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Challenges for the effective management of interstitial lung diseases (ILDs) include difficulties with the early detection of disease, accurate prognostication with baseline data, and accurate and precise response to therapy. The purpose of this Review is to describe the clinical and research gaps in the diagnosis and prognosis of ILD, and how machine learning can be applied to image biomarker research to close these gaps. Machine-learning algorithms can identify ILD in at-risk populations, predict the extent of lung fibrosis, correlate radiological abnormalities with lung function decline, and be used as endpoints in treatment trials, exemplifying how this technology can be used in care for people with ILD. Advances in image processing and analysis provide further opportunities to use machine learning that incorporates deep-learning-based image analysis and radiomics. Collaboration and consistency are required to develop optimal algorithms, and candidate radiological biomarkers should be validated against appropriate predictors of disease outcomes.

Prediction of disease course in patients with progressive pulmonary fibrosis remains challenging. The purpose of this study was to assess the prognostic value of lung fibrosis extent quantified at computed tomography (CT) using data-driven texture analysis (DTA) in a large cohort of well-characterized patients with idiopathic pulmonary fibrosis (IPF) enrolled in a national registry.This retrospective analysis included participants in the Australian IPF Registry with available CT between 2007 and 2016. CT scans were analysed using the DTA method to quantify the extent of lung fibrosis. Demographics, longitudinal pulmonary function and quantitative CT metrics were compared using descriptive statistics. Linear mixed models, and Cox analyses adjusted for age, gender, BMI, smoking history and treatment with anti-fibrotics were performed to assess the relationships between baseline DTA, pulmonary function metrics and outcomes.CT scans of 393 participants were analysed, 221 of which had available pulmonary function testing obtained within 90 days of CT. Linear mixed-effect modelling showed that baseline DTA score was significantly associated with annual rate of decline in forced vital capacity and diffusing capacity of carbon monoxide. In multivariable Cox proportional hazard models, greater extent of lung fibrosis was associated with poorer transplant-free survival (hazard ratio [HR] 1.20, p 0.0001) and progression-free survival (HR 1.14, p 0.0001).In a multi-centre observational registry of patients with IPF, the extent of fibrotic abnormality on baseline CT quantified using DTA is associated with outcomes independent of pulmonary function.

In patients with idiopathic pulmonary fibrosis (IPF), there is an urgent need of biomarkers which can predict disease behaviour or response to treatment. Most published studies report results based on continuous data which can be difficult to apply to individual patients in clinical practice. Having antifibrotic therapies makes it even more important that we can accurately diagnose and prognosticate in IPF patients. Advances in computer technology over the past decade have provided computer-based methods for objectively quantifying fibrotic lung disease on high-resolution CT of the chest with greater strength than visual CT analysis scores. These computer-based methods and, more recently, the arrival of deep learning-based image analysis might provide a response to these unsolved problems. The purpose of this commentary is to provide insights into the problems associated with visual interpretation of HRCT, describe of the current technologies used to provide quantification of disease on HRCT and prognostication in IPF patients, discuss challenges to the implementation of this technology and future directions.

The possibility of distinguishing fibrosing from non-fibrosing lung diseases on high resolution computed tomography (HRCT) is very important in order to be able to establish the appropriate therapy early in the course of the disease and, in some pathologies, to avoid the evolution towards a fibrosing irreversible pattern. For this reason, this article will deal with non-fibrosing pulmonary parenchymal diseases, with an acute or subacute course, which occur on HRCT with a prevalent pattern of diffuse density increase (ground glass opacities and consolidations). Since these alterations, especially ground glass opacities, are often the early sign of the onset of diseases that evolve towards fibrosis, it is important to be able to recognize them and to propose a correct differential diagnosis.

Backgrounds. Although uniportal video-assisted thoracic surgery (VATS) theoretically allows the direct palpation of any zone of the lung through a small incision, sometimes it can be difficult to localize pure ground-glass opacities anyway. The aim of this study is to evaluate the usefulness and safety of preoperative computed tomography (CT)-guided microcoil localization of GGO nodules in patients undergoing uniportal VATS lung resection.Methods. The clinical data and CT images of 30 consecutive patients (30 pulmonary nodules) who underwent preoperative CT-guided coil localization and subsequent uniportal VATS resection, from January 2017 to October 2018, were reviewed.Results. All the CT-localization procedures have been performed with success (30/30) and the mean procedure time was 35±15 minutes. The mean size of the nodules was 15,53±6,72 mm, and the mean distance of the nodules from the pleural surface was 19,08±12,08 mm. Eleven nodules (36,7%) were pure ground-glass opacities and 19 (63,3%) were mixed ground-glass with a solid component of 50% or more. In 5 cases, the localization procedure was complicated by asymptomatic pneumothoraxes and in 1 case the pneumothorax required chest tube insertion. In any case a conversion to thoracotomy was avoided because all nodules were identified and resected through uniportal VATS.Conclusions. Preoperative CT-guided coil localization seems to be a feasible, safe, and accurate procedure. It makes uniportal VATS an easy approach even for resecting small, deep, and impalpable nodules.

