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BackgroundIdiopathic pulmonary fibrosis (IPF) with co-existent emphysema, termed combined pulmonary fibrosis and emphysema (CPFE) may be associated with reduced FVC decline compared to non-CPFE IPF patients. We examined associations between mortality and functional measures of disease progression in two IPF cohorts.MethodsVisual emphysema extent (CPFE:non-CPFE: derivation cohort=317:183; replication cohort=358:152), scored on computed tomography imaging subgrouped CPFE patients using either a) 10%, or b) 15% visual emphysema threshold, or c) an unsupervised machine learning model considering emphysema and ILD extents. Baseline characteristics, 1-year forced vital capacity (FVC) and diffusion capacity for carbon monoxide (DLco) decline (linear mixed effects models), and their associations with mortality (multivariable Cox regression models) were compared across CPFE and non-CPFE subgroups.ResultsIn both IPF cohorts, CPFE patients with >10% emphysema had a greater smoking history and lower baseline DLco compared to CPFE patients with 10% emphysema, 1-year DLco decline was a better indicator of mortality than 1-year FVC decline. Results were maintained in patients suitable for therapeutic IPF trials.Results were replicated in the >15% emphysema population 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 comparable mortality associations.ConclusionWhen assessing disease progression in IPF, DLco decline should be considered in patients with >10% emphysema and a >5% 1-year FVC decline threshold considered in non-CPFE IPF patients.

During COVID-19 pandemic, lung ultrasound (LUS) proved to be of great value in the diagnosis and monitoring of patients with pneumonia. However, limited data exist regarding its use to assess aeration changes during follow-up (FU). Our study aims to prospectively evaluate 232 subjects who underwent a 3-month-FU program after hospitalization for COVID-19 at the University Hospital of Pisa. The goals were to assess the usefulness of standardized LUS compared with the gold standard chest computed tomography (CT) to evaluate aeration changes and to verify LUS and CT agreement at FU. Patients underwent in the same day a standardized 16-areas LUS and high-resolution chest CT reported by expert radiologists, assigning interpretative codes. Based on observations distribution, LUS score cut-offs of 3 and 7 were selected, corresponding to the 50th and 75th percentile, respectively. Patients with LUS scores above both these thresholds were older and with longer hospital stay. Patients with a LUS score ≥3 had more comorbidities. LUS and chest CT showed a high agreement in identifying residual pathological findings, using both cut-off scores of 3 (OR 14,7; CL 3,6-64,5, Sensitivity 91%, Specificity 49%) and 7 (OR 5,8; CL 2,3-14,3, Sensitivity 65%, Specificity 79%). Our data suggest that LUS is very sensitive in identifying pathological findings at FU after a hospitalization for COVID-19 pneumonia, compared to CT. Given its low cost and safety, LUS could replace CT in selected cases, such as in contexts with limited resources or it could be used as a gate-keeper examination before more advanced techniques.

To perform a semiquantitative and quantitative analysis of interstitial lung disease (ILD), through computed tomography (CT), in different serological subgroups of idiopathic inflammatory myopathies (IIM) patients, to find radiologic and clinical differences of disease related to serology.This was a prospective study, which included 98 IIM patients, divided into serological subgroups: anti-aminoacyl-transfer-RNA-synthetases (anti-ARS) positive and myositis-specific autoantibodies (MSA) negative.For each baseline CT the total semiquantitative score of Warrick (WS) and the automated software (Computer-Aided Lung Informatics for Pathology Evaluation and Rating) quantitative scores interstitial lung disease % (ILD%) and vascular-related structure % (VRS%) were calculated. Pulmonary function tests included total lung capacity % (TLC%), forced vital capacity % (FVC%), and diffusing capacity of the lung for carbon monoxide % (DLCO%).Inverse correlations ( P0.001) between the radiologic scores and the functional scores DLCO% and TLC% were found, the most relevant being between ILD% and DLCO% (ρ=-0.590), VRS% and DLCO% (ρ=-0.549), and WS and DLCO% (ρ=-0.471).Positive correlations between ILD% and VRS% (ρ=0.916; P0.001), WS and ILD% (ρ=0.663; ρ0.001), and WS and VRS% (ρ=0.637; P0.001) were obtained.Statistically significant higher values of WS, ILD%, and VRS% were found in the anti-ARS group (WS=15; ILD%=11; VRS%=3.5) compared with the MSA negative one (WS=2.5; ILD%=0.84; VRS%=2.2).The nonspecific interstitial pneumonia pattern was dominant. No statistically significant differences emerged at pulmonary function tests.In this study, ILD in anti-ARS-positive and MSA-negative groups was defined through semiquantitative and quantitative analysis of lung CT. The inverse correlations between the radiologic scores and TLC% and DLCO% ( P0.001) confirm the role of lung CT in the evaluation of ILD in IIM.

