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To test reproducibility and predictive value of a simplified score for assessment of extraprostatic tumor extension (sEPE grade).Sixty-five patients (mean age ± SD, 67 years ± 6.3) treated with radical prostatectomy for prostate cancer who underwent 1.5-Tesla multiparametric magnetic resonance imaging (mpMRI) 6 months before surgery were enrolled. sEPE grade was derived from mpMRI metrics: curvilinear contact length15 mm (CCL) and capsular bulging/irregularity. The diameter of the index lesion (dIL) was also measured. Evaluations were independently performed by seven radiologists, and inter-reader agreement was tested by weighted Cohen K coefficient. A nested (two levels) Monte Carlo cross-validation was used. The best cut-off value for dIL was selected by means of the Youden J index to classify values into a binary variable termed dIL*. Logistic regression models based on sEPE grade, dIL, and clinical scores were developed to predict pathologic EPE. Results on validation set were assessed by the main metrics of the receiver operating characteristics curve (ROC) and by decision curve analysis (DCA). Based on our findings, we defined and tested an alternative sEPE grade formulation.Pathologic EPE was found in 31/65 (48%) patients. Average κsEPE grade is reproducible and when combined with the dIL* accurately predicts extraprostatic tumor extension.• Simple and reproducible mpMRI semi-quantitative scoring system for extraprostatic tumor extension. • sEPE grade accurately predicts extraprostatic tumor extension regardless of reader expertise. • Accurate pre-operative staging and risk stratification for optimized patient management.

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

Purpose We investigated whether the additional use of grey-scale inversion technique improves the interpretation of eight chest abnormalities, in terms of diagnostic performance and interobserver variability. Material and methods A total of 507 patients who underwent a chest computed tomography (CT) examination and a chest radiography (CXR) within 24 h were enrolled. CT was the standard of reference. Images were retrospectively reviewed for the presence of atelectasis, consolidation, interstitial abnormality, nodule, mass, pleural effusion, pneumothorax and rib fractures. Four CXR reading settings, involving 3 readers were organized: only standard; only inverted; standard followed by inverted; and inverted followed by standard. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy, assessed with the area under the curve (AUC), and their 95% confidence interval were calculated for each reader and setting. Interobserver agreement was tested by Cohen’s K test with quadratic weights (Kw) and its 95%CI. Results CXR sensitivity % for any finding was 35.1 (95% CI: 33 to 37) for setting 1, 35.9 (95% CI: 33 to 37), for setting 2, 32.59 (95% CI: 30 to 34) for setting 3, and 35.56 (95% CI: 33 to 37) for setting 4; specificity % 93.78 (95% CI: 91 to 95), 93.92 (95% CI: 91 to 95), 94.43 (95% CI: 92 to 96), 93.86 (95% CI: 91 to 95); PPV % 56.22 (95% CI: 54.2 to 58.2), 56.49 (95% CI: 54.5 to 58.5), 57.15 (95% CI: 55 to 59), 56.75 (95% CI: 54 to 58); NPV % 85.66 (95% CI: 83 to 87), 85.74 (95% CI: 83 to 87), 85.29 (95% CI: 83 to 87), 85.73 (95% CI: 83 to 87); AUC values 0.64 (95% CI: 0.62 to 0.66), 0.65 (95% CI: 0.63 to 0.67), 0.64 (95% CI: 0.62 to 0.66), 0.65 (95% CI: 0.63 to 0.67); Kw values 0.42 (95% CI: 0.4 to 0.44), 0.40 (95% CI: 0.38 to 0.42), 0.42 (95% CI: 0.4 to 0.44), 0.41 (95% CI: 0.39 to 0.43) for settings 1, 2, 3 and 4, respectively. Conclusions No significant advantages were observed in the use of grey-scale inversion technique neither over standard display mode nor in combination at the detection of eight chest abnormalities.