We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy. DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiological abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day computed tomography (CT)-confirmed dataset (normal:abnormal 53:147) and an open-source dataset (PadChest; normal:abnormal 339:334) was compared with that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent and 146 nonurgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10. DLAD-10 exhibited area under the receiver operating characteristic curve values of 0.895–1.00 in the CT-confirmed dataset and 0.913–0.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% (57/60)) than pooled radiologists (84.4% (152/180); p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% (17/24) versus 29.2% (7/24); p=0.006) and urgent (82.7% (258/312) versus 78.2% (244/312); p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean±sd time-to-report critical and urgent radiographs (640.5±466.3 versus 3371.0±1352.5 s and 1840.3±1141.1 versus 2127.1±1468.2 s, respectively; all p, A deep learning algorithm detecting 10 common abnormalities was trained with 146 717 images and showed excellent performance on chest radiographs, helping radiologists improve their performance and advance the reporting time for critical or urgent cases https://bit.ly/3k8tZ5P

Sarcoidosis is a systemic granulomatous disease affecting various organs, and the lungs are the most commonly involved. According to guidelines, diagnosis relies on a consistent clinical picture, histological demonstration of non-caseating granulomas, and exclusion of other diseases with similar histological or clinical picture. Nevertheless, chest imaging plays an important role in both diagnostic assessment, allowing to avoid biopsy in some situations, and prognostic evaluation. Despite the demonstrated lower sensitivity of chest X-ray (CXR) in the evaluation of chest findings compared to high-resolution computed tomography (HRCT), CXR still retains a pivotal role in both diagnostic and prognostic assessment in sarcoidosis. Moreover, despite the huge progress made in the field of radiation dose reduction, chest magnetic resonance (MR), and quantitative imaging, very little research has focused on their application in sarcoidosis. In this review, we aim to describe the latest novelties in diagnostic and prognostic assessment of thoracic sarcoidosis and to identify the fields of research that require investigation.

Purpose Chest imaging modalities play a key role for the management of patient with coronavirus disease (COVID-19). Unfortunately, there is no consensus on the optimal chest imaging approach in the evaluation of patients with COVID-19 pneumonia, and radiology departments tend to use different approaches. Thus, the main objective of this survey was to assess how chest imaging modalities have been used during the different phases of the first COVID-19 wave in Italy, and which diagnostic technique and reporting system would have been preferred based on the experience gained during the pandemic. Material and Methods The questionnaire of the survey consisted of 26 questions. The link to participate in the survey was sent to all members of the Italian Society of Medical and Interventional Radiology (SIRM). Results The survey gathered responses from 716 SIRM members. The most notable result was that the most used and preferred chest imaging modality to assess/exclude/monitor COVID-19 pneumonia during the different phases of the first COVID-19 wave was computed tomography (51.8% to 77.1% of participants). Additionally, while the narrative report was the most used reporting system (55.6% of respondents), one-third of participants would have preferred to utilize structured reporting systems. Conclusion This survey shows that the participants’ responses did not properly align with the imaging guidelines for managing COVID-19 that have been made by several scientific, including SIRM. Therefore, there is a need for continuing education to keep radiologists up to date and aware of the advantages and limitations of the chest imaging modalities and reporting systems.

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.

