Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

Histogram-Based Densitometry Index to Assess the Severity of Interstitial Lung Disease in Systemic Sclerosis in Standard and Low-Dose Computed Tomography

OBJECTIVE

Mean lung attenuation, skewness, and kurtosis are histogram-based densitometry variables that quantify systemic sclerosis-associated interstitial lung disease (SSc-ILD) and were recently merged into a computerized integrated index (CII). Our work tested the CII in low-dose 9-slice (reduced) and standard high-resolution computed tomography (CT) scans to evaluate extensive SSc-ILD and predict mortality.

METHODS

CT scans from patients with SSc-ILD were assessed using the software Horos to compute standard and reduced CIIs. Extensive ILD was determined following the Goh staging system. The association between CIIs and extensive ILD was analyzed with a generalized estimating equation regression model, the predictive ability of CIIs by the area under the receiver-operation characteristic curve (AUC), and the association between CIIs and death by Kaplan-Meier analysis.

RESULTS

Among 243 patients with standard and reduced CT scans available, 157 CT scans from 119 patients with SSc-ILD constituted the derivation cohort. The validation cohort included 116 standard and 175 reduced CT scans. Both CIIs from standard (odds ratio [OR] 0.53, 95% CI 0.37-0.75; AUC 0.77, 95% CI 0.68-0.87) and reduced CT scans (OR 0.54, 95% CI 0.35-0.82; AUC 0.78, 95% CI 0.70-0.87) were significantly associated with extensive ILD. A threshold of CII ≤ -0.96 for standard CT scans and CII ≤ -1.85 for reduced CT scans detected extensive ILD with high sensitivity in both derivation and validation cohorts. Extensive ILD according to Goh staging (OR 2.94, 95% CI 1.10-7.82) and standard CII ≤ -0.96 (OR 1.78, 95% CI 1.24-2.56) significantly predicted mortality; a marginal P value was observed for reduced CII ≤ -1.85 (OR 1.27, 95% CI 0.93-1.75).

CONCLUSION

Thresholds for both standard and reduced CII to identify extensive ILD were developed and validated, with an additional association with mortality. CIIs might help in clinical practice when radiology expertise is missing.

A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy

Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory μCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications.

Ultra-low dose computed tomography protocols using spectral shaping for lung cancer screening: Comparison with low-dose for volumetric LungRADS classification

PURPOSE

To compare Low-Dose Computed Tomography (LDCT) with four different Ultra-Low-Dose Computed Tomography (ULDCT) protocols for PN classification according to the Lung Reporting and Data System (LungRADS).

METHODS

Three hundred sixty-one participants of an ongoing lung cancer screening (LCS) underwent single-breath-hold double chest Computed Tomography (CT), including LDCT (120kVp, 25mAs; CTDIvol 1,62 mGy) and one ULDCT among: fully automated exposure control ("ULDCT₁"); fixed tube-voltage and current according to patient size ("ULDCT₂"); hybrid approach with fixed tube-voltage ("ULDCT₃") and tube current automated exposure control ("ULDCT₄"). Two radiologists (R1, R2) assessed LungRADS 2022 categories on LDCT, and then after 2 weeks on ULDCT using two different kernels (R1: Qr49_{ADMIRE 4}; R2: Br49_{ADMIRE 3}). Intra-subject agreement for LungRADS categories between LDCT and ULDCT was measured by the k-Cohen Index with Fleiss-Cohen weights.

RESULTS

LDCT-dominant PNs were detected in ULDCT in 87 % of cases on Qr49_{ADMIRE 4} and 88 % on Br49_{ADMIRE 3}. The intra-subject agreement was: κULDCT₁ = 0.89 [95 %CI 0.82-0.96]; κULDCT₂ = 0.90 [0.81-0.98]; κULDCT₃ = 0.91 [0.84-0.99]; κULDCT₄ = 0.88 [0.78-0.97] on Qr49_{ADMIRE 4}, and κULDCT₁ = 0.88 [0.80-0.95]; κULDCT₂ = 0.91 [0.86-0.96]; κULDCT₃ = 0.87 [0.78-0.95]; and κULDCT₄ = 0.88 [0.82-0.94] on Br49_{ADMIRE 3}. LDCT classified as LungRADS 4B were correctly identified as LungRADS 4B at ULDCT₃, with the lowest radiation exposure among the tested protocols (median effective doses were 0.31, 0.36, 0.27 and 0.37 mSv for ULDCT₁, ULDCT₂, ULDCT₃, and ULDCT₄, respectively).

