Risk of lung cancer due to external environmental factor and epidemiological data analysis

Overview and countermeasures of cancer burden in China

Cancer is one of the leading causes of human death worldwide. Treatment of cancer exhausts significant medical resources, and the morbidity and mortality caused by cancer is a huge social burden. Cancer has therefore become a serious economic and social problem shared globally. As an increasingly prevalent disease in China, cancer is a huge challenge for the country's healthcare system. Based on recent data published in the Journal of the National Cancer Center on cancer incidence and mortality in China in 2016, we analyzed the current trends in cancer incidence and changes in cancer mortality and survival rate in China. And also, we examined several key risk factors for cancer pathogenesis and discussed potential countermeasures for cancer prevention and treatment in China.

Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images

Background and Objectives: Lung cancer remains a leading cause of cancer mortality worldwide. Accurately classifying benign pulmonary nodules and malignant ones is crucial for early diagnosis and improved patient outcomes. The purpose of this study is to explore the deep-learning model of ResNet combined with a convolutional block attention module (CBAM) for the differentiation between benign and malignant lung cancer, based on computed tomography (CT) images, morphological features, and clinical information. Methods and materials: In this study, 8241 CT slices containing pulmonary nodules were retrospectively included. A random sample comprising 20% (n = 1647) of the images was used as the test set, and the remaining data were used as the training set. ResNet combined CBAM (ResNet-CBAM) was used to establish classifiers on the basis of images, morphological features, and clinical information. Nonsubsampled dual-tree complex contourlet transform (NSDTCT) combined with SVM classifier (NSDTCT-SVM) was used as a comparative model. Results: The AUC and the accuracy of the CBAM-ResNet model were 0.940 and 0.867, respectively, in test set when there were only images as inputs. By combining the morphological features and clinical information, CBAM-ResNet shows better performance (AUC: 0.957, accuracy: 0.898). In comparison, a radiomic analysis using NSDTCT-SVM achieved AUC and accuracy values of 0.807 and 0.779, respectively. Conclusions: Our findings demonstrate that deep-learning models, combined with additional information, can enhance the classification performance of pulmonary nodules. This model can assist clinicians in accurately diagnosing pulmonary nodules in clinical practice.

Effects of Tobacco Smoking on the Survivability of Patients with Multiple Cancers and Single Lung Cancer

Lung cancer is the leading cause of death worldwide among men and women. Tobacco smoking is the number one risk factor for lung cancer. The aim of our study was to evaluate the survivability of patients with single lung cancer in relation to the survival time in patients with multiple neoplasms whose last neoplasm was a lung cancer. A retrospective analysis was con-ducted of data from medical histories of patients hospitalized at the Pulmonary Hospital in Olsztyn (Poland) from 2012 to 2017, with a lung cancer diagnosis as the first or subsequent cancer. The total longevity of women with diagnosed multiple cancers was found to be shorter than that of men: 67.60 years (SD: 7.77) and 69.91 years (SD: 7.97), respectively. Among the ex-smokers, the longevity of men (68.93 years) was longer than that of women (66.18 years). Survival time, counted from the diagnosis of both the first and subsequent cancer, was longer among patients with multiple cancers than among patients with single lung cancer (p = 0.000). Women's survivability was worse than men's in the case of multiple cancers and in the group of people who quit smoking (p = 0.037; p = 0.000). To conclude, smoking tobacco affects the survival of patients with lung cancer. Smoking cessation improves overall survival.

Mathematical Modeling and Analysis of Tumor Chemotherapy

Cancer diseases lead to the second-highest death rate all over the world. For treating tumors, one of the most common schemes is chemotherapy, which can decrease the tumor size and control the progression of cancer diseases. To better understand the mechanisms of chemotherapy, we developed a mathematical model of tumor growth under chemotherapy. This model includes both immune system response and drug therapy. We characterize the symmetrical properties and dynamics of this differential equation model by finding the equilibrium points and exploring the stability and symmetry properties in a range of model parameters. Sensitivity analyses suggest that the chemotherapy drug-induced tumor mortality rate and the drug decay rate contribute significantly to the determination of treatment outcomes. Numerical simulations highlight the importance of CTL activation in tumor chemotherapy.