The aim of the study was to investigate differences in non-small cell lung cancer (NSCLC) intra-thoracic staging by using contrast-enhanced computed tomography (ce-CT) at the arterial phase (AP), at the arterial plus delayed phases (AP + DEP), and at the delayed phase (DEP), and to evaluate their potential impact on disease staging.Two chest radiologists with different level of expertise and a general radiologist independently reviewed the chest CT exams of 150 patients with NSCLC; CT scans were performed 40 s (AP) and 60 s (DEP) after contrast material injection. Image assessment included three reading sessions: session A (AP), session B (AP + DEP) and session C (DEP). CT descriptors for the primary tumour (T), regional nodal involvement (N), and intra-thoracic metastases (M) were evaluated in each reading session. Readers had to assign a confidence level (CL) for the assessment of each descriptor and define the TNM stage. Friedman and Cochran Q test was used to compare the assessments of the 3 reading sessions; inter-reader agreement was determined (Intraclass Correlation Coefficient - ICC).The CL was significantly higher in sessions B and C than in session A for all descriptors, with the exception of pulmonary arterial invasion. Primary tumour inner necrosis and regional nodal involvement were detected in a significantly higher number of cases in sessions B and C as compared to session A (p ≤ 0.001). DEP significantly changed N stage determination (p 0.001), particularly N3, and excluded chest wall invasion (p = 0.05) and venous invasion (p = 0.001). The agreement was good among the 3 readers (ICC = 0.761) and excellent between the 2 chest radiologists (ICC ≥ 0.940), regardless of the contrast phase.The 60-second DEP ce-CT for staging NSCLC significantly increased the readers' CL, changed the N stage determination, and helped excluding chest wall invasion and venous invasion.

Due to its pandemic diffusion, SARS- CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection represents a global threat. Despite a multiorgan involvement has been described, pneumonia is the most common manifestation of COVID-19 (Coronavirus disease 2019) and it is associated with a high morbidity and a considerable mortality. Especially in the areas with high disease burden, chest imaging plays a crucial role to speed up the diagnostic process and to aid the patient management. The purpose of this comprehensive review is to understand the diagnostic capabilities and limitations of chest X-ray (CXR) and high-resolution computed tomography (HRCT) in defining the common imaging features of COVID-19 pneumonia and correlating them with the underlying pathogenic mechanisms. The evolution of lung abnormalities over time, the uncommon findings, the possible complications, and the main differential diagnosis occurring in the pandemic phase of SARS-CoV-2 infection are also discussed.

Multidisciplinary team (MDT) discussion is the gold standard in the management of interstitial lung disease (ILD). The rheumatologist is not routinely involved in MDT, even if up to 20% of ILD are related to systemic autoimmune rheumatic diseases (SARD). The study aims to assess the agreement and its variation over time between rheumatologists and pulmonologists in the screening of SARD and between rheumatologists and an MDT extended to rheumatologists (eMDT) in evaluating the progression of SARD. We computed the agreement between the pulmonologist and rheumatologist in the identification of red flags for SARDs of 81 ILD cases and between the rheumatologist alone and eMDT in the confirmation of 70 suspected SARD-ILD progressions. The agreement between rheumatologists and pulmonologists was moderate for the detection of autoimmunity test positivity (&kappa, = 0.475, p &lt, 0.001) and family history of SARD (&kappa, = 0.491, p &lt, 0.001) and fair for the identification of extrapulmonary symptoms (&kappa, = 0.225, p = 0.064) or routine laboratory abnormalities consistent with SARD. The average agreement between the rheumatologist and eMDT in the identification of ILD progression was moderate (&kappa, = 0.436, p &lt, 0.001). The class of agreement improved from the first to the third semester. The average agreement with the rheumatologist ranged from fair to moderate, suggesting that a shared evaluation of SARD-ILD in eMDT could improve the diagnostic work-up and the evaluation of ILD progression.

Pulmonary neuroendocrine neoplasms (NENs) are a heterogeneous group of tumors of the neuroendocrine cells, which are diffusely interspersed throughout the respiratory system. Two families of NEN are established according to histological grading: the well-differentiated neuroendocrine tumors (NETs) and the high-grade neuroendocrine carcinomas (NECs). Four types of NEN are recognized, including two NETs represented by typical carcinoid (TC) and atypical carcinoid (AC) and two NECs represented by large cell neuroendocrine carcinoma (LCNEC) and small cell lung carcinoma (SCLC).

Amyloidosis is a rare hereditary or acquired protein deposition disorder with different etiologies, characterized by pathological protein deposition essentially in nearly any organs or tissues. There are 2 major forms: primary and secondary amyloidosis. Moreover, it is possible to have systemic or localized disease. The localized form of amyloidosis affecting the small intestine is rare, and it is characterized by the formation of precursor proteins at the site of the lesion. We report a case of localized small bowel amyloidosis studied by F-choline and F-FDG-iodinated PET/CT performed for staging an aggressive prostatic cancer.