CONCLUSIONS

ULDCT by spectral shaping allows the detection and characterization of PNs with an excellent agreement with LDCT and can be proposed as a feasible approach in LCS.

The radiological appearances of lung cancer treated with immunotherapy

Therapy and prognosis of several solid and hematologic malignancies, including non-small cell lung cancer (NSCLC), have been favourably impacted by the introduction of immune checkpoint inhibitors (ICIs). Their mechanism of action relies on the principle that some cancers can evade immune surveillance by expressing surface inhibitor molecules, known as "immune checkpoints". ICIs aim to conceal tumoural checkpoints on the cell surface and reinvigorate the ability of the host immune system to recognize tumour cells, triggering an antitumoural immune response.In this review, we will focus on the imaging patterns of different responses occurring in patients treated by ICIs. We will also discuss imaging findings of immune-related adverse events (irAEs), along with current and future perspectives of metabolic imaging. Finally, we will explore the role of radiomics in the setting of ICI-treated patients.

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF. The linear relationship between X-ray attenuation and tissue density allows lung densitometry on total lung volume. However, the huge density changes caused by PF usually require manual segmentation by trained operators, limiting µCT deployment in preclinical routine. Deep learning approaches have achieved state-of-the-art performance in medical image segmentation. In this work, we propose a fully automated deep learning approach to segment right and left lung on µCT imaging and subsequently derive lung densitometry. Our pipeline first employs a convolutional network (CNN) for pre-processing at low-resolution and then a 2.5D CNN for higher-resolution segmentation, combining computational advantage of 2D and ability to address 3D spatial coherence without compromising accuracy. Finally, lungs are divided into compartments based on air content assessed by density. We validated this pipeline on 72 mice with different grades of PF, achieving a Dice score of 0.967 on test set. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of µCT scans of PF murine models, thus laying the ground for its wider exploitation in preclinical settings.

Artificial intelligence software in pulmonary nodule assessment

Background:

This study tests the impact of the addition of autonomous computed tomography (CT) interpreting software to radiologist assessment of pulmonary nodules.

Methods:

Computed tomography scans for nodule assessment were identified retrospectively. Lung cancer risk factors, initial radiologist (RAD) report, Philips Lung Nodule software report (computer-aided nodule (CAD)) and radiologist report following the review of CT images and CAD (RAD + CAD) were collected. Follow-up recommendations based on current guidelines were derived from each report.

Results:

In all, 100 patients were studied. Median maximal diameter of the largest nodule reported by RAD and RAD + CAD were similar at 10.0 and 9.0 mm, respectively ( p = 0.06) but were reported as larger by CAD at 11.8 mm ( p < 0.001). Follow-up recommendations derived from RAD + CAD were less intensive in 23 (23%) and more intensive in 34 (34%) than that of RAD.

Discussion:

This study suggests that autonomous software use can alter radiologist assessment of pulmonary nodules such that suggested follow-up is altered.

Quantitative Computed Tomography: What Clinical Questions Can it Answer in Chronic Lung Disease?

Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customization of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.

European lung cancer screening: valuable trial evidence for optimal practice implementation

Lung cancer screening (LCS) by low-dose computed tomography is a strategy for secondary prevention of lung cancer. In the last two decades, LCS trials showed several options to practice secondary prevention in association with primary prevention, however, the translation from trial to practice is everything but simple. In 2020, the European Society of Radiology and European Respiratory Society published their joint statement paper on LCS. This commentary aims to provide the readership with detailed description about hurdles and potential solutions that could be encountered in the practice of LCS.

Head-to-head Comparison of Qualitative Radiologist Assessment With Automated Quantitative Computed Tomography Analysis for Bronchiolitis Obliterans Syndrome After Hematopoietic Cell Transplantation

PURPOSE

Computed tomography (CT) findings of bronchiolitis obliterans syndrome (BOS) can be nonspecific and variable. This study aims to measure the incremental value of automated quantitative lung CT analysis to clinical CT interpretation. A head-to-head comparison of quantitative CT lung density analysis by parametric response mapping (PRM) with qualitative radiologist performance in BOS diagnosis was performed.

MATERIALS AND METHODS

Inspiratory and end-expiratory CTs of 65 patients referred to a post-bone marrow transplant lung graft-versus-host-disease clinic were reviewed by 3 thoracic radiologists for the presence of mosaic attenuation, centrilobular opacities, airways dilation, and bronchial wall thickening. Radiologists' majority consensus diagnosis of BOS was compared with automated PRM air trapping quantification and to the gold-standard diagnosis of BOS as per National Institutes of Health (NIH) consensus criteria.

