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Relatives of patients with familial interstitial pneumonia are at increased risk for pulmonary fibrosis, and develop preclinical pulmonary fibrosis (PrePF).We defined the incidence and progression of new onset PrePF; its relationship to survival among first degree relatives of families with familial interstitial pneumonia.This is a cohort study of family members with familial interstitial pneumonia who were initially screened with a questionnaire and chest HRCT scan, and approximately 4 years later, the evaluation was repeated. 493 asymptomatic first-degree relatives of patients with familial interstitial pneumonia were evaluated at baseline, and 296 (60%) of the original subjects participated in subsequent evaluation.The median interval between HRCTs was 3.9 years (IQ range 3.5-4.4). 252 subjects who agreed to repeat evaluation were originally determined not to have PrePF at baseline; 16 developed PrePF. A conservative estimate of the annual incidence of PrePF is 1,023 per 100, 000 person years (95% CI 511-1831). Of 44 subjects with PrePF at baseline, 38.4% subjects had worsening dyspnea compared to 15.4% of those without PrePF (P=0.002). Usual interstitial pneumonia (UIP) by HRCT (P0.0002) and baseline quantitative fibrosis score (P0.001) are also associated with worsening dyspnea. PrePF at the initial screen is associated with decreased survival (P0.001).The incidence of PrePF in this at-risk population is at least 100-fold higher than that reported for sporadic idiopathic pulmonary fibrosis (IPF). While PrePF and IPF represent distinct entities, our study demonstrates that PrePF, like IPF, is progressive and associated with decreased survival.

Background Endothelium damage is a crucial element in the pathogenesis of SARS-Cov-2 infection. Most casualties in critical COVID-19 cases are due to ARDS, diffuse coagulopathy and cytokine storm. ARDS itself is a consequence of pulmonary endothelial cells damage. Damage to retinal capillary microcirculation in post-infective period has been investigated through Optical Coherence Tomography Angiography (OCTA). The aim of the present study is to find a correlation between signs of retinal vascular damage and pulmonary impairment. Methods Patients admitted to hospital and subsequently recovered from COVID-19 infection were summoned 1 month later to undergo coherence tomography (CT) scan and OCTA examination. Results The study population included 87 COVID-19 patients with a mean age of 54.28 ± 14.44 years. Oxygen therapy, non-invasive and invasive mechanical ventilation were necessary in 33, 11 and 4 patients respectively to provide respiratory support during the acute course of the disease. Pulmonary involvement interested 54 patients (62.1%). Peripheral (27.6%) or diffuse (29.9%) involvement and ground glass (GG) opacities (47.1%) represented the prevalent radiological finding. A reduced RCPI FI was independently correlated with the presence of reticulation pattern in CT scan ( p = .019). Also, RNFL and GCC were thinner in patients who displayed reticulation pattern (respectively p = .025 and p = .015). Conclusions A reduction in RPCP-FI and RNFL and GCC thickness were independently correlated to the presence of CT reticulation pattern. This association can reflect cytokine induced remodeling in both organs as a consequence of systemic endothelial damage and inflammation.

COVID-19 pneumonia represents a global threatening disease, especially in severe cases. Chest imaging, with X-ray and high-resolution computed tomography (HRCT), plays an important role in the initial evaluation and follow-up of patients with COVID-19 pneumonia. Chest imaging can also help in assessing disease severity and in predicting patient’s outcome, either as an independent factor or in combination with clinical and laboratory features. This review highlights the current knowledge of imaging features of COVID-19 pneumonia and their temporal evolution over time, and provides recent evidences on the role of chest imaging in the prognostic assessment of the disease.