Summary Background Based on international diagnostic guidelines, high-resolution CT plays a central part in the diagnosis of fibrotic lung disease. In the correct clinical context, when high-resolution CT appearances are those of usual interstitial pneumonia, a diagnosis of idiopathic pulmonary fibrosis can be made without surgical lung biopsy. We investigated the use of a deep learning algorithm for provision of automated classification of fibrotic lung disease on high-resolution CT according to criteria specified in two international diagnostic guideline statements: the 2011 American Thoracic Society (ATS)/European Respiratory Society (ERS)/Japanese Respiratory Society (JRS)/Latin American Thoracic Association (ALAT) guidelines for diagnosis and management of idiopathic pulmonary fibrosis and the Fleischner Society diagnostic criteria for idiopathic pulmonary fibrosis. Methods In this case-cohort study, for algorithm development and testing, a database of 1157 anonymised high-resolution CT scans showing evidence of diffuse fibrotic lung disease was generated from two institutions. We separated the scans into three non-overlapping cohorts (training set, n=929; validation set, n=89; and test set A, n=139) and classified them using 2011 ATS/ERS/JRS/ALAT idiopathic pulmonary fibrosis diagnostic guidelines. For each scan, the lungs were segmented and resampled to create a maximum of 500 unique four slice combinations, which we converted into image montages. The final training dataset consisted of 420 096 unique montages for algorithm training. We evaluated algorithm performance, reported as accuracy, prognostic accuracy, and weighted κ coefficient (κw) of interobserver agreement, on test set A and a cohort of 150 high-resolution CT scans (test set B) with fibrotic lung disease compared with the majority vote of 91 specialist thoracic radiologists drawn from multiple international thoracic imaging societies. We then reclassified high-resolution CT scans according to Fleischner Society diagnostic criteria for idiopathic pulmonary fibrosis. We retrained the algorithm using these criteria and evaluated its performance on 75 fibrotic lung disease specific high-resolution CT scans compared with four specialist thoracic radiologists using weighted κ coefficient of interobserver agreement. Findings The accuracy of the algorithm on test set A was 76·4%, with 92·7% of diagnoses within one category. The algorithm took 2·31 s to evaluate 150 four slice montages (each montage representing a single case from test set B). The median accuracy of the thoracic radiologists on test set B was 70·7% (IQR 65·3–74·7), and the accuracy of the algorithm was 73·3% (93·3% were within one category), outperforming 60 (66%) of 91 thoracic radiologists. Median interobserver agreement between each of the thoracic radiologists and the radiologist's majority opinion was good (κw=0·67 [IQR 0·58–0·72]). Interobserver agreement between the algorithm and the radiologist's majority opinion was good (κw=0·69), outperforming 56 (62%) of 91 thoracic radiologists. The algorithm provided equally prognostic discrimination between usual interstitial pneumonia and non-usual interstitial pneumonia diagnoses (hazard ratio 2·88, 95% CI 1·79–4·61, p Interpretation High-resolution CT evaluation by a deep learning algorithm might provide low-cost, reproducible, near-instantaneous classification of fibrotic lung disease with human-level accuracy. These methods could be of benefit to centres at which thoracic imaging expertise is scarce, as well as for stratification of patients in clinical trials. Funding None.

Thymolipoma is a rare tumor of the thymus. Classic radiologic findings of thymolipoma include fatty masses of the anterior mediastinum in conjunction with the thymus. Differential diagnosis with other more aggressive entities like liposarcoma and teratoma can be challenging. We report a case where chemical shift magnetic resonance imaging helped in the differential diagnosis.

Sarcoidosis is a multisystem chronic inflammatory disease of unknown etiology characterized by widespread growth of non-caseating granulomas. The diagnosis of sarcoidosis is based on clinical and imaging presentation, histologic confirmation and the absence of alternative diseases. Radiology and Nuclear Medicine play an essential role in the diagnostic work-up of patients with sarcoidosis to assess disease extent and activity. In addition, imaging modalities have shown their potential in managing these patients in terms of treatment response and prognostic assessment. Sarcoidosis has a predilection for chest involvement, showing typical and atypical manifestations in the lungs, airways and hilar/mediastinal lymph nodes. Chest radiography (X-ray) still plays an important role in suggesting diagnosis for cases with typical presentation of sarcoidosis, while Computed Tomography (CT) has higher accuracy in detecting early stage disease and in narrowing differential diagnosis, particularly in atypical manifestations. For extrathoracic involvement, both CT and MR (Magnetic Resonance) have comparable performance even though MR is the modality of choice for assessing neurosarcoidosis and cardiac sarcoidosis. In the last decades Positron Emission Tomography/CT (PET/CT) has demonstrated an increasing and relevant value in assessing disease extent and activity, treatment planning and therapy response, with a crucial role in the management of cardiac sarcoidosis. In this article, an overview of the possible imaging manifestations of thoracic and extrathoracic sarcoidosis and current concepts on staging, response assessment and prognosis is provided. Finally, the potential applications of non-FDG radiotracers is briefly discussed.