Background: Transthoracic strain elastosonography (TSE) is being increasingly studied for estimating lung-pleura interface stiffness in pulmonary fibrosis. To date, no data exist on its application in chronic obstructive pulmonary disease (COPD). Objectives: The aim of this article was to describe the TSE pattern in patients with COPD and healthy subjects, either smokers or nonsmokers, and evaluate the feasibility of this technique for early detection of COPD in smokers. Methods: Nineteen patients with COPD, twenty-one healthy smokers, and twenty healthy nonsmokers underwent spirometry and TSE. Elastosonography was performed by one ultrasound-certified operator on 12 different scans for each participant, on right and left sides, anteriorly and posteriorly, on upper and lower lobes. For each scan, lung-pleura interface stiffness index (SI) was calculated, and the average SI on all 12 scans (SI-12) and on posterior basal scans (SI-PB) was calculated and used for comparisons among groups of participants and correlations with spirometric parameters. Results: Patients with lung injury (i.e., with COPD or healthy smokers) exhibited significantly increased lung-pleura interface stiffness on TSE, measured by SI-12 and SI-PB, than healthy nonsmokers (p < 0.05). Unlike SI-12, SI-PB was able to discriminate between subjects with lung injury and healthy nonsmokers on receiver operating characteristics analysis (area under the curve 0.846, 95% confidence interval 0.730–0.926, p < 0.001) and correlated with forced expiratory volume in the first second (r = −0.31, p = 0.018). Conclusion: The measurement of lung-pleura interface stiffness by TSE in posterior basal scans was able to discriminate patients with lung injury from healthy nonsmokers. The role of TSE for detecting early lung damage in COPD should be further investigated.

Background Pneumonia is the main manifestation of coronavirus disease 2019 (COVID-19) infection. Chest computed tomography is recommended for the initial evaluation of the disease; this technique can also be helpful to monitor the disease progression and evaluate the therapeutic efficacy. Aim To review the currently available literature regarding the radiological follow-up of COVID-19-related lung alterations using the computed tomography scan, to describe the evidence about the dynamic evolution of COVID-19 pneumonia and verify the potential usefulness of the radiological follow-up. Methods We used pertinent keywords on PubMed to select relevant studies; the articles we considered were published until October 30, 2020. Through this selection, 69 studies were identified, and 16 were finally included in the review. Results Summarizing the included works' findings, we identified well-defined stages in the short follow-up time frame. A radiographic deterioration reaching a peak roughly within the first 2 wk; after the peak, an absorption process and repairing signs are observed. At later radiological follow-up, with the limitation of little evidence available, the lesions usually did not recover completely. Conclusion Following computed tomography scan evolution over time could help physicians better understand the clinical impact of COVID-19 pneumonia and manage the possible sequelae; a longer follow-up is advisable to verify the complete resolution or the presence of long-term damage.

Purpose of review To discuss rationale and methods for determining progressive lung fibrosis on thoracic computed tomography (CT) and describe limitations and challenges. Recent findings Identifying patients with progressive lung fibrosis is important to determine optimal treatment. Serial high-resolution computed tomography is a method of determining disease progression. A number of studies are reviewed in this article, that have explored various parameters (both visual and automated) that signify progressive fibrosis on CT. Summary To reliably use serial CT as a marker of disease progression in fibrotic lung disease, clinicians and radiologists need to be aware of the optimal methods for identifying changes in disease extent, and understand their limitations.

Coronary artery calcium (CAC) is a known risk factor for cardiovascular events, but not yet routinely evaluated in Low Dose Computed Tomography (LDCT) screening. The present analysis compared the accuracy of a new automated CAC quantification versus prior manual quantification on baseline LDCT screening images as predictors of all-cause mortality at 12 years.The study included 1129 volunteers of the Multicentric Italian Lung Detection (MILD) trial who underwent a baseline LDCT scan from September 2005 to September 2006, already analyzed in a previous paper on CAC scoring. The initial manual CAC (mCAC) had been scored by one operator using a dedicated software, while the new automated CAC (aCAC) score was measured by a fully automated artificial intelligence software. All CAC scores were stratified in four categories: 0, 0.1- 19.9, 20-399, and ≥ 400.The study showed a high correlation between aCAC and mCAC scores, with an Intraclass Correlation Coefficient of 0.887. Of 613 negative mCAC score, 87.6% had aCAC score >0, and 14.0% >20. A CAC score >20 revealed a higher risk of 12-year all-cause mortality both with mCAC and aCAC. Focusing on the 535 individuals with false negative mCAC score, aCAC identified a subset of volunteers with a significantly poorer survival of 86% (aCAC 20-399, p=0.0007).CAC quantification could be accurately and safely performed with a fully automated software on baseline LDCT screening images to predict all-cause mortality risk.

