A B S T R A C T PurposeRecent screening trial results indicate that low-dose computed tomography (LDCT) reduces lung cancer mortality in high-risk patients. However, high false-positive rates, costs, and potential harms highlight the need for complementary biomarkers. The diagnostic performance of a noninvasive plasma microRNA signature classifier (MSC) was retrospectively evaluated in samples prospectively collected from smokers within the randomized Multicenter Italian Lung Detection (MILD) trial. Patients and MethodsPlasma samples from 939 participants, including 69 patients with lung cancer and 870 disease-free individuals (n ϭ 652, LDCT arm; n ϭ 287, observation arm) were analyzed by using a quantitative reverse transcriptase polymerase chain reaction-based assay for MSC. Diagnostic performance of MSC was evaluated in a blinded validation study that used prespecified risk groups. ResultsThe diagnostic performance of MSC for lung cancer detection was 87% for sensitivity and 81% for specificity across both arms, and 88% and 80%, respectively, in the LDCT arm. For all patients, MSC had a negative predictive value of 99% and 99.86% for detection and death as a result of disease, respectively. LDCT had sensitivity of 79% and specificity of 81% with a false-positive rate of 19.4%. Diagnostic performance of MSC was confirmed by time dependency analysis. Combination of both MSC and LDCT resulted in a five-fold reduction of LDCT false-positive rate to 3.7%. MSC risk groups were significantly associated with survival ( 1 2 ϭ 49.53; P Ͻ .001). ConclusionThis large validation study indicates that MSC has predictive, diagnostic, and prognostic value and could reduce the false-positive rate of LDCT, thus improving the efficacy of lung cancer screening.

Background The National Lung Screening Trial showed that lung cancer (LC) screening by three annual rounds of low-dose computed tomography (LDCT) reduces LC mortality. We evaluated the benefit of prolonged LDCT screening beyond 5 years, and its impact on overall and LC specific mortality at 10 years. Design The Multicentric Italian Lung Detection (MILD) trial prospectively randomized 4099 participants, to a screening arm ( n = 2376), with further randomization to annual ( n = 1190) or biennial ( n = 1186) LDCT for a median period of 6 years, or control arm ( n = 1723) without intervention. Between 2005 and 2018, 39 293 person-years of follow-up were accumulated. The primary outcomes were 10-year overall and LC specific mortality. Landmark analysis was used to test the long-term effect of LC screening, beyond 5 years by exclusion of LCs and deaths that occurred in the first 5 years. Results The LDCT arm showed a 39% reduced risk of LC mortality at 10 years [hazard ratio (HR) 0.61; 95% confidence interval (CI) 0.39–0.95], compared with control arm, and a 20% reduction of overall mortality (HR 0.80; 95% CI 0.62–1.03). LDCT benefit improved beyond the 5th year of screening, with a 58% reduced risk of LC mortality (HR 0.42; 95% CI 0.22–0.79), and 32% reduction of overall mortality (HR 0.68; 95% CI 0.49–0.94). Conclusions The MILD trial provides additional evidence that prolonged screening beyond 5 years can enhance the benefit of early detection and achieve a greater overall and LC mortality reduction compared with NLST trial. ClinicalTrials.gov identifier NCT02837809.

HCC nodules 3.5-8.5 cm in diameter can be ablated in one or two RF sessions after occlusion of the tumor arterial supply.

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.

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.