 89.52%, and 85.80%; (B) outperforms the baseline models by 2.89%, 3.86%, and 3.87% on average; and (C) produces maximum segmentation performance gain by 14.11%, 9.28%, and 12.70% for individual cases when (1) nnU-Net with instant normalisation and leaky ReLU; (2) nnU-Net with batch normalisation and ReLU; and (3) modified dilated U-Net are used respectively. Our proposed method outperformed the state-of-the-art airway segmentation approaches. Furthermore, our proposed technique has low RAM and GPU memory usage, and it is GPU memory-efficient and highly flexible, enabling it to be deployed on any 2D deep learning model.

Calcific pulmonary nodule, is it wandering?

Wandering pulmonary nodule is defined as a nodule with morphologically identical features found in different regions of the lung on different imaging studies. In this article, we report a 61-year-old patient who was examined for cough and found to have an 8 mm calcific nodule in the lower lobe of the left lung on computed tomography (CT) scan (Fig. 1A, B). On follow-up CT scan two years later, a nodule with the same morphology and size was detected in the same lobe but at a different location (Fig. 1C, D).

The Effectiveness of Biological Agents on Chronic Rhinosinusitis with Nasal Polyposis in Patients with Comorbid Asthma: A Multicenter Real-Life Study from Türkiye.

Background and Objectives: Real-life data on the efficacy of biologic agents (BAs) on asthma-comorbid CRSwNP are needed. Our primary goal is to investigate the effects of BAs on CRSwNP symptoms, as well as endoscopic and tomography scores. Our secondary goal is to show a reduction in the frequency of acute sinusitis exacerbations and the need for surgery. Materials and Methods: We conducted a multicenter, retrospective, real-life study. We screened the patients with asthma-comorbid CRSwNP treated with omalizumab or mepolizumab. A total of 69 patients (40 F/29 M; omalizumab n = 55, mepolizumab n = 14) were enrolled. We compared the visual analog scale (VAS), sinonasal outcome test-22 (SNOT-22), nasal congestion score (NCS), Lund-Mackay computed tomography score (LMS), and total endoscopic polyp scores (TPS) before and after BAs. We evaluated the endoscopic sinus surgery (ESS) and acute exacerbations of chronic rhinosinusitis (AECRS) frequencies separately, according to the BAs. Results: The overall median (min-max) age was 43 (21-69) years. The median (min-max) of biologic therapy duration was 35 (4-113) months for omalizumab and 13.5 (6-32) for mepolizumab. Significant improvements were seen in VAS, SNOT-22, and NCS with omalizumab and mepolizumab. A significant decrease was observed in TPS with omalizumab [95% CI: 0-4] (p < 0.001), but not with mepolizumab [95% CI: -0.5-2] (p = 0.335). The frequency of ESS and AECRS were significantly reduced with omalizumab [95% CI: 2-3] (p < 0.001) and [95% CI: 2-5] (p < 0.001); and mepolizumab [95% CI: 0-2] (p = 0.002) and [95% CI: 2-8.5] (p < 0.001), respectively. There was no significant difference in LMS with either of the BAs. Conclusions: Omalizumab and mepolizumab can provide a significant improvement in the sinonasal symptom scores. BAs are promising agents for CRSwNP patients with frequent exacerbations and multiple surgeries.

Mortality surrogates in combined pulmonary fibrosis and emphysema.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) with coexistent emphysema, termed combined pulmonary fibrosis and emphysema (CPFE) may associate with reduced forced vital capacity (FVC) declines compared to non-CPFE IPF patients. We examined associations between mortality and functional measures of disease progression in two IPF cohorts. METHODS: Visual emphysema presence (>0% emphysema) scored on computed tomography identified CPFE patients (CPFE/non-CPFE: derivation cohort n=317/n=183, replication cohort n=358/n=152), who were subgrouped using 10% or 15% visual emphysema thresholds, and an unsupervised machine-learning model considering emphysema and interstitial lung disease extents. Baseline characteristics, 1-year relative FVC and diffusing capacity of the lung for carbon monoxide (D LCO) decline (linear mixed-effects models), and their associations with mortality (multivariable Cox regression models) were compared across non-CPFE and CPFE subgroups. RESULTS: In both IPF cohorts, CPFE patients with ≥10% emphysema had a greater smoking history and lower baseline D LCO compared to CPFE patients with <10% emphysema. Using multivariable Cox regression analyses in patients with ≥10% emphysema, 1-year D LCO decline showed stronger mortality associations than 1-year FVC decline. Results were maintained in patients suitable for therapeutic IPF trials and in subjects subgrouped by ≥15% emphysema and using unsupervised machine learning. Importantly, the unsupervised machine-learning approach identified CPFE patients in whom FVC decline did not associate strongly with mortality. In non-CPFE IPF patients, 1-year FVC declines ≥5% and ≥10% showed strong mortality associations. CONCLUSION: When assessing disease progression in IPF, D LCO decline should be considered in patients with ≥10% emphysema and a ≥5% 1-year relative FVC decline threshold considered in non-CPFE IPF patients.

