(1) Background: Cell proliferation (Ki-67) has important clinical value in the treatment and prognosis of non-small cell lung cancer (NSCLC). However, current detection methods for Ki-67 are invasive and can lead to incorrect results. This study aimed to explore a deep learning classification model for the prediction of Ki-67 and the prognosis of NSCLC based on FDG-PET/CT images. (2) Methods: The FDG-PET/CT scan results of 159 patients with NSCLC confirmed via pathology were analyzed retrospectively, and the prediction models for the Ki-67 expression level based on PET images, CT images and PET/CT combined images were constructed using Densenet201. Based on a Ki-67 high expression score (HES) obtained from the prediction model, the survival rate of patients with NSCLC was analyzed using Kaplan–Meier and univariate Cox regression. (3) Results: The statistical analysis showed that Ki-67 expression was significantly correlated with clinical features of NSCLC, including age, gender, differentiation state and histopathological type. After a comparison of the three models (i.e., the PET model, the CT model, and the FDG-PET/CT combined model), the combined model was found to have the greatest advantage in Ki-67 prediction in terms of AUC (0.891), accuracy (0.822), precision (0.776) and specificity (0.902). Meanwhile, our results indicated that HES was a risk factor for prognosis and could be used for the survival prediction of NSCLC patients. (4) Conclusions: The deep-learning-based FDG-PET/CT radiomics classifier provided a novel non-invasive strategy with which to evaluate the malignancy and prognosis of NSCLC.

A high level of oxidized low‑density lipoproteins (oxLDLs) is an independent risk factor for cardiovascular disease. The aim of the present study was to investigate whether insulin‑like growth factor‑1 (IGF‑1) protected endothelial progenitor cells (EPCs) from injury caused by ox‑LDLs, and whether the endothelial nitric oxide synthase (eNOS)/nitric oxide (NO) pathway was involved in this process. EPCs were isolated from human peripheral blood and characterized. In order to evaluate the effect of IGF‑1 on EPCs, cells were incubated with ox‑LDLs (100 mg/ml) for 24 h to induce a model of EPC dysfunction in vitro, which demonstrated a decrease in the number of EPCs, concomitant with increased apoptosis and decreased proliferation rates. IGF‑1 dose‑dependently increased the number of EPCs. Concurrently, IGF‑1 decreased the levels of apoptosis of EPCs and improved EPCs proliferation following ox‑LDLs challenge. In addition, IGF‑1 significantly increased NO levels in ox‑LDLs‑treated EPCs, accompanied by an upregulation in eNOS expression. The protective effects of IGF‑1 on EPCs and NO production were abolished by L‑NAME, a specific eNOS inhibitor. These results suggested that IGF‑1 protects EPCs from dysfunction induced by oxLDLs through a mechanism involving the eNOS/NO pathway.

Subsequently to the publication of this article, the authors have realized that the name of the first author was spelt incorrectly: The name was spelt as ‘Hao‑jing Wen’, whereas the name should have been presented as ‘Jing‑wen Hao. The name as it should have appeared in the author list is featured above. The authors regret that this this error was not corrected prior to the publication of the above article, and apologize to the author in question and to the readership for any inconvenience caused.[the original article was published in Molecular Medicine Reports 19: 660‑666, 2019; DOI:10.3892/mmr.2018.9633]