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Introduction: Neutrophil extracellular traps (NETs) are complex structures released by activated neutrophils that may modulate different steps of the metastatic cascade. The aim of our study was to investigate how NETs can modulate the adhesion properties of cancer cells and whether cell exposure to NETs can activate the epithelial-to-mesenchymal transition (EMT) program thus enhancing the migratory and invasive properties of tumor cells. Materials and methods: Different cancer cell lines were subjected to a solidphase adhesion assay using NET-coated plates with or without the addition of antibodies against α5β1 or CCDC25 receptor. After 1-4 h of incubation, adherent cells were expressed as the percentage of total cell number. To test EMT occurrence, cells were treated with NETs for up to 48 h and then the levels of E-cadherin, vimentin, Snail, Slug, Zeb 1 and Twist 1 along with levels of Notch 1 and cleaved Notch 1 were determined by western blotting. Untreated and NETtreated cells were subjected to migration assays using 24-multiwell plates with transwell and FBS as chemoattractant. Results: Cancer cell adhesion to NET-coated plates varied between 30% and 92.7% and was significantly higher than that obtained in uncoated plates. The addition of antibodies against α5β1 or CCDC25 caused a strong reduction of cell adhesion to NETs. The prolonged exposure of EGFR-driven cancer cell lines to NETs caused the activation of the EMT program through the upregulation and cleavage of Notch 1 and was confirmed by the enhanced expression of EMT markers. The consequent loss of the epithelial phenotype induced a strong reduction of the expression of the oncogene driver. Cell migration was significantly enhanced in NET-treated cells as compared to untreated cells. Discussion: Our findings reveal the dynamic role of NETs that may provide a DNA and fibronectin rich environment for binding of many cancer cells at distant sites where the prolonged exposure to NETs triggers the EMT through the activation of Notch 1 signaling pathway with the subsequent enhancement of migratory and invasive properties of cancer cells. Furthermore, our findings provide an example of how an immune/inflammatory microenvironment may directly modulate the sensitivity of cancer cells to oncogene targeted agents. [ABSTRACT FROM AUTHOR]

The aim of our study was to predict the occurrence of distant metastases in non-small-cell lung cancer (NSCLC) patients using machine learning methods and texture analysis of 18F-labeled 2-deoxy-d-glucose Positron Emission Tomography/Computed Tomography {[18F]FDG PET/CT} images. In this retrospective and single-center study, we evaluated 79 patients with advanced NSCLC who had undergone [18F]FDG PET/CT scan at diagnosis before any therapy. Patients were divided into two independent training (n = 44) and final testing (n = 35) cohorts. Texture features of primary tumors and lymph node metastases were extracted from [18F]FDG PET/CT images using the LIFEx program. Six machine learning methods were applied to the training dataset using the entire panel of features. Dedicated selection methods were used to generate different combinations of five features. The performance of selected machine learning methods applied to the different combinations of features was determined using accuracy, the confusion matrix, receiver operating characteristic (ROC) curves, and area under the curve (AUC). A total of 104 and 78 lesions were analyzed in the training and final testing cohorts, respectively. The support vector machine (SVM) and decision tree methods showed the highest accuracy in the training cohort. Seven combinations of five features were obtained and introduced in the models and subsequently applied to the training and final testing cohorts using the SVM and decision tree. The accuracy and the AUC of the decision tree method were higher than those obtained with the SVM in the final testing cohort. The best combination of features included shape sphericity, gray level run length matrix_run length non-uniformity (GLRLM_RLNU), Total Lesion Glycolysis (TLG), Metabolic Tumor Volume (MTV), and shape compacity. The combination of these features with the decision tree method could predict the occurrence of distant metastases with an accuracy of 74.4% and an AUC of 0.63 in NSCLC patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Lung cancer is the leading cause of cancer-related death worldwide. The 18F-FDG PET/CT scan is used daily for the diagnosis and staging of lung cancer. The heterogeneity of 18F-FDG uptake inside tumor lesions may have prognostic implications for lung cancer patients. Therefore, we tested whether an index of heterogeneity such as the coefficient of variation (CoV), determined in both metastatic lymph nodes and primary tumors, can predict overall survival of lung cancer patients. We found that a combination of CoV of targeted lymph nodes with CoV of primary tumors in each patient provides a more accurate prognostic stratification of lung cancer allowing risk-adapted therapy in individual patients. Purpose The aim of the present study was to test whether the coefficient of variation (CoV) of 18F-FDG PET/CT images of metastatic lymph nodes and primary tumors may predict clinical outcome in patients with advanced non-small cell lung cancer (NSCLC). Materials and Methods Fifty-eight NSCLC patients who had undergone 18F-FDG PET/CT at diagnosis were evaluated. SUVmax, SUVmean, CoV, MTV and TLG were determined in targeted lymph nodes and corresponding primary tumors along with Total MTV (MTVTOT) and Whole-Body TLG (TLGWB) of all malignant lesions. Univariate analysis was performed using Cox proportional hazards regression whereas the Kaplan–Meier method and log-rank tests were used for survival analysis. Results Fifty-eight metastatic lymph nodes were analyzed and average values of SUVmax, SUVmean, CoV, MTV and TLG were 11.89 ± 8.54, 4.85 ± 1.90, 0.37 ± 0.16, 46.16 ± 99.59 mL and 256.84 ± 548.27 g, respectively, whereas in primary tumors they were 11.92 ± 6.21, 5.47 ± 2.34, 0.36 ± 0.14, 48.03 ± 64.45 mL and 285.21 ± 397.95 g, respectively. At univariate analysis, overall survival (OS) was predicted by SUVmax (p = 0.0363), SUVmean (p = 0.0200) and CoV (p = 0.0139) of targeted lymph nodes as well as by CoV of primary tumors (p = 0.0173), MTVTOT (p = 0.0007), TLGWB (p = 0.0129) and stage (p = 0.0122). Using Kaplan–Meier analysis, OS was significantly better in patients with CoV of targeted lymph nodes ≤ 0.29 than those with CoV > 0.29 (p = 0.0147), meanwhile patients with CoV of primary tumors > 0.38 had a better prognosis compared to those with CoV ≤ 0.38 (p = 0.0137). Finally, we combined the CoV values of targeted lymph nodes and primary tumors in all possible arrangements and a statistically significant difference was found among the four survival curves (p = 0.0133). In particular, patients with CoV of targeted lymph nodes ≤ 0.29 and CoV of primary tumors > 0.38 had the best prognosis. Conclusions The CoV of targeted lymph nodes combined with the CoV of primary tumors can predict prognosis of NSCLC patients. [ABSTRACT FROM AUTHOR]