RESULTS

Using a previously established threshold of 28% air trapping on PRM, the diagnostic performance for BOS was as follows: sensitivity 56% and specificity 94% (area under the receiver operator curve [AUC]=0.75). Radiologist review of inspiratory CT images alone resulted in a sensitivity of 80% and a specificity of 69% (AUC=0.74). When radiologists assessed both inspiratory and end-expiratory CT images in combination, the sensitivity was 92% and the specificity was 59% (AUC=0.75). The highest performance was observed when the quantitative PRM report was reviewed alongside inspiratory and end-expiratory CT images, with a sensitivity of 92% and a specificity of 73% (AUC=0.83).

CONCLUSIONS

In the CT diagnosis of BOS, qualitative expert radiologist interpretation was noninferior to quantitative PRM. The highest level of diagnostic performance was achieved by the combination of quantitative PRM measurements with qualitative image feature assessments.

Volumetric Measurements in Lung Cancer Screening Reduces Unnecessary Low-Dose Computed Tomography Scans: Results from a Single-Center Prospective Trial on 4119 Subjects

This study aims to compare the low-dose computed tomography (LDCT) outcome and volume-doubling time (VDT) derived from the measured volume (MV) and estimated volume (EV) of pulmonary nodules (PNs) detected in a single-center lung cancer screening trial. MV, EV and VDT were obtained for prevalent pulmonary nodules detected at the baseline round of the bioMILD trial. The LDCT outcome (based on bioMILD thresholds) and VDT categories were simulated on PN- and screenee-based analyses. A weighted Cohen’s kappa test was used to assess the agreement between diagnostic categories as per MV and EV, and 1583 screenees displayed 2715 pulmonary nodules. In the PN-based analysis, 40.1% PNs were included in different LDCT categories when measured by MV or EV. The agreements between MV and EV were moderate (κ = 0.49) and fair (κ = 0.37) for the LDCT outcome and VDT categories, respectively. In the screenee-based analysis, 46% pulmonary nodules were included in different LDCT categories when measured by MV or EV. The agreements between MV and EV were moderate (κ = 0.52) and fair (κ = 0.34) for the LDCT outcome and VDT categories, respectively. Within a simulated lung cancer screening based on a recommendation by estimated volumetry, the number of LDCTs performed for the evaluation of pulmonary nodules was higher compared with in prospective volumetric management.

Diagnosis and management of lung involvement in systemic lupus erythematosus and Sjögren's syndrome: a literature review

Lung involvement in systemic lupus erythematosus (SLE) and primary Sjögren's syndrome (pSS) has extensively been outlined with a multiplicity of different manifestations. In SLE, the most frequent finding is pleural effusion, while in pSS, airway disease and parenchymal disorders prevail. In both cases, there is an increased risk of pre-capillary and post-capillary pulmonary arterial hypertension (PAH) and pulmonary venous thromboembolism (VTE). The risk of VTE is in part due to an increased thrombophilic status secondary to systemic inflammation or to the well-established association with antiphospholipid antibody syndrome (APS). The lung can also be the site of an organ-specific complication due to the aberrant pathologic immune-hyperactivation as occurs in the development of lymphoma or amyloidosis in pSS. Respiratory infections are a major issue to be addressed when approaching the differential diagnosis, and their exclusion is required to safely start an immunosuppressive therapy. Treatment strategy is mainly based on glucocorticoids (GCs) and immunosuppressants, with a variable response according to the primary pathologic process. Anticoagulation is recommended in case of VTE and multi-targeted treatment regimens including different drugs are the mainstay for PAH management. Antibiotics and respiratory physiotherapy can be considered relevant complement therapeutic measures. In this article, we reviewed lung manifestations in SLE and pSS with the aim to provide a comprehensive overview of their diagnosis and management to physicians taking care of patients with connective tissue diseases.

Feasibility, face, and content validity of quantitative computed tomography in interstitial lung disease related to connective tissue diseases

OBJECTIVES

Quantitative computed tomography (QCT) is a promising tool for objective assessment of interstitial lung disease (ILD) related to connective tissue diseases (CTD). However, its validity was never investigated. The aim of this study was to assess QCT feasibility, face, and content validity evaluation concerning CTD-ILD.

METHODS

A rheumatologist and a chest radiologist conceived an online survey with nine statements: Two about general issue involving CTD-ILD, one for the face validity, three both for content validity and feasibility. Each statement had to be rated with a score from 0 to 100, respectively, (complete disagreement and agreement). We considered a statement supported by the experts if the median score was ≥75.0.

RESULTS

A panel of 14 experts (11 from Europe, three from America) with a nine years median experience was enrolled. All the statements about feasibility, face and content validity were supported, except for QCT capability to recognize elementary lesions.