Lung cancer has become a paradigm for precision medicine in oncology, and liquid biopsy (LB) together with radiomics may have a great potential in this scenario. They are both minimally invasive, easy to perform, and can be repeated during patient’s follow-up. Also, increasing evidence suggest that LB and radiomics may provide an efficient way to screen and diagnose tumors at an early stage, including the monitoring of any change in the tumor molecular profile. This could allow treatment optimization, improvement of patients' quality of life, and healthcare-related costs reduction. Latest reports on lung cancer patients suggest a combination of these two strategies, along with cutting-edge data analysis, to decode valuable information regarding tumor type, aggressiveness, progression, and response to treatment. The approach seems more compatible with clinical practice than the current standard, and provides new diagnostic companions being able to suggest the best treatment strategy compared to conventional methods. To implement radiomics and liquid biopsy directly into clinical practice, an artificial intelligence (AI)-based system could help to link patients' clinical data together with tumor molecular profiles and imaging characteristics. AI could also solve problems and limitations related to LB and radiomics methodologies. Further work is needed, including new health policies and the access to large amounts of high-quality and well-organized data, allowing a complementary and synergistic combination of LB and imaging, to provide an attractive choice e in the personalized treatment of lung cancer.

Purpose Coronary artery calcifications (CAC) are very strong indicators for increased cardio-vascular (CV) risk and can be evaluated also in low-dose computed tomography (LDCT) for lung cancer screening. We assessed whether a simple and fast CAC visual score is associated with CV mortality. Methods CAC were retrospectively assessed by two observers using a 4-score (absent, mild, moderate and severe) scale in baseline LDCT obtained in 1364 participants to the ITALUNG trial who had 55–69 years of age and a smoking history ≥20 pack–years. Correlations with CV risk factors at baseline and with CV mortality after 11 years of follow-up were investigated. Results CAC were absent in 470 (34.5%), mild in 433 (31.7%), moderate in 357 (26.2%) and severe in 104 (7.6%) subjects. CAC severity correlated (≤0.001) with age, male sex, pack-years, history of arterial hypertension or diabetes, obesity and treated hypercholesterolemia. Twenty-one CV deaths occurred. Moderate or severe CAC were significantly associated with higher CV mortality after adjustment for all other known risk factors (ARR = 2.72; 95 %CI:1.04–7.11). Notably, also in subjects with none or one only additional CV risk factor, the presence of moderate-severe CAC allowed to identify a subgroup of subjects with higher CV death risk (RR = 3.66; CI95%:1.06–12.6). Conclusions Moderate or severe CAC visually assessed in LDCT examinations for lung cancer screening are independently associated with CV mortality.

Interstitial lung disease (ILD) are a large group of diffuse lung diseases characterized by similar clinical, pathological and radiological features. High resolution computed tomography (HRCT) has a central role in ILD diagnosis and management. In the last few years, computer-aided methods as Quantitative Computer Tomography (QCT) and Artificial Intelligence (AI) software were proposed as a source of reliable quantitative imaging biomarkers. The present review aimed to summarize and describe the current QCT and AI methods and to evaluate their potential diagnostic and prognostic role. The first attempt to a quantitative analysis of HRCT in ILD is represented by the density histogram analysis with the definition of two new parameter, Kurtosis and Skewness. Then texture analysis tools were developed as Adaptive Multiple Features Method (AMFM), Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER), Quantitative Lung Fibrosis (QLF) and Automated Quantification System (AQS). The introduction of AI technologies further increased the amount of objective and reproducible biomarkers. The diagnostic and prognostic role of QCT and AI methods was analyzed and confirmed in various studies, as reported in the review. QCT and AI technologies application led to the introduction of new objective biomarkers with relevant diagnostic and prognostic implications. However, there is still the need for more prospective study and the creation of open-source datasets would help to assess QCT and AI methods efficacy and to compare them.

PurposeTo investigate the correlations between densitometric and Computer Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER)-derived indices of pulmonary emphysema and their change in the short-term period for groups of patients with different smoking habits.MethodThis retrospective study included 284 subjects from the ITALUNG trial (198 men and 86 women; mean±sdage 60±4 years) who underwent low-dose chest computed tomography at baseline and 2-year follow-up. Subjects were divided into four groups (persistent smokers, restarters, quitters and former smokers) according to their smoking habit at baseline and follow-up. Densitometric and texture analyses were performed, using CALIPER software. A correlation analysis was conducted between CALIPER-derived low-attenuation areas (LAAs) and densitometric indices, including the 15th percentile of the whole-lung attenuation histogram (Perc15) and the relative areas with density ≤−950 HU (RA950). Densitometric indices and LAAs were evaluated at baseline and variation assessed longitudinally with comparisons between groups with different smoking habit. Further analysis of parenchymal changes per pulmonary zone was performed.ResultsLAAs were strongly correlated with Perc15(rs=0.81; p950(rs=0.905; p15, lower RA950, lower LAAs (particularly mild sub-class of LAAs) than the group of ex-smokers (pConclusionCALIPER texture analysis provides an objective measure comparable to traditional density/histogram features to assess the lung parenchymal changes in relation to different smoking habits.