We investigated the role of Coefficient of Variation (CoV), a first-order texture parameter derived from 18F-FDG PET/CT, in the prognosis of Non-Small Cell Lung Cancer (NSCLC) patients. Eighty-four patients with advanced NSCLC who underwent 18F-FDG PET/CT before therapy were retrospectively studied. SUVmax, SUVmean, CoV, total Metabolic Tumor Volume (MTVTOT) and whole-body Total Lesion Glycolysis (TLGWB) were determined by an automated contouring program (SUV threshold at 2.5). We analyzed 194 lesions: primary tumors (n = 84), regional (n = 48) and non-regional (n = 17) lymph nodes and metastases in liver (n = 9), bone (n = 23) and other sites (n = 13); average CoVs were 0.36 ± 0.13, 0.36 ± 0.14, 0.42 ± 0.18, 0.30 ± 0.14, 0.37 ± 0.17, 0.34 ± 0.13, respectively. No significant differences were found between the CoV values among the different lesion categories. Survival analysis included age, gender, histology, stage, MTVTOT, TLGWB and imaging parameters derived from primary tumors. At univariate analysis, CoV (p = 0.0184), MTVTOT (p = 0.0050), TLGWB (p = 0.0108) and stage (p = 0.0041) predicted Overall Survival (OS). At multivariate analysis, age, CoV, MTVTOT and stage were retained in the model (p = 0.0001). Patients with CoV > 0.38 had significantly better OS than those with CoV ≤ 0.38 (p = 0.0143). Patients with MTVTOT ≤ 89.5 mL had higher OS than those with MTVTOT > 89.5 mL (p = 0.0063). Combining CoV and MTVTOT, patients with CoV ≤ 0.38 and MTVTOT > 89.5 mL had the worst prognosis. CoV, by reflecting the heterogeneity of glycolytic phenotype, can predict clinical outcomes in NSCLC patients. [ABSTRACT FROM AUTHOR]

Simple Summary: Adrenal masses represent a common incidental finding at imaging exams such as computed tomography (CT) and magnetic resonance imaging performed for unrelated reasons. Encompassing both benign and malignant entities, these lesions can prove challenging to classify. 2-[18F]FDG PET-CT is a recognized imaging modality for the characterization of indeterminate adrenal masses, but it is an expensive imaging modality and involves radiation exposure. To reduce the number of required scans and identify a less invasive potential alternative, we investigated whether CT radiomics could be used to predict the diagnostic parameter obtained using 2-[18F]FDG PET-CT (namely SUVmax). However, in our retrospective cohort of 179 adrenal masses and 150 PET-CT scans (of which 66 without iodine contrast injection), no correlation was found between the radiomics synthetic value (RadSV) and 2-[18F]FDG PET-CT SUVmax. This preliminary finding suggests that it might not be possible to use CT radiomics to reduce 2-[18F]FDG PET-CT referrals, confirming the role as problem solving tool of this imaging modality. Background: Indeterminate adrenal masses (AM) pose a diagnostic challenge, and 2-[18F]FDG PET-CT serves as a problem-solving tool. Aim of this study was to investigate whether CT radiomics features could be used to predict the 2-[18F]FDG SUVmax of AM. Methods: Patients with AM on 2-[18F]FDG PET-CT scan were grouped based on iodine contrast injection as CT contrast-enhanced (CE) or CT unenhanced (NCE). Two-dimensional segmentations of AM were manually obtained by multiple operators on CT images. Image resampling and discretization (bin number = 16) were performed. 919 features were calculated using PyRadiomics. After scaling, unstable, redundant, and low variance features were discarded. Using linear regression and the Uniform Manifold Approximation and Projection technique, a CT radiomics synthetic value (RadSV) was obtained. The correlation between CT RadSV and 2-[18F]FDG SUVmax was assessed with Pearson test. Results: A total of 725 patients underwent PET-CT from April 2020 to April 2021. In 150 (21%) patients, a total of 179 AM (29 bilateral) were detected. Group CE consisted of 84 patients with 108 AM (size = 18.1 ± 4.9 mm) and Group NCE of 66 patients with 71 AM (size = 18.5 ± 3.8 mm). In both groups, 39 features were selected. No statisticallyf significant correlation between CT RadSV and 2-[18F]FDG SUVmax was found (Group CE, r = 0.18 and p = 0.058; Group NCE, r = 0.13 and p = 0.27). Conclusions: It might not be feasible to predict 2-[18F]FDG SUVmax of AM using CT RadSV. Its role as a problem-solving tool for indeterminate AM remains fundamental. [ABSTRACT FROM AUTHOR]