PURPOSE: This study aimed to evaluate a potential relationship between the diffusing capacity of the lung for carbon monoxide(DLCO) and the aggressiveness of lung adenocarcinoma(ADC).METHODS: Patients who underwent radical surgery for lung ADC between 2001 and 2018 were retrospectively reviewed. DLCO values were dichotomized into DLCOlow(normal(≥80%).Relationships between DLCO and ADC histopathological features, clinical features, as well as with overall survival (OS), were evaluated. RESULTS: Four hundred and sixty patients were enrolled, of which 193(42%) were included in the DLCOlow group. DLCOlow was associated with smoking status, low FEV1, micropapillary and solid ADC, tumour grade 3,high tumour lymphoid infiltrate and presence of tumour desmoplasia. In addition, DLCO values were higher in low-grade ADC and progressively decreased in intermediate and high-grade ADC(p=0.024). After adjusting for clinical variables, at multivariable logistic regression analysis, DLCOlow still showed a significant correlation with high lymphoid infiltrate(p=0.017), presence of desmoplasia(p=0.065), tumour grade 3(p=0.062),micropapillary and solid ADC subtypes(p=0.008).To exclude the association between non-smokers and well-differentiated ADC, the relationship between DLCO and histopathological ADC patterns was confirmed in the subset of 377 former and current smokers(p=0.021).At univariate analysis, gender, DLCO, FEV1, ADC histotype, tumor grade, stage, pleural invasion, tumour necrosis, tumour desmoplasia, lymphatic and blood invasion were significantly related with OS. At multivariate analysis, only gender(pCONCLUSIONS: We found a relationship between DLCO and ADC patterns as well as with tumour grade, tumour lymphoid infiltrate and desmoplasia, suggesting that lung damage may be associated with tumour aggressiveness.

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

Benign metastasizing leiomyoma (BML) is a rare disease characterized by extrauterine benign leiomyomatosis in patients with a previous or concomitant history of uterine leiomyoma. Currently, there are no specific criteria to predict the metastasizing ability of the uterine leiomyoma and the risk of malignant degeneration of pulmonary BML, and these are the aims of this study. We analyzed 10 uterine (three leiomyomas, four leiomyomas that gave rise to lung BML, three healthy tissues) and 11 pulmonary tissue samples (eight lung BML, three healthy tissues). Interestingly, one of the BML lesions exceptionally evolved into a leiomyosarcoma (case 2). Uterine leiomyoma microvascular density (MVD) was higher in the patients with uterine leiomyomas that gave rise to lung BML, reaching a peak in case 2. Strong positivity for the estrogen (ER) and progesterone (PR) receptors and a low proliferation index (Ki67

Introduction: Interstitial lung disease (ILD) is the main cause of mortality in systemic sclerosis (SSc). Methods stratifying the prognosis of SSc-ILD are lacking in clinical practice. The quantifi...

Cytisine, a partial agonist-binding nicotine acetylcholine receptor, is a promising cessation intervention. We conducted a single-center, randomized, controlled trial (RCT) in Italy to assess the efficacy and tolerability of cytisine as a smoking cessation therapy among lung cancer screening participants.From July 2019 to March 2020, the Screening and Multiple Intervention on Lung Epidemics RCT enrolled 869 current heavy tobacco users in a low-dose computed tomography screening program, with a randomized comparison of pharmacologic intervention with cytisine plus counseling (N = 470) versus counseling alone (N = 399). The primary outcome was continuous smoking abstinence at 12 months, biochemically verified through carbon monoxide measurement.At the 12-month follow-up, the quit rate was 32.1% (151 participants) in the intervention arm and 7.3% (29 participants) in the control arm. The adjusted OR of continuous abstinence was 7.2 (95% confidence interval: 4.6-11.2). Self-reported adverse events occurred more frequently in the intervention arm (399 events among 196 participants) than in the control arm (230 events among 133 participants, p0.01). The most common adverse events were gastrointestinal symptoms, comprising abdominal swelling, gastritis, and constipation.The efficacy and safety observed in the Screening and Multiple Intervention on Lung Epidemics RCT indicate that cytisine, a very low-cost medication, is a useful treatment option for smoking cessation and a feasible strategy to improve low-dose computed tomography screening outcomes with a potential benefit for all-cause mortality.