IntroductionDespite overlaying an irreplaceable role as a key diagnostic tool in modern medicine, the role of radiologist still appears to be unclear to patients.Methods We conducted a survey in outpatient clinic of Radiological Sciences Department of the University Hospital “A. Gemelli” in Rome, aiming to assess how correct patients identify the figure of the radiologist. The patients were interviewed by the trained physician using structured questionnaire.Results We included the number of 259 patients. Majority were female 63.3%, most were 60-69 years old (24.3%), have finished second grade secondary school (35.1%) and were subjected to magnetic resonance (28.6%) while the least were subjected to chest X-ray (8.1%). Only 38.7% answered correctly to question No 1 “Who performed your examination?”, and only 30.9% correctly identified the radiologist as a person interpreting the exam (question No 2 “Who is going to interpret your radiological examination?”). Overall, 16.8% responded correctly to the both questions. Significantly less patients with primary school (OR: 0.18, CI 95% 0.06-0.49) and first grade secondary school (OR: 0.37, CI 95% 0.18-0.75) correctly addressed the question No 1 in compare to those with second grade secondary school. The first grade secondary education (OR: 0.43, CI 95% 0.20-0.92) was inversely associated with correct answer to question No 2. Patients with primary education were significantly less prone to give both correct answers (OR: 0.12, CI 95% 0.02-0.60).Conclusion We report insufficient knowledge among patients on radiologist’s role in healthcare system. The level of knowledge is associated with level of education.

BACKGROUND Computed tomography (CT) plays a key role in evaluation of paranasal sinus inflammation, but improved, and standardized, objective assessment is needed. Computerized volumetric analysis has benefits over visual scoring, but typically relies on manual image segmentation, which is difficult and time-consuming, limiting practical applicability. We hypothesized that a convolutional neural network (CNN) algorithm could perform automatic, volumetric segmentation of the paranasal sinuses on CT, enabling efficient, objective measurement of sinus opacification. In this study we performed initial clinical testing of a CNN for fully automatic quantitation of paranasal sinus opacification in the diagnostic workup of patients with chronic upper and lower airway disease. METHODS Sinus CT scans were collected on 690 patients who underwent imaging as part of multidisciplinary clinical workup at a tertiary care respiratory hospital between April 2016 and November 2017. A CNN was trained to perform automatic segmentation using a subset of CTs (n = 180) that were segmented manually. A nonoverlapping set (n = 510) was used for testing. CNN opacification scores were compared with Lund-MacKay (LM) visual scores, pulmonary function test results, and other clinical variables using Spearman correlation and linear regression. RESULTS CNN scores were correlated with LM scores (rho = 0.82, p < 0.001) and with forced expiratory volume in 1 second (FEV1 ) percent predicted (rho = -0.21, p < 0.001), FEV1 /forced vital capacity ratio (rho = -0.27, p < 0.001), immunoglobulin E (rho = 0.20, p < 0.001), eosinophil count (rho = 0.28, p < 0.001), and exhaled nitric oxide (rho = 0.40, p < 0.001). CONCLUSION Segmentation of the paranasal sinuses on CT can be automated using a CNN, providing truly objective, volumetric quantitation of sinonasal inflammation.

The aim of this study is to present the results of the Italian survey on the management of pulmonary nodules incidentally identified at computed tomography (CT). An online electronic survey, consisting of 23 multiple-choice questions, was created using the SurveyMonkey web-based tool. The questionnaire was developed by the Board of the Italian College of Chest Radiology of the Italian Society of Medical and Interventional Radiology (SIRM) and by an experienced group of Italian Academic Chest Radiologists. The link to the online electronic survey was submitted by email to all the SIRM members. A total of 767 radiologists, corresponding to 7.5% of all the SIRM members, participated in the online survey. The majority of participants (92%) routinely describe the attenuation of pulmonary nodules in the report, and 84.1% recommend the further follow-up, with 92.7% of respondents taking CT nodule morphological features into consideration. The 57.7% of participants adhere to the Fleischner Society guidelines for the management of incidental pulmonary nodules. However, 56.6% and 75.6% of respondents have a more cautious approach than that recommended by the guidelines and tend to use a shorter follow-up for both solid and ground-glass nodules, respectively. Finally, 94.5% of participants favor congresses and refresher courses dedicated to insights on lung nodule diagnosis and management. This survey demonstrates that the management of pulmonary nodules incidentally detected on CT is still complex and controversial. The majority of SIRM members express a need for an update on this topic.

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.