Accurate diagnosis of idiopathic pulmonary fibrosis (IPF) is essential to ensure prompt initiation of appropriate treatment and enrolment in clinical trials. According to current international guidelines [1], in the appropriate clinical context the identification of the usual interstitial pneumonia (UIP) pattern on chest high-resolution computed tomography (HRCT) allows a confident diagnosis of IPF. Patients referred to tertiary centres for suspected interstitial lung disease (ILD) may provide CT images that do not fulfil recommended scanning protocols [1], therefore triggering the execution of another CT. However, the impact of suboptimal technical quality on the identification of the UIP pattern by thoracic radiologists is unknown. We assessed the quality of chest CT scans performed in the context of an ILD network including a tertiary ILD centre (hub) and the affiliated centres (spokes), and the impact on the identification of the UIP pattern. Footnotes This manuscript has recently been accepted for publication in the European Respiratory Journal . It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article. Conflict of interest: Dr. Sgalla reports personal fees from Boehringer Ingelheim, outside the submitted work. Conflict of interest: Dr. LARICI has nothing to disclose. Conflict of interest: Dr. Re has nothing to disclose. Conflict of interest: Dr. Farchione has nothing to disclose. Conflict of interest: Dr. Cicchetti has nothing to disclose. Conflict of interest: Dr. Calandriello has nothing to disclose. Conflict of interest: Dr. Comes has nothing to disclose. Conflict of interest: Dr. Golfi has nothing to disclose. Conflict of interest: Dr. Iovene has nothing to disclose. Conflict of interest: Dr. Varone has nothing to disclose. Conflict of interest: Dr. Manfredi has nothing to disclose. Conflict of interest: Dr. Richeldi reports personal fees from Sanofi-Aventis, personal fees from Roche, personal fees from ImmuneWorks, personal fees from Shionogi, personal fees from Boehringer Ingelheim, personal fees from Celgene, personal fees from Nitto, personal fees from Fibrogen, personal fees from Promedior, personal fees from Bristol Myers Squibb, personal fees from DynaMed, outside the submitted work.

Idiopathic Pulmonary Fibrosis (IPF) is a chronic, progressive, fibrosing interstitial pneumonia associated with the histologic and/or radiologic pattern of usual interstitial pneumonia (UIP). Nowadays, the high-resolution computed tomography pattern of “definite UIP” is enough to define a diagnosis of UIP without histological proof. This is pivotal especially in elderly patients with comorbidities. Early recognition of IPF is relevant for its prognostic implication. Some pharmacological strategies have been proposing novel molecules that tend to slow lung function decline, even though without healing fibrosis. We report a case of ex-heavy smoker male with IPF showing clinical and radiological improvement after 11 months of treatment with Pirfenidone. The definite diagnosis was challenging and it was reached by a multidisciplinary approach.

Highlights • The awareness of imaging findings of COVID-19 pneumonia and their relationship with pathogenesis is useful in providing a confident diagnosis. • Chest X-ray might be used as first-line imaging modality in the areas with high prevalence of disease. • HRCT plays a pivotal role in the assessment of complications and differential diagnosis with other infectious and non- infectious lung disease., Due to its pandemic diffusion, SARS- CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) infection represents a global threat. Despite a multiorgan involvement has been described, pneumonia is the most common manifestation of COVID-19 (Coronavirus disease 2019) and it is associated with a high morbidity and a considerable mortality. Especially in the areas with high disease burden, chest imaging plays a crucial role to speed up the diagnostic process and to aid the patient management. The purpose of this comprehensive review is to understand the diagnostic capabilities and limitations of chest X-ray (CXR) and high-resolution computed tomography (HRCT) in defining the common imaging features of COVID-19 pneumonia and correlating them with the underlying pathogenic mechanisms. The evolution of lung abnormalities over time, the uncommon findings, the possible complications, and the main differential diagnosis occurring in the pandemic phase of SARS-CoV-2 infection are also discussed.