Automated airway quantification associates with mortality in idiopathic pulmonary fibrosis.

OBJECTIVES: The study examined whether quantified airway metrics associate with mortality in idiopathic pulmonary fibrosis (IPF). METHODS: In an observational cohort study (n = 90) of IPF patients from Ege University Hospital, an airway analysis tool AirQuant calculated median airway intersegmental tapering and segmental tortuosity across the 2nd to 6th airway generations. Intersegmental tapering measures the difference in median diameter between adjacent airway segments. Tortuosity evaluates the ratio of measured segmental length against direct end-to-end segmental length. Univariable linear regression analyses examined relationships between AirQuant variables, clinical variables, and lung function tests. Univariable and multivariable Cox proportional hazards models estimated mortality risk with the latter adjusted for patient age, gender, smoking status, antifibrotic use, CT usual interstitial pneumonia (UIP) pattern, and either forced vital capacity (FVC) or diffusion capacity of carbon monoxide (DLco) if obtained within 3 months of the CT. RESULTS: No significant collinearity existed between AirQuant variables and clinical or functional variables. On univariable Cox analyses, male gender, smoking history, no antifibrotic use, reduced DLco, reduced intersegmental tapering, and increased segmental tortuosity associated with increased risk of death. On multivariable Cox analyses (adjusted using FVC), intersegmental tapering (hazard ratio (HR) = 0.75, 95% CI = 0.66-0.85, p < 0.001) and segmental tortuosity (HR = 1.74, 95% CI = 1.22-2.47, p = 0.002) independently associated with mortality. Results were maintained with adjustment using DLco. CONCLUSIONS: AirQuant generated measures of intersegmental tapering and segmental tortuosity independently associate with mortality in IPF patients. Abnormalities in proximal airway generations, which are not typically considered to be abnormal in IPF, have prognostic value. CLINICAL RELEVANCE STATEMENT: Quantitative measurements of intersegmental tapering and segmental tortuosity, in proximal (second to sixth) generation airway segments, independently associate with mortality in IPF. Automated airway analysis can estimate disease severity, which in IPF is not restricted to the distal airway tree. KEY POINTS: • AirQuant generates measures of intersegmental tapering and segmental tortuosity. • Automated airway quantification associates with mortality in IPF independent of established measures of disease severity. • Automated airway analysis could be used to refine patient selection for therapeutic trials in IPF.

Imaging features of radiation-induced lung disease and its relationship with clinical and dosimetric factors in breast cancer patients.

Aim: The aim is to extensively evaluate imaging features of radiation induced lung disease in breast cancer patients and to determine the relationship of imaging alterations with dosimetric parameters and patient related characteristics. Materials and Methods: A total of 76 breast cancer patients undergoing radiotherapy (RT) were studied retrospectively by case notes, treatment plans, dosimetric parameters, and chest computed tomography (CT) scans. Time intervals, that chest CT scans were acquired, were grouped as 1-6 months, 7-12 months, 13-18 months and more than 18 months after RT. Chest CTs (one or more for each patient) were assessed for the presence of ground glass opacity, septal thickening, consolidation/patchy pulmonary opacity/alveolar infiltrates, subpleural air cyst, air bronchogram, parenchymal bands, traction bronchiectasis, pleural/subpleural thickening and pulmonary volume loss. These alterations were scored by applying a system devised by Nishioka et al. Nishioka scores were analyzed for the relationship with clinical and dosimetric factors. Statistical Analysis Used: IBM SPSS Statistics for Windows, version 22.0 (IBM Corp., Armonk, N.Y., USA) was used to analyze data. Results: Median follow-up time was 49 months. Advanced age and aromatase inhibitor intake were correlated with higher Nishioka scores for 1-6 months' period. However, both were found nonsignificant in multivariate analysis. Nishioka scores of CT scans acquired more than 12 months after RT were positively correlated with mean lung dose, V5, V20, V30, and V40. Receiver operating characteristic analysis revealed that V5 for ipsilateral lung was the most robust dosimetric parameter predicting chronic lung injury. V5 >41% indicates the development of radiological lung changes. Conclusions: Keeping V5 ≤41% for ipsilateral lung could provide avoiding chronic lung sequelae.

Delineating associations of progressive pleuroparenchymal fibroelastosis in patients with pulmonary fibrosis.