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

There are no robust data on the real onset of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and spread in the prepandemic period worldwide. We investigated the presence of SARS-CoV-2 receptor-binding domain (RBD)–specific antibodies in blood samples of 959 asymptomatic individuals enrolled in a prospective lung cancer screening trial between September 2019 and March 2020 to track the date of onset, frequency, and temporal and geographic variations across the Italian regions. SARS-CoV-2 RBD-specific antibodies were detected in 111 of 959 (11.6%) individuals, starting from September 2019 (14%), with a cluster of positive cases (>30%) in the second week of February 2020 and the highest number (53.2%) in Lombardy. This study shows an unexpected very early circulation of SARS-CoV-2 among asymptomatic individuals in Italy several months before the first patient was identified, and clarifies the onset and spread of the coronavirus disease 2019 (COVID-19) pandemic. Finding SARS-CoV-2 antibodies in asymptomatic people before the COVID-19 outbreak in Italy may reshape the history of pandemic.

Background To determine if an increased epicardial fat volume (EFV) is associated with coronary artery disease (CAD) in individuals with symptoms of cardiovascular (CV) disease. Methods Coronary Computed Tomographic Angiography (CCTA), demographic and clinical variables of 1344 individuals were retrieved: semi-automated measurements for EFV and coronary artery calcifications (CAC) were obtained. Individuals were grouped into three categories according to the presence of CAD, resulting in absent (CAD0), non-obstructive (CAD1) or obstructive (CAD2) disease-groups. Relation of EFV with CAD was assessed with two approaches: 1) presence of any CAD; 2) each individual CAD category. Results Median EFV was 90.52 ml (range 11.27–442.21 ml); median CAC was 56.5 (range 0–10,144); 848 individuals (63.1%) were categorized as CAD0, 326 (24.3%) as CAD1, 170 (12.6%) as CAD2. EFV was lower in subjects without CAC (EFVmedian = 66.5 ml), as compared to those with CAC 0.1–100 (EFVmedian = 91.47), CAC 100.1–400 (EFVmedian = 97.46) and CAC >400 (EFVmedian = 109.48) (p EFV was lower in CAD0 (EFVmedian = 87.21 ml), as compared to CAD1 (EFVmedian = 93.89 ml) and CAD2 (EFVmedian = 102.98 ml) individuals (p A logistic regression model built by including demographic and clinical variables showed inconsistent predictive value of EFV for either CAD1 or CAD2 (p > 0.05). Conclusions In the setting of symptomatic individuals, an increased amount of epicardial fat was associated with larger amount of coronary artery calcifications and was observed in individuals with obstructive CAD, however without predictive value to confidently determine CAD presence and severity.

We present three patients affected by pulmonary squamous cell carcinoma, metastatic esophageal cancer and advanced non-Hodgkin lymphoma, who incurred in coronavirus 2019 (COVID-19) infection during the early phase of epidemic wave in Italy. All patients presented with fever. Social contact with subject positive for COVID-19 was declared in only one of the three cases. In all cases, laboratory findings showed lymphopenia and elevated C-reactive protein (CRP). Chest x-ray and computed tomography showed bilateral ground-glass opacities, shadowing, interstitial abnormalities, and "crazy paving" pattern which evolved with superimposition of consolidations in one patient. All patients received antiviral therapy based on ritonavir and lopinavir, associated with hydroxychloroquine. Despite treatment, two patients with advanced cancers died after 39 and 17 days of hospitalization, while the patient with lung cancer was dismissed at home, in good conditions.

