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There is growing evidence that inflammatory, immunologic, and metabolic status is associated with cancer patients survival. Here, we built a simple algorithm to predict lung cancer outcome. Perioperative routine blood tests (RBT) of a cohort of patients with resectable primary lung cancer (LC) were analysed. Inflammatory, immunologic, and metabolic profiles were used to create a single algorithm (RBT index) predicting LC survival. A concurrent cohort of patients with resectable lung metastases (LM) was used to validate the RBT index. Charts of 2088 consecutive LC and 1129 LM patients undergoing lung resection were evaluated. Among RBT parameters, C-reactive protein (CRP), lymphocytes, neutrophils, hemoglobin, albumin and glycemia independently correlated with survival, and were used to build the RBT index. Patients with a high RBT index had a higher 5-year mortality than low RBT patients (adjusted HR 1.93, 95% CI 1.62–2.31). High RBT patients also showed a fourfold higher risk of 30-day postoperative mortality (2.3% vs. 0.5%, p 0.0019). The LM analysis validated the results of the LC cohort. We developed a simple and easily available multifunctional tool predicting short-term and long-term survival of curatively resected LC and LM. Prospective external validation of RBT index is warranted.

Background: Large randomized trials have demonstrated that lung cancer (LC) screening with low-dose computed tomography (LDCT) reduces LC mortality in heavy smokers. We previously showed in the MILD screening trial that the combination of a prespecified circulating microRNA (miRNA) signature classifier (MSC) and LDCT improves the accuracy of LDCT alone. The primary aim of the prospective BioMILD study was to assess the additional value of the blood MSC assay at the time of baseline LDCT with the goal of personalizing LC screening intervals. Patients and methods: The study enrolled 4119 volunteers from January 2013 to March 2016, with a median follow-up of 5.3 years. Baseline LDCT and miRNAs stratified participants into four groups: CT−/MSC− (n = 2664; 64.7%); CT−/MSC+ (n = 800; 19.4%); CT+/MSC− (n = 446; 10.8%); and CT+/MSC+ (n = 209; 5.1%). As per the protocol, those in the CT−/MSC− and CT−/MSC+ groups were allocated to LDCT repeat at 3-year and 1-year intervals; CT+ participants were allocated for 1-year or earlier intervals on the basis of LDCT features independent of MSC results. Results: CT+ participants had a 15.8-fold higher 4-year LC incidence than CT− participants (95% confidence interval 10.34-24.05), and MSC+ participants had a 2.0-fold higher 4-year LC incidence than MSC− participants (95% confidence interval 1.40-2.90); there was no evidence that the MSC effect differed between CT+ and CT− participants. LC incidence at 4 years was 0.8% in CT−/MSC−, 1.1% in CT−/MSC+, 10.8% in CT+/MSC−, and 20.1% in CT+/MSC+ participants. LC mortality rates at 5 years in the four risk groups were 0.5 in CT−/MSC−, 1.5 in CT−/MSC+, 4.2 in CT+/MSC−, and 10.1 in CT+/MSC+. Conclusion: The combined use of LDCT and blood miRNAs at baseline predicts individual LC incidence and mortality, with a major effect of MSC for LDCT-positive individuals. These findings may have important implications in personalizing screening intervals.