Lung ultrasonography (LUS) provides an estimation of peripheral airspace (PAS) geometry of the lung. Altered PAS produces sonographic interstitial syndrome (SIS). Idiopathic pulmonary fibrosis (IPF) involves peripheral lung with altered PAS. The aim of the study is to correlate echographic patterns with peripheral fibrotic changes on high-resolution Chest CT scan (HRCT). Patients underwent LUS and HRCT on the same date. Four LUS patterns were described: (1) near normal, (2) SIS with predominance of reverberant artifacts, (3) SIS with vertical predominance, (4) white lung. Four HRCT grades of peripheral fibrotic infiltrates were reported: grade 1 mild, grade 2 moderate, grade 3 severe, grade 4 massive or honeycomb. LUS pattern 1 was indicative of mild to moderate fibrotic alterations in 100% of cases. LUS pattern 2 matched with HRCT grade 2 in 24 out of 30 cases (77%). Huge discordance in four cases because of large honeycomb cysts. LUS pattern 3 was indicative of severe to massive alterations in 100% of cases. LUS pattern 4 showed a heterogeneous distribution of HRCT grades, severe changes, and ground glass opacities (GGO). This preliminary work demonstrates some level of agreement between LUS patterns and HRCT grades. Limitations and methodological issues have been shown to support subsequent studies of agreement.

We investigated the utility of a deep learning algorithm for providing automated classification of fibrotic lung disease on HRCT according to criteria specified in two international diagnostic guideline statements; 1) the ATS/ERS/JRS.ALAT guidelines for diagnosis and management of IPF and 2) the Fleischner Society diagnostic criteria for IPF. We benchmarked algorithm performance against a cohort of 91 thoracic radiologists. 1157 HRCT studies showing evidence of a fibrotic lung disease from 2 institutions were used to train the algorithm which was based on Google9s InceptionV2 neural network. Algorithm performance, reported as accuracy, prognostic accuracy and Cohen9s kappa coefficient of interobserver agreement, was evaluated on a cohort of 150 HRCTs with fibrotic lung disease against the majority vote of ninety-one specialist thoracic radiologists drawn from multiple international thoracic imaging societies. The median accuracy of the thoracic radiologists was 70.7±0.09% while accuracy of the algorithm was 73.3%, outperforming 60/91 of the thoracic radiologists. The algorithm9s categorisation of UIP vs not UIP provided equal prognostic discrimination that the majority opinion of the thoracic radiologists (HR 2.88, p HRCT evaluation by a deep learning algorithm may provide low-cost, reproducible, near-instantaneous classification of fibrotic lung disease on HRCT with human-level accuracy.

The incidence of indeterminate pulmonary nodules has risen constantly over the past few years. Determination of lung nodule malignancy is pivotal, because the early diagnosis of lung cancer could lead to a definitive intervention. According to the current international guidelines, size and growth rate represent the main indicators to determine the nature of a pulmonary nodule. However, there are some limitations in evaluating and characterising nodules when only their dimensions are taken into account. There is no single method for measuring nodules, and intrinsic errors, which can determine variations in nodule measurement and in growth assessment, do exist when performing measurements either manually or with automated or semi-automated methods. When considering subsolid nodules the presence and size of a solid component is the major determinant of malignancy and nodule management, as reported in the latest guidelines. Nevertheless, other nodule morphological characteristics have been associated with an increased risk of malignancy. In addition, the clinical context should not be overlooked in determining the probability of malignancy. Predictive models have been proposed as a potential means to overcome the limitations of a sized-based assessment of the malignancy risk for indeterminate pulmonary nodules.

Sarcoidosis is a systemic disease characterized by widespread development of noncaseating epithelioid granulomas. Based on a joint statement by the American Thoracic Society (ATS), European Thoracic Society (ERS), and the World Association for Sarcoidosis and other Granulomatous Disorders (WASOG), the diagnosis of sarcoidosis requires a compatible clinical picture, histologic demonstration of noncaseating granulomas, and exclusion of other diseases that may give a similar histologic or clinical picture. Although to a casual reader these recommendations suggest that making a diagnosis of sarcoidosis is straightforward, a consensus on the diagnostic role of imaging in sarcoidosis remains unclear. First, despite the primacy of chest computed tomography (CT) for evaluating diffuse lung disease, a guideline-based diagnosis of sarcoidosis without biopsy requires specific appearances on chest radiography (Löfgren's syndrome) or 67Ga Scintigraphy (Heerfordt's syndrome). Second, although the literature on CT as a prognostic tool in interstitial lung disease is relatively extensive, very little of this focuses specifically on sarcoidosis. Third, staging of sarcoidosis relies on radiographic appearances at presentation using a system devised by Scadding more than 40 years ago. This is in stark contrast to idiopathic pulmonary fibrosis where significant advances have been made in imaging-based staging and computer-assisted quantification of disease in the past 10 years. In this article, in addition to describing the myriad of imaging patterns seen in sarcoidosis, the impact imaging has on diagnosis, staging, and prediction of outcome will also be discussed. Cardiac-related sarcoidosis and neurosarcoidosis will be discussed in separate articles.