Qualitative and quantitative CT imaging features might intercept the multifaceted tumor immune microenvironment (TIME), providing a non-invasive approach to design new prognostic models in NSCLC patients.Our study population consisted of 100 surgically resected NSCLC patients among which 31 served as a validation cohort for quantitative image analysis. TIME was classified according to PD-L1 expression and the magnitude of Tumor Infiltrating Lymphocytes (TILs) and further defined as hot or cold by the tissue analysis of effector (CD8-to-CD3Specific CT-SFs (texture [TXT], effect [EFC] and margins [MRG]) strongly correlated to PD-L1 and TILs status and showed significant impact on survival outcome (TXT, HR:3.39, 95 % CI 1.12-10-27, P 0.05; EFC, HR:0.41, 95 % CI 0.18-0.93, P 0.05; MRG, HR:1.93, 95 % CI 0.88-4.25, P = 0.09). Seven CT derived radiomic features were able to sharply discriminate cases with hot (inflamed) vs cold (desert) TIME, which also exhibited opposite OS (long vs short, HR:0.09, 95 % CI 0.04-0.23, P 0.001) and DFS (long vs short, HR:0.31, 95 % CI 0.16-0.58, P 0.001). Moreover, we identified 6 prognostic radiomic features among which ClusterProminence displayed the highest statistical significance (HR:0.13, 95 % CI 0.06-0.31, P 0.001). These findings were independently validated in an additional cohort of NSCLC (HR:0.11, 95 % CI 0.03-0.40, P = 0.001). Finally, in our training cohort we developed a multiparametric prognostic model, interlacing TIME and clinico-pathological characteristics with CT-SFs (ROC curve AUC:0.83, 95 % CI 0.71-0.92, P 0.001) or CT-RFs (AUC: 0.91, 95 % CI 0.83-0.99, P 0.001), which appeared to outperform pTNM staging (AUC: 0.66, 95 % CI 0.51-0.80, P 0.05) in the risk assessment of NSCLC.Higher order CT extracted features associated with specific TIME profiles may reveal a radio-immune signature with prognostic impact on resected NSCLC.

To evaluate the incidence and clinical significance of pneumothorax (PTX) and pulmonary hemorrhage (PH) after CT-guided lung biopsy (CT-LB). To test correlations of PTX and chest tube insertion (CTI) with PH and other imaging and procedural parameters. Pre-procedural CT and CT-LB scans of 904 patients were examined. Incidence of PTX and PH and PH location (type-1 along needle track; type-2 perilesional) and severity according to its thickness (low grade 6 mm) were recorded. PTX was considered clinically significant if treated with CTI, PH if treated with endoscopic/endovascular procedure. Binary logistic regression analyses were used to determine the effects of different imaging and procedural parameters on the likelihood to develop PTX, CTI and PH and to define their correlation. PTX occurred in 306/904 cases (33.8%); CTI was required in 18/306 (5.9%). PH occurred in 296/904 cases (32.7%), and no case required treatment. Nodule-to-pleura distance (ORPTX = 1.052; ORCTI = 1.046; ORPH 1.077), emphysema (ORPTX = 1.287; ORPH = 0.573), procedure time (ORPTX = 1.019; ORCTI = 1.039; ORPH = 1.019), target size (ORPTX = 0.982; ORPH = 0.968) and needle gauge (ORPTX = 0.487; ORCTI = 4.311; ORPH = 2.070) showed statistically significant correlation to PTX, CTI and PH. Type-1 PH showed a protective effect against PTX and CTI (ORPTX = 0.503; ORCTI = 0.416). PTX and PH have similar incidence after CT-guided lung biopsy. PH along needle track may represent a protective factor against development of PTX and against PTX requiring CTI.

Purpose Bronchiectasis is a chronic disease characterized by an irreversible dilatation of bronchi leading to chronic infection, airway inflammation, and progressive lung damage. Three specific patterns of bronchiectasis are distinguished in clinical practice: cylindrical, varicose, and cystic. The predominance and the extension of the type of bronchiectasis provide important clinical information. However, characterization is often challenging and is subject to high interobserver variability. The aim of this study is to provide an automatic tool for the detection and classification of bronchiectasis through convolutional neural networks. Materials and methods Two distinct approaches were adopted: (i) direct network performing a multilabel classification of 32×32 regions of interest (ROIs) into 4 classes: healthy, cylindrical, cystic, and varicose and (ii) a 2-network serial approach, where the first network performed a binary classification between normal tissue and bronchiectasis and the second one classified the ROIs containing abnormal bronchi into one of the 3 bronchiectasis typologies. Performances of the networks were compared with other architectures presented in the literature. Results Computed tomography from healthy individuals (n=9, age=47±6, FEV1%pred=109±17, FVC%pred=116±17) and bronchiectasis patients (n=21, age=59±15, FEV1%pred=74±25, FVC%pred=91±22) were collected. A total of 19,059 manually selected ROIs were used for training and testing. The serial approach provided the best results with an accuracy and F1 score average of 0.84, respectively. Slightly lower performances were observed for the direct network (accuracy=0.81 and F1 score average=0.82). On the test set, cylindrical bronchiectasis was the subtype classified with highest accuracy, while most of the misclassifications were related to the varicose pattern, mainly to the cylindrical class. Conclusion The developed networks accurately detect and classify bronchiectasis disease, allowing to collect quantitative information regarding the radiologic severity and the topographical distribution of bronchiectasis subtype.