01 augustus 2023, Contains fulltext : 296016.pdf (Publisher’s version ) (Closed access), Background Prior chest CT provides valuable temporal information (eg, changes in nodule size or appearance) to accurately estimate malignancy risk. Purpose To develop a deep learning (DL) algorithm that uses a current and prior low-dose CT examination to estimate 3-year malignancy risk of pulmonary nodules. Materials and Methods In this retrospective study, the algorithm was trained using National Lung Screening Trial data (collected from 2002 to 2004), wherein patients were imaged at most 2 years apart, and evaluated with two external test sets from the Danish Lung Cancer Screening Trial (DLCST) and the Multicentric Italian Lung Detection Trial (MILD), collected in 2004-2010 and 2005-2014, respectively. Performance was evaluated using area under the receiver operating characteristic curve (AUC) on cancer-enriched subsets with size-matched benign nodules imaged 1 and 2 years apart from DLCST and MILD, respectively. The algorithm was compared with a validated DL algorithm that only processed a single CT examination and the Pan-Canadian Early Lung Cancer Detection Study (PanCan) model. Results The training set included 10 508 nodules (422 malignant) in 4902 trial participants (mean age, 64 years ± 5 [SD]; 2778 men). The size-matched external test sets included 129 nodules (43 malignant) and 126 nodules (42 malignant). The algorithm achieved AUCs of 0.91 (95% CI: 0.85, 0.97) and 0.94 (95% CI: 0.89, 0.98). It significantly outperformed the DL algorithm that only processed a single CT examination (AUC, 0.85 [95% CI: 0.78, 0.92; P = .002]; and AUC, 0.89 [95% CI: 0.84, 0.95; P = .01]) and the PanCan model (AUC, 0.64 [95% CI: 0.53, 0.74; P < .001]; and AUC, 0.63 [95% CI: 0.52, 0.74; P < .001]). Conclusion A DL algorithm using current and prior low-dose CT examinations was more effective at estimating 3-year malignancy risk of pulmonary nodules than established models that only use a single CT examination. Clinical trial registration nos. NCT00047385, NCT00496977, NCT0283780

Item does not contain fulltext, BACKGROUND: A baseline computed tomography (CT) scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC risk and a high CD risk. METHODS: Participant demographics and quantitative CT measures of LC, cardiovascular disease and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting 5-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data were used to perform external validation (n=2287). RESULTS: Our final CD model outperformed an external pre-scan model (CD Risk Assessment Tool) in both the derivation (area under the curve (AUC) 0.744 (95% CI 0.727-0.761) and 0.677 (95% CI 0.658-0.695), respectively) and validation cohorts (AUC 0.744 (95% CI 0.652-0.835) and 0.725 (95% CI 0.633-0.816), respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096 (27%)) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287 (34%)) were 129 (versus 29) and 1.67 (versus 0.43). CONCLUSIONS: Evaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.

Background: The Multicentric Italian Lung Detection (MILD) trial demonstrated that prolonged low-dose computed tomography (LDCT) screening could achieve a 39% reduction in lung cancer (LC) mortality. We have here evaluated the long-term results of annual vs. biennial LDCT and the impact of screening intensity on overall and LC-specific mortality at 10 years. Patients and methods: Between 2005 and 2018, the MILD trial prospectively randomised the 2376 screening arm participants to annual (n = 1190) or biennial (n = 1186) LDCT, for a median screening period of 6.2 years and 23,083 person-years of follow-up. The primary outcomes were 10-year overall and LC-specific mortality, and the secondary end-points were the frequency of advanced-stage and interval LCs. Results: The biennial LDCT arm showed a similar overall mortality (hazard ratio [HR] 0.80, 95% confidence interval [CI] 0.57–1.12) and LC-specific mortality at 10 years (HR 1.10, 95% CI 0.59–2.05), as compared with the annual LDCT arm. Biennial screening saved 44% of follow-up LDCTs in subjects with negative baseline LDCT, and 38% of LDCTs in all participants, with no increase in the occurrence of stage II-IV or interval LCs. Conclusions: The MILD trial provides original evidence that prolonged screening beyond five years with biennial LDCT can achieve an LC mortality reduction comparable to annual LDCT, in subjects with a negative baseline examination.

In the original article, the Figure 1 caption contained an erroneous sentence. It has now been corrected to read: ‘Cumulative overall mortality and lung cancermortality, by arm over 10 years of follow-up.’ The Publisher apologizes for the error.

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.