SummaryBackgroundMortality in pulmonary sarcoidosis is highly variable and a reliable prognostic algorithm for disease staging and for guiding management decisions is needed. The objective of this study is to derive and test a staging system for determining prognosis in pulmonary sarcoidosis.MethodsWe identified the prognostic value of high-resolution computed tomography (HRCT) patterns and pulmonary function tests, including the composite physiological index (CPI) in patients with pulmonary sarcoidosis. We integrated prognostic physiological and HRCT variables to form a clinical staging algorithm predictive of mortality in a test cohort. The staging system was externally validated in a separate cohort by the same methods of discrimination used in the primary analysis and tested for clinical applicability by four test observers.FindingsThe test cohort included 251 patients with pulmonary sarcoidosis in the study referred to the Sarcoidosis clinic at the Royal Brompton Hospital, UK, between Jan 1, 2000, and June 30, 2010. The CPI was the strongest predictor of mortality (HR 1·04, 95% CI 1·02–1·06, p

This study compared the sensitivity of two commercial computer-aided detection (CAD) systems in identifying noncalcified pulmonary nodules on low-dose multidetector computed tomography (MDCT) scans by using a double reference standard.Three chest low-dose MDCT scans of patients who had undergone lung cancer screening were retrospectively analysed using two distinct commercial CAD systems: LungCAD VC10A, Siemens Medical Solutions (CAD1) and LungVCAR, GE Healthcare (CAD2). The exact location of each finding suggested by each system was recorded by an independent reader according to spatial coordinates (x, y, z). Two panels of experienced thoracic radiologists from two different institutions independently established two reference standards (RS1, RS2) by identifying the true positive findings with spatial coordinates without using CAD. Sensitivity of the two CAD systems, defined by lesionlevel analysis, was tested and sensitivities compared.RS1 identified 34 noncalcified pulmonary nodules, whereas RS2 identified 54. The total number of findings detected by the two CAD systems was 684. CAD1 correctly identified 13/34 nodules (sensitivity 38%) for RS1 and 17/54 (sensitivity 30%) for RS2, whereas CAD2 correctly identified 11/34 nodules (sensitivity 35%) for RS1 and 13/54 (sensitivity 23%) for RS2. Comparison between the two CAD systems did not show a statistically significant difference in terms of sensitivity (p0.05) for both RS1 (p=0.42) and RS2 (p=0.33).The two commercial CAD systems had similar sensitivity in detecting noncalcified pulmonary nodules on low-dose MDCT of the chest.

Multiecho gradient-echo T*2 magnetic resonance imaging is a well-established technique for iron overload assessment but there are few reports concerning the pancreas. The aim of this work was to assess the feasibility and reproducibility of the magnetic resonance imaging for measuring pancreatic regional and global T*2 values, to establish the lower limit of normal in a large cohort of healthy subjects and to correlate the measured values with age and gender. One hundred and twenty healthy subjects (61 males, 51±17 years) underwent magnetic resonance imaging (1.5T) using a multiecho gradient-echo T*2 sequence. T*2 measurements were performed in pancreatic head, body, and tail. The global value was calculated as the mean. Measurement of pancreatic T*2 values was feasible in all subjects. For the T*2 global value the coefficient of variation for intraoperator and interoperator reproducibility were 7.7% and 13%, respectively. The global T*2 values ranged from 24 to 52 ms with the lower limit of normal of 26 ms. There were no significant differences among the regional pancreatic T*2 values. No significant correlation was found between T*2 and patient age or gender. In conclusion, pancreatic T*2 measurements appear to be feasible, reproducible, nontime-consuming and reliable. Gender- and age-related differences concerning pancreatic T*2 were not found.