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).

The aim of this study was to apply density correction method to the quantitative image analysis of non-small cell lung cancer (NSCLC) computed tomography (CT) images, determining its influence on overall survival (OS) prediction of surgically treated patients. Clinicopathological (CP) data and preoperative CT scans, pre- and post-contrast medium (CM) administration, of 57 surgically treated NSCLC patients, were retrospectively collected. After CT volumetric density measurement of primary gross tumour volume (GTV), aorta and tracheal air, density correction was conducted on GTV (reference values: aortic blood and tracheal air). For each resulting data set (combining CM administration and normalization), first-order statistical and textural features were extracted. CP and imaging data were correlated with patients 1-, 3- and 5-year OS, alone and combined (uni-/multivariate logistic regression and Akaike information criterion). Predictive performance was evaluated using the ROC curves and AUC values and compared among non-normalized/normalized data sets (DeLong test). The best predictive values were obtained when combining CP and imaging parameters (AUC values: 1 year 0.72; 3 years 0.82; 5 years 0.78). After normalization resulted an improvement in predicting 1-year OS for some of the grey level size zonebased features (large zone low grey level emphasis) and for the combined CP-imaging model, a worse performance for grey level co-occurrence matrix (cluster prominence and shade) and first-order statistical (range) parameters for 1- and 5-year OS, respectively. The negative performance of cluster prominence in predicting 1-year OS was the only statistically significant result (p value 0.05). Density corrections of volumetric CT data showed an opposite influence on the performance of imaging quantitative features in predicting OS of surgically treated NSCLC patients, even if no statistically significant for almost all predictors.

Background Respiratory alemtuzumab-related adverse events are clinically heterogeneous and include respiratory infections, infusion-related dyspnea, hypoxia and secondary autoimmune disorders. Case report Here we report three cases of drug-induced lung disease following treatment with alemtuzumab in multiple sclerosis patients. First case was diagnosed as a non-specified intestitial pneumonitis associated with organizing pneumonia with subacute onset, second case was an acute respiratory distress syndrome with onset during second cycle, third case was a diffuse acute alveolar hemorrhage during first cycle infusion. All patients developed acute respiratory failure, reversible after steroid therapy. Conclusions Clinicians should be aware to early recognize acute and subacute respiratory adverse events for a promptly management. In these patients re-treatment is challenging.

Imaging plays a key role in the assessment of response to therapy in lung cancer, particularly in the definition of disease progression/reduction after chemotherapy (ChT) and/or radiotherapy (RT) and in the subsequent management of the patient. Tumour response criteria have been introduced aiming at standardizing results in cancer treatments. However, measurement errors and limits of such parameters, as well as the complex imaging response patterns observed after the use of newer personalized pharmacological treatments for non-small-cell lung cancer (NSCLC), pose a challenge in the application of common standardized criteria for the response evaluation. The tumour response after RT can present with different patterns and timing, highly dependent on the techniques used. One of the major issues is distinguishing radiation-induced lung injury/disease (RILI/RILD) from tumour recurrence. Therefore, response assessment in lung cancer should evolve according to the advances in lung cancer treatment and the introduction of newer diagnostic techniques, including functional and molecular imaging.

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.