Contains fulltext : 232913.pdf (Publisher’s version ) (Open Access), OBJECTIVES: The 2019 Lung CT Screening Reporting & Data System version 1.1 (Lung-RADS v1.1) introduced volumetric categories for nodule management. The aims of this study were to report the distribution of Lung-RADS v1.1 volumetric categories and to analyse lung cancer (LC) outcomes within 3 years for exploring personalized algorithm for lung cancer screening (LCS). METHODS: Subjects from the Multicentric Italian Lung Detection (MILD) trial were retrospectively selected by National Lung Screening Trial (NLST) criteria. Baseline characteristics included selected pre-test metrics and nodule characterization according to the volume-based categories of Lung-RADS v1.1. Nodule volume was obtained by segmentation with dedicated semi-automatic software. Primary outcome was diagnosis of LC, tested by univariate and multivariable models. Secondary outcome was stage of LC. Increased interval algorithms were simulated for testing rate of delayed diagnosis (RDD) and reduction of low-dose computed tomography (LDCT) burden. RESULTS: In 1248 NLST-eligible subjects, LC frequency was 1.2% at 1 year, 1.8% at 2 years and 2.6% at 3 years. Nodule volume in Lung-RADS v1.1 was a strong predictor of LC: positive LDCT showed an odds ratio (OR) of 75.60 at 1 year (p < 0.0001), and indeterminate LDCT showed an OR of 9.16 at 2 years (p = 0.0068) and an OR of 6.35 at 3 years (p = 0.0042). In the first 2 years after negative LDCT, 100% of resected LC was stage I. The simulations of low-frequency screening showed a RDD of 13.6-21.9% and a potential reduction of LDCT burden of 25.5-41%. CONCLUSIONS: Nodule volume by semi-automatic software allowed stratification of LC risk across Lung-RADS v1.1 categories. Personalized screening algorithm by increased interval seems feasible in 80% of NLST eligible. KEY POINTS: * Using semi-automatic segmentation of nodule volume, Lung-RADS v1.1 selected 10.8% of subjects with positive CT and 96.87 relative risk of lung cancer at 1 year, compared to negative CT. *

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 r

Almost 25% of lung cancers (LCs) occur in never-smokers. LC inflammatory profile, based on plasma C-reactive protein levels (CRP), predicts mortality, independently by smoking-status. We hypothesized that: CRP could be associated with tumor immune contexture (TIC) in never-smokers and both these two parameters may improve their prognosis. Sixty-eight never-smokers LC patients with high or low CRP were selected. The programmed cell death protein 1 (PD-1) and its ligand (PD-L1), the human leukocyte antigens (HLA-DR and HLA-I), CD8, CD4, CD3, CD33, CD163, and CD68 were evaluated by immunohistochemistry on surgical samples given TIC evaluation. The classification model based on TIC scores was generated by Classification and Regression Tree analysis. Tumor mutational burden was evaluated by targeted next-generation sequencing. Exclusively high CRP (H-CRP) subset showed PD-L1 expression in 35% of LC as well as lower HLA-I and HLA-DR in their stromal cells. CD3, CD4, CD8, HLA-I, HLA-DR tumor cells staining were associated with a “low inflammatory profile” subset. CRP and LC immune profiles drive clinical outcome: 5-year survival 88% against 8% was associated with low and high-risk profiles (p < 0.0001). Clinical outcome prediction in never-smoker LC patients may be improved by both CRP and tumor immune contexture evaluation.

OBJECTIVES: Although it has been postulated that patients might benefit from the centralization of high-volume specialized centres, conflicting results have been reported on the relationship between the number of lung resections performed and the long-term, all-cause mortality rates among patients who underwent surgery for lung cancer. A population-based observational study was performed to contribute to the ongoing debate. METHODS: The 2613 patients, all residents of the Lombardy region (Italy), who underwent lung resection for lung cancer from 2012 to 2014 were entered into the cohort and were followed until 2018. The hospitals were classified according to the annual number of pulmonary resections performed. Three categories of lung resection cases were identified: low (≤30), intermediate (31-95) and high (>95). The outcome of interest was all-cause death. A frailty model was used to estimate the death risk associated with the categories of numbers of lung resections performed, taking into account the multilevel structure of the data. A set of sensitivity analyses was performed to account for sources of systematic uncertainty. RESULTS: The 1-year and 5-year survival rates of cohort members were 90% and 63%. Patients operated on in high-volume centres were on average younger and more often women. Compared to patients operated on in a low-volume centre, the mortality risk exhibited a significant, progressive reduction as the numbers of lung resections performed increased to intermediate (-13%; 95% confidence interval +10% to-31%) and high (-26%; 0% to-45%). Sensitivity analyses revealed that the association was consistent. CONCLUSIONS: Further evidence that the volume of lung resection cases performed strongly affects the long-term survival of lung cancer patients has been supplied.