This study was done to prospectively assess the repeatability and intra- and interobserver variability of first-pass perfusion with 64-detector-row computed tomography (CT) in non-small-cell lung cancer (NSCLC) with a maximum diameter of up to 8 cm.Twelve patients with NSCLC underwent 64-detector-row first-pass CT perfusion (CTP) of the whole tumour. Two different techniques were used according to lesion size (cine mode; sequential mode). After 24 h, each study was repeated to assess repeatability. Lesion blood volume (BV), blood flow (BF), mean transit time (MTT) and peak enhancement intensity (PEI) were automatically calculated by two chest radiologists in two different reading sessions. Intra- and interobserver variability was also assessed.The first-pass CTP technique was repeatable and precise with within-subject coefficient of variation (WCV) of 9.3, 16.4, 11.2 and 14.9 %, respectively, for BV, BF, MTT and PEI. High intra- and interobserver agreement was demonstrated for each perfusion parameter, with Cronbach's α coefficients and intraclass correlation coefficients ranging from 0.99 to 1. Precision of measurements was slightly better for intraobserver analysis with WCV ranging between 1.05 and 3.03 %.Non-small-cell lung cancer first-pass perfusion performed with 64-detector-row CT showed good repeatability and high intra- and interobserver agreement for all perfusion parameters and may be considered a reliable and robust tool for assessing tumour vascularisation.

Hemoptysis is the expectoration of blood that originates from the lower respiratory tract. It is usually a self-limiting event but in fewer than 5% of cases it may be massive, representing a life-threatening condition that warrants urgent investigations and treatment. This article aims to provide a comprehensive literature review on hemoptysis, analyzing its causes and pathophysiologic mechanisms, and providing details about anatomy and imaging of systemic bronchial and nonbronchial arteries responsible for hemoptysis. Strengths and limits of chest radiography, bronchoscopy, multidetector computed tomography (MDCT), MDCT angiography and digital subtraction angiography to assess the cause and lead the treatment of hemoptysis were reported, with particular emphasis on MDCT angiography. Treatment options for recurrent or massive hemoptysis were summarized, highlighting the predominant role of bronchial artery embolization. Finally, a guide was proposed for managing massive and non-massive hemoptysis, according to the most recent medical literature.

Background. The aim of the article is to systematically review published data about the comparison between positron emission tomography (PET) or PET/computed tomography (PET/CT) using Fluorine-18-Fluorodeoxyglucose (FDG) and whole-body magnetic resonance imaging (WB-MRI) in patients with different tumours. Methods. A comprehensive literature search of studies published in PubMed/MEDLINE, Scopus and Embase databases through April 2012 and regarding the comparison between FDG-PET or PET/CT and WB-MRI in patients with various tumours was carried out. Results. Forty-four articles comprising 2287 patients were retrieved in full-text version, included and discussed in this systematic review. Several articles evaluated mixed tumours with both diagnostic methods. Concerning the specific tumour types, more evidence exists for lymphomas, bone tumours, head and neck tumours and lung tumours, whereas there is less evidence for other tumour types. Conclusions. Overall, based on the literature findings, WB-MRI seems to be a valid alternative method compared to PET/CT in oncology. Further larger prospective studies and in particular cost-effectiveness analysis comparing these two whole-body imaging techniques are needed to better assess the role of WB-MRI compared to FDG-PET or PET/ CT in specific tumour types.

Multiecho gradient-echo T*2 magnetic resonance imaging is a well-established technique for iron overload assessment but there are few reports concerning the pancreas. The aim of this work was to assess the feasibility and reproducibility of the magnetic resonance imaging for measuring pancreatic regional and global T*2 values, to establish the lower limit of normal in a large cohort of healthy subjects and to correlate the measured values with age and gender. One hundred and twenty healthy subjects (61 males, 51±17 years) underwent magnetic resonance imaging (1.5T) using a multiecho gradient-echo T*2 sequence. T*2 measurements were performed in pancreatic head, body, and tail. The global value was calculated as the mean. Measurement of pancreatic T*2 values was feasible in all subjects. For the T*2 global value the coefficient of variation for intraoperator and interoperator reproducibility were 7.7% and 13%, respectively. The global T*2 values ranged from 24 to 52 ms with the lower limit of normal of 26 ms. There were no significant differences among the regional pancreatic T*2 values. No significant correlation was found between T*2 and patient age or gender. In conclusion, pancreatic T*2 measurements appear to be feasible, reproducible, nontime-consuming and reliable. Gender- and age-related differences concerning pancreatic T*2 were not found.