Cardiovascular disease is the leading cause of morbidity and mortality worldwide. Atherosclerosis is a complex and multifactorial process, characterized by early asymptomatic formation of plaque in the arterial walls, silent plaque progression that poses flow limitation, and risk of sudden rupture

Lung cancer, the most commonly diagnosed cancer worldwide, usually presents as solid pulmonary nodules (SPNs) on early diagnostic images. Classification of malignant disease at this early timepoint is critical for improving the success of surgical resection and increasing 5-year survival rates. ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) PET/CT has demonstrated value for SPNs diagnosis with high sensitivity to detect malignant SPNs, but lower specificity in diagnosing malignant SPNs in populations with endemic infectious lung disease. This study aimed to determine whether quantitative heterogeneity derived from various texture features on dual time FDG PET/CT images (DTPI) can differentiate between malignant and benign SPNs in patients from granuloma-endemic regions. Machine learning methods were employed to find optimal discrimination between malignant and benign nodules. Machine learning models trained by texture features on DTPI images achieved significant improvements over standard clinical metrics and visual interpretation for discriminating benign from malignant SPNs, especially by texture features on delayed FDG PET/CT images.

Lung cancer usually presents as a solitary pulmonary nodule (SPN) on diagnostic imaging during the early stages of the disease. Since the early diagnosis of lung cancer is very important for treatment, the accurate diagnosis of SPNs has much importance. The aim of this study was to evaluate the discriminant power of dual time point imaging (DTPI) PET/CT in the differentiation of malignant and benign FDG-avid solitary pulmonary nodules by using neighborhood gray-tone difference matrix (NGTDM) texture features. Retrospective analysis was carried out on 116 patients with SPNs (35 benign and 81 malignant) who had DTPI 18F-FDG PET/CT between January 2005 and May 2015. Both PET and CT images were acquired at 1 h and 3 h after injection. The SUVmax and NGTDM texture features (coarseness, contrast, and busyness) of each nodule were calculated on dual time point images. Patients were randomly divided into training and validation datasets. Receiver operating characteristic (ROC) curve analysis was performed on all texture features in the training dataset to calculate the optimal threshold for differentiating malignant SPNs from benign SPNs. For all the lesions in the testing dataset, two visual interpretation scores were determined by two nuclear medicine physicians based on the PET/CT images with and without reference to the texture features. In the training dataset, the AUCs of delayed busyness, delayed coarseness, early busyness, and early SUVmax were 0.87, 0.85, 0.75 and 0.75, respectively. In the validation dataset, the AUCs of visual interpretations with and without texture features were 0.89 and 0.80, respectively. Compared to SUVmax or visual interpretation, NGTDM texture features derived from DTPI PET/CT images can be used as good predictors of SPN malignancy. Improvement in discriminating benign from malignant nodules using SUVmax and visual interpretation can be achieved by adding busyness extracted from delayed PET/CT images.

Regulated by the tumor microenvironment, the metabolic network of the tumor is reprogrammed, driven by oncogenes and tumor suppressor genes. The metabolic phenotype of tumors of different driven-genes and different tissue types is extremely heterogeneous. KRAS-mutant non-small cell lung cancer (NSCLC) has glutamine dependence. In this study, we demonstrated that glutamine utilization of KRAS-mutant NSCLC was higher than that of KRAS wild-type. CB839, an efficient glutaminase inhibitor, synergized with the MEK inhibitor selumetinib to enhance antitumor activity in KRAS-mutant NSCLC cells and xenografts, and the therapeutic response could be well identified by ¹⁸F-FDG PET imaging. Combination therapy induced redox stress, manifesting as a decrease in mitochondrial membrane potential and an increase in ROS levels, and energetic stress manifesting as suppression of glycolysis and glutamine degradation. The phosphorylation of AKT was also suppressed. These effects combined to induce autophagy and thereby caused cancer cell death. Our results suggest that dual inhibition of the MEK-ERK pathway and glutamine metabolism activated by KRAS mutation may be an effective treatment strategy for KRAS-driven NSCLC.