CONCLUSIONS

The panel of experts supported feasibility, face, and content validity of QCT assessment concerning CTD-ILD. This may stimulate a greater use in clinical practice and further studies to confirm its discriminative properties and its construct validity.

Interstitial lung disease in systemic sclerosis quantification of disease classification and progression with high-resolution computed tomography: An observational study

INTRODUCTION

Systemic sclerosis-associated interstitial lung disease accounts for up to 20% of mortality in these patients and has a highly variable prognosis. Functional respiratory imaging, a quantitative computed tomography imaging technique which allows mapping of regional information, can provide a detailed view of lung structures. It thereby shows potential to better characterize this disease.

PURPOSE

To evaluate the use of functional respiratory imaging quantitative computed tomography in systemic sclerosis-associated interstitial lung disease staging, as well as the relationship between short-term changes in pulmonary function tests and functional respiratory imaging quantitative computed tomography with respect to disease severity.

MATERIALS AND METHODS

An observational cohort of 35 patients with systemic sclerosis was retrospectively studied by comparing serial pulmonary function tests and in- and expiratory high-resolution computed tomography over 1.5-year interval. After classification into moderate to severe lung disease and limited lung disease (using a hybrid method integrating quantitative computed tomography and pulmonary function tests), post hoc analysis was performed using mixed-effects models and estimated marginal means in terms of functional respiratory imaging parameters.

RESULTS

At follow-up, relative mean forced vital capacity percentage change was not significantly different in the limited (6.37%; N = 13; p = 0.053) and moderate to severe disease (-3.54%; N = 16; p = 0.102) groups, respectively. Specific airway resistance decreased from baseline for both groups. (Least square mean changes -25.11% predicted (p = 0.006) and -14.02% predicted (p = 0.001) for limited and moderate to severe diseases.) In contrast to limited disease from baseline, specific airway radius increased in moderate to severe disease by 8.57% predicted (p = 0.011) with decline of lower lobe volumes of 2.97% predicted (p = 0.031).

CONCLUSION

Functional respiratory imaging is able to differentiate moderate to severe disease versus limited disease and to detect disease progression in systemic sclerosis.

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia

OBJECTIVES

To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes.

METHODS

All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated.

RESULTS

A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3- (r = 0.284), and PaO2/FiO2 (P/F) values (r = - 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = -0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3- (r = 0.394), and P/F (r = - 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05).

CONCLUSION

The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation.

KEY POINTS

• Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient's outcome, in particular concerning invasive ventilation.

Screen-detected solid nodules: from detection of nodule to structured reporting

Lung cancer screening (LCS) is gaining some interest worldwide after positive results from International trials. Unlike other screening practices, LCS is performed by an extremely sensitive test, namely low-dose computed tomography (LDCT) that can detect the smallest nodules in lung parenchyma. Up-to-date detection approaches, such as computer aided detection systems, have been increasingly employed for lung nodule automatic identification and are largely used in most LCS programs as a complementary tool to visual reading. Solid nodules of any size are represented in the vast majority of subjects undergoing LDCT. However, less than 1% of solid nodules will be diagnosed lung cancer. This fact calls for specific characterization of nodules to avoid false positives, overinvestigation, and reduce the risks associated with nodule work up. Recent research has been exploring the potential of artificial intelligence, including deep learning techniques, to enhance the accuracy of both detection and characterisation of lung nodule. Computer aided detection and diagnosis algorithms based on artificial intelligence approaches have demonstrated the ability to accurately detect and characterize parenchymal nodules, reducing the number of false positives, and to outperform some of the currently used risk models for prediction of lung cancer risk, potentially reducing the proportion of surveillance CT scans. These forthcoming approaches will eventually integrate a new reasoning for development of future guidelines, which are expected to evolve into precision and personalized stratification of lung cancer risk stratification by continuous fashion, as opposed to the current format with a limited number of risk classes within fixed thresholds of nodule size. This review aims to detail the standard of reference for optimal management of solid nodules by low-dose computed and its projection into the fine selection of candidates for work up.