Lung cancer (LC) is currently one of the main causes of cancer-related deaths worldwide. Low-dose computed tomography (LDCT) of the chest has been proven effective in secondary prevention (i.e., early detection) of LC by several trials. In this work, we investigated the potential impact of radiomics on indeterminate prevalent pulmonary nodule (PN) characterization and risk stratification in subjects undergoing LDCT-based LC screening. As a proof-of-concept for radiomic analyses, the first aim of our study was to assess whether indeterminate PNs could be automatically classified by an LDCT radiomic classifier as solid or sub-solid (first-level classification), and in particular for sub-solid lesions, as non-solid versus part-solid (second-level classification). The second aim of the study was to assess whether an LCDT radiomic classifier could automatically predict PN risk of malignancy, and thus optimize LDCT recall timing in screening programs. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, positive predictive value, negative predictive value, sensitivity, and specificity. The experimental results showed that an LDCT radiomic machine learning classifier can achieve excellent performance for characterization of screen-detected PNs (mean AUC of 0.89 ± 0.02 and 0.80 ± 0.18 on the blinded test dataset for the first-level and second-level classifiers, respectively), providing quantitative information to support clinical management. Our study showed that a radiomic classifier could be used to optimize LDCT recall for indeterminate PNs. According to the performance of such a classifier on the blinded test dataset, within the first 6 months, 46% of the malignant PNs and 38% of the benign ones were identified, improving early detection of LC by doubling the current detection rate of malignant nodules from 23% to 46% at a low cost of false positives. In conclusion, we showed the high potential of LDCT-based radiomics for improving the characterization and optimizing screening recall intervals of indeterminate PNs.

Background: Coronavirus disease (COVID-19), an acute respiratory syndrome caused by a novel severe acute respiratory syndrome coronavirus (SARS-CoV-2), has rapidly spread worldwide, significantly affecting the outcome of a highly vulnerable group such as cancer patients. The aim of the present study was to evaluate the clinical impact of COVID-19 infection on outcome and oncologic treatment of cancer patients. Patient and methods: We retrospectively enrolled cancer patients with laboratory and/or radiologic confirmed SARS-CoV-2 infection, admitted to our center from February to April 2020. Descriptive statistics were used to summarize the clinical data and univariate analyses were performed to investigate the impact of anticancer treatment modifications due to COVID-19 outbreak on the short-term overall survival (OS). Results: Among 61 patients enrolled, 49 (80%) were undergoing anticancer treatment and 41 (67%) had metastatic disease. Most patients were men; median age was 68 years. Median OS was 46.6 days (40% of deaths occurred within 20 days from COVID-19 diagnosis). Among 59 patients with available data on therapeutic course, 46 experienced consequences on their anticancer treatment schedule. Interruption or a starting failure of the oncologic therapy correlated with significant shorter OS. Anticancer treatment delays did not negatively affect the OS. Lymphocytopenia development after COVID was significantly associated with worst outcome. Conclusions: COVID-19 diagnosis in cancer patients may affect their short-term OS, especially in case of interruption/starting failure of cancer therapy. Maintaining/delaying cancer therapy seems not to influence the outcome in selected patients with recent COVID-19 diagnosis.