We herein report an uncommon association of intimately admixed atypical carcinoid (AC) and large cell neuroendocrine (NE) carcinoma (LCNEC) of the thymus, occurring in two 20- and 39-year-old Caucasian males. Both tumors were treated by maximal thymectomy. The younger patient presented with a synchronous lesion and died of disease after 9 months, while the other patient was associated with a recurrent ectopic adrenocorticotropic hormone Cushing’s syndrome and is alive with disease at the 2-year follow-up. MEN1 syndrome was excluded in either case. Immunohistochemically, disarrayed cytoplasmic and nuclear ß-catenin expression was seen alongside an intra-tumor Ki-67 antigen labeling index (LI) ranging from 2 to 80% in the younger patient’s tumor and from 3 to 45% in the other. Both exhibited upregulated cyclin D1 and retinoblastoma, while vimentin was overexpressed in the recurrent LCNEC only. Next-generation sequencing revealed CTNNB1, TP53, and JAK3 mutations in the synchronous tumor and CTNNB1 mutation alone in the metachronous tumor (the latter with the same mutation as the first tumor of 17 years prior). None of the 23 T-NET controls exhibited this hallmarking triple alteration (p = 0.003). These findings suggested that LCNEC components developed from pre-existing CTNNB1-mutated AC upon loss-of-function TP53 and gain-of-function JAK3 mutations in one case and an epithelial-mesenchymal transition upon vimentin overexpression in the other case. Both tumors maintained intact cyclin D1–retinoblastoma machinery. Our report challenges the concept of secondary LCNEC as an entity that develops from pre-existing AC as a result of tumor progression, suggesting a paradigm shift to the current pathogenesis of NET

Recent randomised trials on screening with low-dose CT have shown important reductions in lung cancer (LC) mortality and have triggered international efforts to implement LC screening. Detection rates of stage I LC with volume CT approaching 70% have been demonstrated. In April 2019 'ESMO Open-Cancer Horizons' convened a roundtable discussion on the challenges and potential solutions regarding the implementation of LC screening in Europe. The expert panel reviewed the current evidence for LC screening with low-dose CT and discussed the next steps, which are covered in this article. The panel concluded that national health policy groups in Europe should start to implement CT screening as adequate evidence is available. It was recognised that there are opportunities to improve the screening process through 'Implementation Research Programmes'.

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Background: Blood level of C-reactive protein (CRP) at diagnosis is a well-know prognostic bio-marker in different primary tumors, but its role has not been investigated in resectable lung metastases. The aim of our study is to assess the predictive value of baseline (CRP0) and 3rd postoperative day (CRP3) levels on long-term survival of patients undergoing lung metastasectomy. Methods: A total of 846 consecutive patients underwent the first pulmonary resection for lung metastases between January 2003 and December 2015, including 611 (72%) single surgical procedures, 235 (28%) multiple metastasectomies, 501 (59%) epithelial primary tumors, 276 (33%) sarcomas, 66 (8%) melanomas, 286 (33.8%) with 0 risk factors (CRP0 ≤ 2 and CRP3 ≤ 84 mg/L) and 560 (66.2%) with ≥ 1 risk factor (CRP 0 > 2 and/or CRP 3 > 84 mg/L). Results: Cumulative 5-year survival was 57% in patients with low CRP (0 risk factors) versus 43% in high CRP (≥ 1 risk factor, p < 0.0002), 62% versus 50% respectively for epithelial tumors (p < 0.0140), and 51% versus 34% for sarcomas (p < 0.0111). Multivariable Cox analysis confirmed a mortality hazard ratio of 2.5 at 1-year and 1.5 at 5-years in patients with high CRP. Conclusions: Baseline and postoperative CRP levels predict survival of patients with resectable lung metastases. These data provide a rationale for prospective clinical trials testing the efficacy of anti-inflammatory or immune-modulating agents as “adjuvant” therapy after lung metastasectomy, in patients with elevated pre- and/or postoperative CRP levels.