Pulmonary Hypertension in Association with Lung Disease: Quantitative CT and Artificial Intelligence to the Rescue? State-of-the-Art Review

Accurate phenotyping of patients with pulmonary hypertension (PH) is an integral part of informing disease classification, treatment, and prognosis. The impact of lung disease on PH outcomes and response to treatment remains a challenging area with limited progress. Imaging with computed tomography (CT) plays an important role in patients with suspected PH when assessing for parenchymal lung disease, however, current assessments are limited by their semi-qualitative nature. Quantitative chest-CT (QCT) allows numerical quantification of lung parenchymal disease beyond subjective visual assessment. This has facilitated advances in radiological assessment and clinical correlation of a range of lung diseases including emphysema, interstitial lung disease, and coronavirus disease 2019 (COVID-19). Artificial Intelligence approaches have the potential to facilitate rapid quantitative assessments. Benefits of cross-sectional imaging include ease and speed of scan acquisition, repeatability and the potential for novel insights beyond visual assessment alone. Potential clinical benefits include improved phenotyping and prediction of treatment response and survival. Artificial intelligence approaches also have the potential to aid more focused study of pulmonary arterial hypertension (PAH) therapies by identifying more homogeneous subgroups of patients with lung disease. This state-of-the-art review summarizes recent QCT developments and potential applications in patients with PH with a focus on lung disease.

Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging

PURPOSE

The aim of this methods work is to explore the different behavior of radiomic features resulting by using or not the contrast medium in chest CT imaging of non-small cell lung cancer.

METHODS

Chest CT scans, unenhanced and contrast-enhanced, of 17 patients were selected from images collected as part of the staging process. The major T1-T3 lesion was contoured through a semi-automatic approach. These lesions formed the lesion phantoms to study features behavior. The stability of 94 features of the 3D-Slicer package Radiomics was analyzed. Feature discrimination power was quantified by means of Gini's coefficient. Correlation between distance matrices was evaluated through Mantel statistic. Heatmap, cluster and silhouette plots were applied to find well-structured partitions of lesions.

RESULTS

The Gini's coefficient evidenced a low discrimination power, <0.05, for four features and a large discrimination power, around 0.8, for five features. About 90% of features was affected by the contrast medium, masking tumor lesions variability; thirteen features only were found stable. On 8178 combinations of stable features, only one group of four features produced the same partition of lesions with the silhouette width greater than 0.51, both on unenhanced and contrast-enhanced images.

CONCLUSIONS

Gini's coefficient highlighted the features discrimination power in both CT series. Many features were sensitive to the use of the contrast medium, masking the lesions intrinsic variability. Four stable features produced, on both series, the same partition of cancer lesions with reasonable structure; this may merit being objects of further validation studies and interpretative investigations.

Computer-aided analysis in evaluation and grading of interstitial lung diseases in correlation with CT-based visual scoring and pulmonary function tests

Background

Interstitial lung diseases (ILDs) represent a large group of more than 200 different entities. High resolution computed tomography (HRCT) is accepted as the gold standard imaging modality in the diagnosis of ILD. The visual-based scoring offers an advantage in finding a specific type of ILD. Computer-aided CT attenuation histogram is another way of characterizing and quantifying diffuse lung disease. The histogram analysis (HIST) consists of calculating skewness, kurtosis, and mean lung density to quantify lung disease and monitor progression. The aim of our study was to investigate the value of computer-aided analysis of HRCT for interstitial lung diseases in correlation with scoring and pulmonary function tests.

Results

This prospective study included 50 patients with suspected ILD. The mean age of patients was 46.7 years ± 12.5. Mean forced expiratory volume FEV1 was 63.6 ± 20.9. HRCT examination was done for all patients followed by CT-based visual scaling. Most of the studied patients (43.3%) had a CT visual semi-quantitative scoring ranged between 40 and 64. CT-based lung density histograms (LDH) were obtained for all patients using the 3D Slicer Software (Chest Imaging Platform). There was a significant difference between patient’s groups of different (mild, moderate, and severe) grades of ILD according to FEV1 regarding MLD, skewness, and kurtosis of corresponding CT-based density histograms (p values < 0.001). More significant and higher correlation was observed between computerized aided CT quantified mean lung densities (MLD) and (FEV1) (p value < 0.001 and r = − 0.570). The ROC curve analysis demonstrated good performance for CT visual scoring with PFT (AUC = 0.71); a cutoff scoring 15 or higher was associated with best sensitivity (75%) and specificity (100%). Meanwhile, ROC curve analysis for MLD and FEV1 demonstrated an excellent performance for computer-based CT quantification (AUC = 0.85) with a value of − 769 HU which increased sensitivity to 65% and specificity to 100%.

Conclusion

Visual-based scoring techniques offer an advantage in finding a specific type of ILD. Computer-based quantification system could be a means for accurately monitoring the disease progression or response to therapy.