Background: Computer quantification of baseline computed tomography (CT) radiological pleuroparenchymal fibroelastosis (PPFE) associates with mortality in idiopathic pulmonary fibrosis (IPF). We examined mortality associations of longitudinal change in computer-quantified PPFE-like lesions in IPF and fibrotic hypersensitivity pneumonitis (FHP). Methods: Two CT scans 6-36 months apart were retrospectively examined in one IPF (n=414) and one FHP population (n=98). Annualised change in computerised upper-zone pleural surface area comprising radiological PPFE-like lesions (Δ-PPFE) was calculated. Δ-PPFE >1.25% defined progressive PPFE above scan noise. Mixed-effects models evaluated Δ-PPFE against change in visual CT interstitial lung disease (ILD) extent and annualised forced vital capacity (FVC) decline. Multivariable models were adjusted for age, sex, smoking history, baseline emphysema presence, antifibrotic use and diffusion capacity of the lung for carbon monoxide. Mortality analyses further adjusted for baseline presence of clinically important PPFE-like lesions and ILD change. Results: Δ-PPFE associated weakly with ILD and FVC change. 22-26% of IPF and FHP cohorts demonstrated progressive PPFE-like lesions which independently associated with mortality in the IPF cohort (hazard ratio 1.25, 95% CI 1.16-1.34, p<0.0001) and the FHP cohort (hazard ratio 1.16, 95% CI 1.00-1.35, p=0.045). Interpretation: Progression of PPFE-like lesions independently associates with mortality in IPF and FHP but does not associate strongly with measures of fibrosis progression.

COVID-19 pneumonia: lessons learned, challenges, and preparing for the future.

Coronavirus disease 2019 (COVID-19) is a viral disease that causes life-threatening health problems during acute illness, causing a pandemic and millions of deaths. Although computed tomography (CT) was used as a diagnostic tool for COVID-19 in the early period of the pan demic due to the inaccessibility or long duration of the polymerase chain reaction tests, cur rent studies have revealed that CT scan should not be used to diagnose COVID-19. However, radiologic findings are vital in assessing pneumonia severity and investigating complications in patients with COVID-19. Long-term symptoms, also known as long COVID, in people recovering from COVID-19 affect patients' quality of life and cause global health problems. Herein, we aimed to summarize the lessons learned in COVID-19 pneumonia, the challenges in diagnosing the disease and complications, and the prospects for future studies.

Pixel-based analysis of pulmonary changes on CT lung images due to COVID-19 pneumonia.

Objectives: Computed tomography (CT) plays a complementary role in the diagnosis of the pneumonia-burden of COVID-19 disease. However, the low contrast of areas of inflammation on CT images, areas of infection are difficult to identify. The purpose of this study is to develop a post-image-processing method for quantitative analysis of COVID-19 pneumonia-related changes in CT attenuation values using a pixel-based analysis rather than more commonly used clustered focal pneumonia volumes. The COVID-19 pneumonia burden is determined by experienced radiologists in the clinic. Previous AI software was developed for the measurement of COVID-19 lesions based on the extraction of local pneumonia features. In this respect, changes in the pixel levels beyond the clusters may be overlooked by deep learning algorithms. The proposed technique focuses on the quantitative measurement of COVID-19 related pneumonia over the entire lung in pixel-by-pixel fashion rather than only clustered focal pneumonia volumes. Material and Methods: Fifty COVID-19 and 50 age-matched negative control patients were analyzed using the proposed technique and commercially available artificial intelligence (AI) software. The %pneumonia was calculated using the relative volume of parenchymal pixels within an empirically defined CT density range, excluding pulmonary airways, vessels, and fissures. One-way ANOVA analysis was used to investigate the statistical difference between lobar and whole lung %pneumonia in the negative control and COVID-19 cohorts. Results: The threshold of high-and-low CT attenuation values related to pneumonia caused by COVID-19 were found to be between ₋642.4 HU and 143 HU. The %pneumonia of the whole lung, left upper, and lower lobes were 8.1 ± 4.4%, 6.1 ± 4.5, and 11.3 ± 7.3% for the COVID-19 cohort, respectively, and statistically different (P < 0.01). Additionally, the pixel-based methods correlate well with existing AI methods and are approximately four times more sensitive to pneumonia particularly at the upper lobes compared with commercial software in COVID-19 patients (P < 0.01). Conclusion: Pixel-by-pixel analysis can accurately assess pneumonia in COVID-19 patients with CT. Pixel-based techniques produce more sensitive results than AI techniques. Using the proposed novel technique, %pneumonia could be quantitatively calculated not only in the clusters but also in the whole lung with an improved sensitivity by a factor of four compared to AI-based analysis. More significantly, pixel-by-pixel analysis was more sensitive to the upper lobe pneumonia, while AI-based analysis overlooked the upper lung pneumonia region. In the future, this technique can be used to investigate the efficiency of vaccines and drugs and post COVID-19 effects.