To retrospectively compare semi-qualitative and quantitative CT pulmonary angiography (CTPAs) image metrics testing diagnostic performance between protocols performed by 20 or 40 ml of contrast medium (CM) in patients with suspected pulmonary embolism (PE).A total of 102 CTPAs performed by 20 ml (ultra-low volume: ULV) and 74 CTPAs performed by 40 ml (low volume: LV) protocol for the diagnosis of clinically suspected PE performed between October 2012 and September 2013 were retrieved. High-concentration CM (Iomeprol 400 mgI/ml) was injected at 3 ml/s (iodine delivery rate 1.2 mgI/s). Two radiologists (blinded and independent) semi-qualitatively scored vascular enhancement and image noise according to a five-point visual scoring system. Quantitative analysis was performed by regions of interest quantifying densitometric parameters, such as central and peripheral pulmonary arteries vascular contrast enhancement (CE, threshold for diagnostic CE ≥ 250 HU), and metrics for image noise. Continuous variables were compared by the Student's t test between groups if normally distributed while categorical variables were analyzed with the Chi-squared test. Interobserver agreement was calculated by the weighted kappa test; correlation coefficients were calculated using Pearson's correlation tests.The semi-qualitative scores for central and peripheral pulmonary arteries vascular CE were sufficient by ULV, yet inferior than LV (p 0.001). Semi-qualitative image noise was comparable between ULV and LV, and the interobserver agreement was only fair for quality of peripheral vessels. Agreement on nondiagnostic semi-qualitative parameters was seen in 9/102 (8.8%) ULV CTPAs, in particular associated with massive PE (2/9), pleuro-pulmonary abnormalities (5/9) or without major abnormalities (2/9). Quantitative analysis showed that mean CE was lower in ULV group (p 0.001), though greater than the diagnostic threshold of 250 HU in both groups.Diagnostic vascular CE ( 250 HU) was obtained in both 20 ml and 40 ml CTPAs. CTPA by 20 ml of CM rendered diagnostic CE for the assessment of pulmonary arteries in patients with clinical suspicion of acute PE. Decreased image quality was mostly associated with massive PE or concomitant pleuro-parenchymal abnormalities.

BACKGROUND: To reduce the radiation exposure from chest computed tomography (CT), ultralow-dose CT (ULDCT) protocols performed at sub-millisievert levels were previously tested for the evaluation of pulmonary nodules (PNs). The purpose of our study was to investigate the effect of ULDCT and iterative image reconstruction on volumetric measurements of solid PNs. METHODS: CT datasets of an anthropomorphic chest phantom containing solid microspheres were obtained with a third-generation dual-source CT at standard dose, 1/8th, 1/20th and 1/70th of standard dose [CT volume dose index (CTDI(vol)): 0.03–2.03 mGy]. Semi-automated volumetric measurements were performed on CT datasets reconstructed with filtered back projection (FBP) and advanced modelled iterative reconstruction (ADMIRE), at strength level 3 and 5. Absolute percentage error (APE) evaluated measurement accuracy related to the effective volume. Scan repetition differences were evaluated using Bland-Altman analysis. Two-way analysis of variance (ANOVA) assessed influence of different scan parameters on APE. Proportional differences (PDs) tested the effect of dose settings and reconstruction algorithms on volumetric measurements, as compared to the standard protocol (standard dose-FBP). RESULTS: Bland-Altman analysis revealed small mean interscan differences of APE with narrow limits of agreement (–0.1%±4.3% to –0.3%±3.8%). Dose settings (P

The concept of tumor spread through air spaces (STAS) in lung adenocarcinoma was officially recognized as a pattern of invasion in 2015 World Health Organization (WHO) classification . The STAS is defined as the presence of “micropapillary clusters, solid nests, or single cells beyond the edge of the tumor into air spaces in the surrounding lung parenchyma” within the surgical specimen ( Figure 1 ), which implies a post - surgical diagnosis (1) .

Lung nodules are frequent findings in chest computed tomography (CT) in patients with metastatic melanoma. In this study, we assessed the frequency and compared morphologic differences of metastases and benign nodules. We retrospectively evaluated 85 patients with melanoma (AJCC stage III or IV). Inclusion criteria were ≤20 lung nodules and follow-up using CT ≥183 days after baseline. Lung nodules were evaluated for size and morphology. Nodules with significant growth, nodule regression in line with RECIST assessment or histologic confirmation were judged to be metastases. A total of 438 lung nodules were evaluated, of which 68% were metastases. At least one metastasis was found in 78% of patients. A 10 mm diameter cut-off (used for RECIST) showed a specificity of 95% and a sensitivity of 20% for diagnosing metastases. Central location (n = 122) was more common in metastatic nodules (p = 0.009). Subsolid morphology (n = 53) was more frequent (p < 0.001), and calcifications (n = 13) were solely found in non-metastatic lung nodules (p < 0.001). Our data show that lung nodules are prevalent in about two-thirds of melanoma patients (AJCC stage III/IV) and the majority are metastases. Even though we found a few morphologic indicators for metastatic or non-metastatic lung nodules, morphology has limited value to predict the presence of lung metastases.