In-vivo lung fibrosis staging in a bleomycin-mouse model: a new micro-CT guided densitometric approach

Although increasing used in the preclinical testing of new anti-fibrotic drugs, a thorough validation of micro-computed tomography (CT) in pulmonary fibrosis models has not been performed. Moreover, no attempts have been made so far to define density thresholds to discriminate between aeration levels in lung parenchyma. In the present study, a histogram-based analysis was performed in a mouse model of bleomycin (BLM)-induced pulmonary fibrosis by micro-CT, evaluating longitudinal density changes from 7 to 21 days after BLM challenge, a period representing the progression of fibrosis. Two discriminative densitometric indices (i.e. 40th and 70th percentiles) were extracted from Hounsfield Unit density distributions and selected for lung fibrosis staging. The strong correlation with histological findings (r_Spearman = 0.76, p < 0.01) confirmed that variations in 70th percentile could reflect a pathological lung condition and estimate the effect of antifibrotic treatments. This index was therefore used to define lung aeration levels in mice distinguishing in hyper-inflated, normo-, hypo- and non-aerated pulmonary compartments. A retrospective analysis performed on a large cohort of mice confirmed the correlation between the proposed preclinical density thresholds and the histological outcomes (r_Spearman = 0.6, p < 0.01), strengthening their suitability for tracking disease progression and evaluating antifibrotic drug candidates.

Aerosolized pH1N1 influenza infection induces less systemic and local immune activation in the lung than combined intrabronchial, nasal and oral exposure in cynomolgus macaques

Non-human primates form an important animal model for the evaluation of immunogenicity and efficacy of novel 'universal' vaccine candidates against influenza virus. However, in most studies a combination of intra-tracheal or intra-bronchial, oral and nasal virus inoculation is used with a standard virus dose of between 1 and 10 million tissue culture infective doses, which differs from typical modes of virus exposure in humans. This paper studies the systemic and local inflammatory and immune effects of aerosolized versus combined-route exposure to pandemic H1N1 influenza virus. In agreement with a previous study, both combined-route and aerosol exposure resulted in similar levels of virus replication in nose, throat and lung lavages. However, the acute release of pro-inflammatory cytokines and chemokines, acute monocyte activation in peripheral blood as well as increased cytokine production and T-cell proliferation in the lungs were only observed after combined-route infection and not after aerosol exposure. Longitudinal evaluation by computed tomography demonstrated persistence of lung lesions after resolution of the infection and a tendency for more lesions in the lower lung lobes after combined-route exposure versus upper and middle lung lobes after aerosol exposure. Computed tomography scores were observed to correlate with fever. In conclusion, influenza virus infection by aerosol exposure is accompanied by less immune-activation and inflammation in comparison with direct virus installation, despite similar levels of virus replication and development of lesions in the lungs.

Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC

OBJECTIVES

Qualitative and quantitative CT imaging features might intercept the multifaceted tumor immune microenvironment (TIME), providing a non-invasive approach to design new prognostic models in NSCLC patients.

MATERIALS AND METHODS

Our study population consisted of 100 surgically resected NSCLC patients among which 31 served as a validation cohort for quantitative image analysis. TIME was classified according to PD-L1 expression and the magnitude of Tumor Infiltrating Lymphocytes (TILs) and further defined as hot or cold by the tissue analysis of effector (CD8-to-CD3^high/PD-1-to-CD8^low) or inert (CD8-to-CD3^low/PD-1-to-CD8^high) phenotypes. CT datasets acted as source for qualitative (semantic, CT-SFs) and quantitative (radiomic, CT-RFs) features which were correlated with clinico-pathological and TIME profiles to determine their impact on survival outcome.

RESULTS

Specific CT-SFs (texture [TXT], effect [EFC] and margins [MRG]) strongly correlated to PD-L1 and TILs status and showed significant impact on survival outcome (TXT, HR:3.39, 95 % CI 1.12-10-27, P < 0.05; EFC, HR:0.41, 95 % CI 0.18-0.93, P < 0.05; MRG, HR:1.93, 95 % CI 0.88-4.25, P = 0.09). Seven CT derived radiomic features were able to sharply discriminate cases with hot (inflamed) vs cold (desert) TIME, which also exhibited opposite OS (long vs short, HR:0.09, 95 % CI 0.04-0.23, P < 0.001) and DFS (long vs short, HR:0.31, 95 % CI 0.16-0.58, P < 0.001). Moreover, we identified 6 prognostic radiomic features among which ClusterProminence displayed the highest statistical significance (HR:0.13, 95 % CI 0.06-0.31, P < 0.001). These findings were independently validated in an additional cohort of NSCLC (HR:0.11, 95 % CI 0.03-0.40, P = 0.001). Finally, in our training cohort we developed a multiparametric prognostic model, interlacing TIME and clinico-pathological characteristics with CT-SFs (ROC curve AUC:0.83, 95 % CI 0.71-0.92, P < 0.001) or CT-RFs (AUC: 0.91, 95 % CI 0.83-0.99, P < 0.001), which appeared to outperform pTNM staging (AUC: 0.66, 95 % CI 0.51-0.80, P < 0.05) in the risk assessment of NSCLC.