Simple Summary Computed tomography plays a pivotal role in malignant pleural mesothelioma imaging management, ranging from diagnosis, differential diagnosis and staging to assessment of therapy response. Indeed, CT still presents some intrinsic limitations such as a poor contrast resolution between tumor and contiguous soft tissues, resulting in a challenging assessment of locoregional staging. Moreover, the current response evaluation criteria are based on unidimensional criteria, while malignant pleural mesothelioma has a complex tridimensional pattern of growth. To overcome these limits, the recent efforts in literature focused on computer-based methods, such as radiomics or automated segmentation, and magnetic resonance imaging. This review aims to describe their potential role in diagnosis, staging and assessment of therapy response in malignant pleural mesothelioma. Abstract Malignant pleural mesothelioma is a rare neoplasm with poor prognosis. CT is the first imaging technique used for diagnosis, staging, and assessment of therapy response. Although, CT has intrinsic limitations due to low soft tissue contrast and the current staging system as well as criteria for evaluating response, it does not consider the complex growth pattern of this tumor. Computer-based methods have proven their potentiality in diagnosis, staging, prognosis, and assessment of therapy response; moreover, computer-based methods can make feasible tasks like segmentation that would otherwise be impracticable. MRI, thanks to its high soft tissue contrast evaluation of contrast enhancement and through diffusion-weighted-images, could replace CT in many clinical settings.

[No Abstract Available], Wellcome Trust [209553/Z/17/Z]; UCLH Biomedical Research Centre, This study was supported by Wellcome Trust grant 209553/Z/17/Z and the UCLH Biomedical Research Centre. Funding information for this article has been deposited with the Crossref Funder Registry.

A key step of the diagnosis of Idiopathic Pulmonary Fibrosis (IPF) is the examination of high-resolution computed tomography images (HRCT). IPF exhibits a typical radiological pattern, named Usual Interstitial Pneumoniae (UIP) pattern, which can be detected in non-invasive HRCT investigations, thus avoiding surgical lung biopsy. Unfortunately, the visual recognition and quantification of UIP pattern can be challenging even for experienced radiologists due to the poor inter and intra-reader agreement. This study aimed to develop a tool for the semantic segmentation and the quantification of UIP pattern in patients with IPF using a deep-learning method based on a Convolutional Neural Network (CNN), called UIP-net. The proposed CNN, based on an encoder-decoder architecture, takes as input a thoracic HRCT image and outputs a binary mask for the automatic discrimination between UIP pattern and healthy lung parenchyma. To train and evaluate the CNN, a dataset of 5000 images, derived by 20 CT scans of different patients, was used. The network performance yielded 96.7% BF-score and 85.9% sensitivity. Once trained and tested, the UIP-net was used to obtain the segmentations of other 60 CT scans of different patients to estimate the volume of lungs affected by the UIP pattern. The measurements were compared with those obtained using the reference software for the automatic detection of UIP pattern, named Computer Aided Lungs Informatics for Pathology Evaluation and Rating (CALIPER), through the Bland-Altman plot. The network performance assessed in terms of both BF-score and sensitivity on the test-set and resulting from the comparison with CALIPER demonstrated that CNNs have the potential to reliably detect and quantify pulmonary disease in order to evaluate its progression and become a supportive tool for radiologists.

Incidental solitary pulmonary nodules (SPNs) have become an increasingly common CT finding worldwide. Although there are currently many imaging strategies for evaluating SPNs, the differential diagnosis and management of SPNs remains complex because of overlap between benign and malignant processes. Moreover, transbronchial or percutaneous CT-guided biopsies do not always allow definitive diagnoses. In such cases, video-assisted thoracic surgery (VATS) has become the preferred surgical procedure for diagnosis and, in selected cases, for treatment of indeterminate SPNs. The difficulties in localizing smaller, deeper, and ground-glass nodules have been approached with different techniques. The aim of this study was to report 20 years of experience with radioguided thoracoscopic resection of SPNs at the Regional Centre of Nuclear Medicine of Pisa.Three hundred ninety-five patients with SPNs less than 2 cm and deeper than 5 mm below the visceral pleura underwent CT-guided injection of a suspension composed of 0.1 to 0.2 mL Tc-labeled human albumin macroaggregates (Tc-MAA) and of 0.2 to 0.3 mL of nonionic contrast medium into or adjacent to the SPN. During VATS, the pulmonary area with the highest target/background ratio identified by an 11-mm-diameter collimated thoracoscopic gamma probe was resected.From 1997 to 2016, approximately 395 patients with SPN underwent VATS wedge resection using the radioguided technique. Mean SPN size was 13 mm (range, 5-20 mm) with mean distance of 15 mm (range, 6-39 mm) from the visceral pleura. Mean VATS procedural time was 40 minutes (range, 20-90 minutes), with an average time of 3 minutes (range, 1-5 minutes) to localize the nodule. Neither mortality nor major perioperative complication was reported. The success rate of VATS with radioguidance in our series was 99%. Histological examination revealed 206 benign lesions (52%), 59 primary lung tumors (15%), and 130 metastatic nodules (33%).This study demonstrates that radioguided SPN localization by VATS is a feasible, safe, and rapid procedure with highly successful rate of SPN resection.