Non-cystic fibrosis bronchiectasis represents a heterogenous spectrum of disorders characterised by an abnormal and permanent dilatation of the bronchial tree associated with respiratory symptoms. To date, diagnosis relies on computed tomography (CT) evidence of dilated airways. Nevertheless, definite radiological criteria and standardised CT protocols are still to be defined. Although largely used, current radiological scoring systems have shown substantial drawbacks, mostly failing to correlate morphological abnormalities with clinical and prognostic data. In limited cases, bronchiectasis morphology and distribution, along with associated CT features, enable radiologists to confidently suggest an underlying cause. Quantitative imaging analyses have shown a potential to overcome the limitations of the current radiological criteria, but their application is still limited to a research setting. In the present review, we discuss the role of imaging and its current limitations in non-cystic fibrosis bronchiectasis. The potential of automatic quantitative approaches and artificial intelligence in such a context will be also mentioned.

Purpose To test radiomics for prognostication of intrahepatic mass-forming cholangiocarcinoma (IMCC) and to develop a comprehensive risk model. Methods Histologically proven IMCC (representing the full range of stages) were retrospectively analyzed by volume segmentation on baseline hepatic venous phase computed tomography (CT), by two readers with different experience (R1 and R2). Morphological CT features included: tumor size, hepatic satellite lesions, lymph node and distant metastases. Radiomic features (RF) were compared across CT protocols and readers. Univariate analysis against overall survival (OS) warranted ranking and selection of RF into radiomic signature (RSign), which was dichotomized into high and low-risk strata (RSign*). Models without and with RSign* (Model 1 and 2, respectively) were compared. Results Among 78 patients (median follow-up 262 days, IQR 73–957), 62/78 (79%) died during the study period, 46/78 (59%) died within 1 year. Up to 10% RF showed variability across CT protocols; 37/108 (34%) RF showed variability due to manual segmentation. RSign stratified OS (univariate: HR 1.37 for R1, HR 1.28 for R2), RSign* was different between readers (R1 0.39; R2 0.57). Model 1 showed AUC 0.71, which increased in Model 2: AUC 0.81 (p p = 0.001) and AIC 90.2 for R2. Conclusion The use of RF into a unified RSign score stratified OS in patients with IMCC. Dichotomized RSign* classified survival strata, its inclusion in risk models showed adjunct yield. The cut-off value of RSign* was different between readers, suggesting that the use of reference values is hampered by interobserver variability.

Radiomics has emerged as a noninvasive tool endowed with the potential to intercept tumor characteristics thereby predicting clinical outcome. In a recent study on resected non-small cell lung cancer (NSCLC), we identified highly prognostic computed tomography (CT) -derived radiomic features (RFs), which in turn were able to discriminate hot from cold tumor immune microenvironment (TIME). We aimed at validating a radiomic model capable of dissecting specific TIME profiles bearing prognostic power in resected NSCLC. The validation cohort included 31 radically resected NSCLCs clinicopathologically matched with the training set (n = 69). TIME was classified in hot and cold according to a multiparametric immunohistochemical analysis involving PD-L1 score and incidence of immune effector phenotypes among tumor infiltrating lymphocytes (TILs). High- throughput radiomic features (n = 841) extracted from CT images were correlated to TIME parameters to ultimately define prognostic classes. We confirmed PD-1 to CD8 ratio as best predictor of clinical outcome among TIME characteristics. Significantly prolonged overall survival (OS) was observed in patients carrying hot (median OS not reached) vs cold (median OS 22 months; hazard ratio 0.28, 95% confidence interval 0.09 -0.82; p = 0.015) immune background, thus validating the prognostic impact of these two TIME categories in resected NSCLC. Importantly, in the validation setting, three out of eight previously identified RFs sharply distinguishing hot from cold TIME were endorsed. Among signature-related RFs, Wavelet-HHH_gldm_HighGrayLevelEmphasis highly performed as descriptor of hot immune contexture (area under the receiver operating characteristic curve 0.94, 95% confidence interval 0.81 -1.00; p = 0.01). Based on our findings, Radiomics may decipher specific TIME profiles providing a noninvasive prognostic approach in resected NSCLC and an exploitable predictive strategy in advanced cases.