CONCLUSION

Higher order CT extracted features associated with specific TIME profiles may reveal a radio-immune signature with prognostic impact on resected NSCLC.

What's New on Quantitative CT Analysis as a Tool to Predict Growth in Persistent Pulmonary Subsolid Nodules? A Literature Review

Pulmonary subsolid nodules (SSNs) are observed not infrequently on thin-section chest computed tomography (CT) images. SSNs persisting after a follow-up period of three to six months have a high likelihood of being pre-malignant or malignant lesions. Malignant SSNs usually represent the histologic spectrum of pulmonary adenocarcinomas, and pulmonary adenocarcinomas presenting as SSNs exhibit quite heterogeneous behavior. In fact, while most lesions show an indolent course and may grow very slowly or remain stable for many years, others may exhibit significant growth in a relatively short time. Therefore, it is not yet clear which persistent SSNs should be surgically removed and for how many years stable SSNs should be monitored. In order to solve these two open issues, the use of quantitative analysis has been proposed to define the "tailored" management of persistent SSNs. The main purpose of this review was to summarize recent results about quantitative CT analysis as a diagnostic tool for predicting the behavior of persistent SSNs. Thus, a literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science databases to find original articles published from January 2014 to October 2019. The results of the selected studies are presented and compared in a narrative way.

Quantitative assessment of interstitial lung disease in Sjögren's syndrome

BACKGROUND

Interstitial lung disease (ILD) is a frequent manifestation of Sjögren's syndrome (SS), an autoimmune disease of salivary and lacrimal glands, and affects approximately 20% of patients. No clinical or serological features appear to be useful to predict its presence, severity or progression, and chest high-resolution computed tomography (CT) remains the gold standard for diagnosis. Semiquantitative CT (SQCT) based on visual assessment (Goh and Taouli scoring) can estimate ILD extent, although it is burdened by relevant intra- and interobserver variability. Quantitative chest CT (QCT) is a promising alternative modality to assess ILD severity.

AIM

To determine whether QCT assessment can identify extensive or limited lung disease in patients with SS and ILD.

METHODS

This multi-center, cross-sectional and retrospective study enrolled patients with SS and a chest CT scan. SQCT assessment was carried out in a blinded and centralized manner to calculate both Goh and Taouli scores. An operator-independent analysis of all CT scans with the open-source software platform Horos was used to evaluate the QCT indices. Patients were classified according to the extent of ILD and differences in QCT index distribution were investigated with non-parametric tests.

RESULTS

From a total of 102 consecutive patients with SS, the prevalence of ILD was 35.3% (36/102). There was a statistically significant difference in QCT index distribution between the SS with ILD and SS without ILD groups (p<0.001). Moreover, SS-ILD patients with ILD >20% (by Goh score) had a QCT index statistically different from those with limited ILD extent (p<0.001). Finally, QCT indices showed a moderate-to-good correlation with the Goh and Taouli scores (from 0.44 to 0.65; p<0.001).

CONCLUSIONS

QCT indices can identify patients with SS and ILD and discriminate those with lesser or greater lung disease.

Quantitative CT Analysis for Predicting the Behavior of Part-Solid Nodules with Solid Components Less than 6 mm: Size, Density and Shape Descriptors

Persistent part-solid nodules (PSNs) with a solid component <6 mm usually represent minimally invasive adenocarcinomas and are significantly less aggressive than PSNs with a solid component ≥6 mm. However, not all PSNs with a small solid component behave in the same way: some nodules exhibit an indolent course, whereas others exhibit more aggressive behavior. Thus, predicting the future behavior of this subtype of PSN remains a complex and fascinating diagnostic challenge. The main purpose of this study was to apply open-source software to investigate which quantitative computed tomography (CT) features may be useful for predicting the behavior of a select group of PSNs. We retrospectively selected 50 patients with a single PSN with a solid component <6 mm and diameter <15 mm. Computerized analysis was performed using ImageJ software for each PSN and various quantitative features were calculated from the baseline CT images. The area, perimeter, mean Feret diameter, linear mass density, circularity and solidity were significantly related to nodule growth (p ≤ 0.031). Therefore, quantitative CT analysis was helpful for predicting the future behavior of a select group of PSNs with a solid component <6 mm and diameter <15 mm.