Rationale and Objectives To estimate recurrence risk after surgery in nonsmall cell lung cancer (NSCLC) patients by employing tumoral and peritumoral radiomics analysis. Materials and Methods One-hundred twenty-four surgically treated stage IA–IIB NSCLC patients’ data from 2008 to 2013 were retrospectively collected. Patient outcome was defined as local recurrence (LR), distant metastasis (DM), and (sum of LR and DM) total recurrence (TR) at follow-up. Volumetric region of interests (ROIs) were drawn for the tumor, peritumoral lung parenchyma (2 cm around the tumor) and involved lobe on CT images. Ninety-four (morphological, first-order, textural, fractal-based) radiomics features were extracted from the ROIs and datasets were created from single or combined ROIs. Predictive models were built with radiomics signature (RS) and clinicopathological data, and the area under the curve (AUC) was used to evaluate the performance. Radiomics score was calculated with the best models’ feature coefficients, low- and high-risk groups of patients defined accordingly. Kaplan–Meier curves were built, and the log-rank test was used for comparison among low- and high-risk groups. Differences in recurrence risk among the two risk groups were calculated (chi-square test). Results Fifty-six patients developed TR (25 LR, 31 DM). The tumor-node-metastasis (TNM) stage recurrence predictability (AUCTR 0.680; AUCDM 0.672; AUCLR 0.580) was substantially improved when RS was added to the predictive model (AUCTR 0.760; AUCDM 0.759; AUCLR 0.750). Seventy-five percent of high-risk patients developed TR. Recurrence risk of the high-risk group was 16-fold higher than that of the low-risk group (p Conclusion Combination of the tumoral and peritumoral RS with TNM staging system outperformed TNM staging alone in individualized recurrence risk estimation of patients with surgically treated NSCLC.

ObjectivesTo evaluate interstitial lung disease associated with systemic sclerosis (SSc-ILD) and its changes during treatment by using quantitative analysis (QA) compared to semi-quantitative analysis (semiQA) of chest computed tomography (CT) scans. To assess the prognostic value of QA in predicting functional changes.Materials and methodsWe retrospectively selected 35 consecutive patients with SSc-ILD with complete pulmonary functional evaluation, Doppler-echocardiography, immunological tests, and chest CT scan at both baseline and follow-up after immunosuppressive therapy. CT images were analyzed by two chest radiologists for semiQA and by a computational platform for texture analysis of ILD patterns (CALIPER) for QA. Concordance between semiQA and QA was tested. Traction bronchiectasis severity was scored. Analysis of ROC curves was performed.ResultsSeventy CT scans were analyzed and QA failed in 4/70 scans. Thus, the final population included 31/35 patients (51.3±12.1 years). QA had a weak-to-good concordance with semiQA (ICC reticular:0.275; ICC ground-glass:0.667) and QA correlated better than semiQA (r = -0.3 to -0.74 vs r = -0.3 to -0.4) with functional parameters. Both methods correlated with traction bronchiectases score and pulmonary artery diameter at CT. A pulmonary artery diameter ≥29mm distinguished patients with lower lung volumes and ILD extent greater than 39% (p0.05) in predicting disease progression as assessed by functional parameters, whereas variation in total lung volume at QA accurately predicted changes in the composite functional respiratory endpoint with FVC% and DLco% (AUC = 0.74; 95%CI: 0.54 to 0.93; p = 0.03).ConclusionsPulmonary QA of CT images can objectively quantify specific patterns of ILD changes during treatment in patients with SSc-ILD. Changes in QA patterns do not correlate with functional changes, but variation in total lung volume at QA accurately predicted changes in the composite functional respiratory endpoint with FVC% and DLco%. Pulmonary artery diameter at CT reflects the interstitial involvement, identifying patients with more severe prognosis.

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.

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.

Missed lung cancer is a source of concern among radiologists and an important medicolegal challenge. In 90% of the cases, errors in diagnosis of lung cancer occur on chest radiographs. It may be challenging for radiologists to distinguish a lung lesion from bones, pulmonary vessels, mediastinal structures, and other complex anatomical structures on chest radiographs. Nevertheless, lung cancer can also be overlooked on computed tomography (CT) scans, regardless of the context, either if a clinical or radiologic suspect exists or for other reasons. Awareness of the possible causes of overlooking a pulmonary lesion can give radiologists a chance to reduce the occurrence of this eventuality. Various factors contribute to a misdiagnosis of lung cancer on chest radiographs and on CT, often very similar in nature to each other. Observer error is the most significant one and comprises scanning error, recognition error, decision-making error, and satisfaction of search. Tumor characteristics such as lesion size, conspicuity, and location are also crucial in this context. Even technical aspects can contribute to the probability of skipping lung cancer, including image quality and patient positioning and movement. Albeit it is hard to remove missed lung cancer completely, strategies to reduce observer error and methods to improve technique and automated detection may be valuable in reducing its likelihood.