Quantitative CT texture analysis for diagnosing systemic sclerosis: Effect of iterative reconstructions and radiation doses

To test whether texture analysis (TA) can discriminate between Systemic Sclerosis (SSc) and non-SSc patients in computed tomography (CT) with different radiation doses and reconstruction algorithms.In this IRB-approved retrospective study, 85 CT scans at different radiation doses [49 standard dose CT (SDCT) with a volume CT dose index (CTDIvol) of 4.86 ± 2.1 mGy and 36 low-dose (LDCT) with a CTDIvol of 2.5 ± 1.5 mGy] were selected; 61 patients had Ssc ("cases"), and 24 patients had no SSc ("controls"). CT scans were reconstructed with filtered-back projection (FBP) and with sinogram-affirmed iterative reconstruction (SAFIRE) algorithms. 304 TA features were extracted from each manually drawn region-of-interest at 6 pre-defined levels: at the midpoint between lung apices and tracheal carina, at the level of the tracheal carina, and 4 between the carina and pleural recesses. Each TA feature was averaged between these 6 pre-defined levels and was used as input in the machine learning algorithm artificial neural network (ANN) with backpropagation (MultilayerPerceptron) for differentiating between SSc and non-SSc patients.Results were compared regarding correctly/incorrectly classified instances and ROC-AUCs.ANN correctly classified individuals in 93.8% (AUC = 0.981) of FBP-LDCT, in 78.5% (AUC = 0.859) of FBP-SDCT, in 91.1% (AUC = 0.922) of SAFIRE3-LDCT and 75.7% (AUC = 0.815) of SAFIRE3-SDCT, in 88.1% (AUC = 0.929) of SAFIRE5-LDCT and 74% (AUC = 0.815) of SAFIRE5-SDCT.Quantitative TA-based discrimination of CT of SSc patients is possible showing highest discriminatory power in FBP-LDCT images.

Solid Indeterminate Pulmonary Nodules of Less Than 300 mm3: Application of Different Volume Doubling Time Cut-offs in Clinical Practice

In the British Thoracic Society guidelines for incidental pulmonary nodules, volumetric analysis has become the recommended method for growth assessment in solid indeterminate pulmonary nodules (SIPNs) <300 mm³. In these guidelines, two different volume doubling time (VDT) cut-offs, 400 and 600 days, were proposed to differentiate benign from malignant nodules. The present study aims to evaluate the performance of these VDT cut-offs in a group of SIPNs <300 mm³ which were incidentally detected in a routine clinical setting. During a 7-year period, we retrospectively selected 60 patients with a single SIPN <300 mm³. For each SIPN, the volume and VDT were calculated using semiautomatic software throughout the follow-up period, and the performance of the 400- and 600-day VDT cut-offs was compared. In the selected sample, there were 38 benign and 22 malignant nodules. In this group of nodules, the sensitivity, negative predictive value and accuracy of the 600-day VDT cut-off were higher than those of the 400-day VDT cut-off. Therefore, in the management of SIPNs <300 mm³ which were incidentally detected in a clinical setting, the 600-day VDT cut-off was better at differentiating benign from malignant nodules than the 400-day VDT cut-off, by reducing the number of false negatives.

Interstitial lung abnormalities: prognostic stratification of subtle radiological findings

PURPOSE OF REVIEW

The purpose of this review is to provide the radiological description of interstitial lung abnormalities (ILA) as an increasingly reported entity on high-resolution computed tomography (HRCT), and to discuss their prospective interpretation.

RECENT FINDINGS

Elementary findings consistent with ILA are described on HRCT, yet the diagnostic confidence for the interpretation of these subtle features might be challenging and further hampered by interobserver variability. Quantitative analysis is expected to provide standardized and reproducible description of ILA. There is affinity between ILA morphology and histopathological pattern, either fibrosis or atypical adenomatous hyperplasia. Beyond radiology, there are predictors of risk of ILA, such as: age, smoking habit, circulating biomarkers, and genetic sequencing. ILA with fibrotic morphology show prognostic impact including progression to interstitial lung disease, mortality from respiratory disease, and all-cause mortality. The association between ILA and susceptibility to lung damage further includes the interlacing connection between interstitial findings and lung cancer, both as a risk factor for diagnosis and as a predictor of survival.

SUMMARY

ILA are a (minor) finding on HRCT and they should be reported by radiologists for optimal management within the specific clinic-functional scenario. ILA encompass a number of semiological characteristics associated with either fibrotic or nonfibrotic disease.