Global Epidemiology of Lung Cancer
TLDR
The evidence on lung cancer epidemiology, including data of international scope with comparisons of economically, socially, and biologically different patient groups is reviewed, including a discussion on the epidemiologic characteristics of special groups including women and nonsmokers.Abstract:
While lung cancer has been the leading cause of cancer-related deaths for many years in the United States, incidence and mortality statistics – among other measures – vary widely worldwide. The aim of this study was to review the evidence on lung cancer epidemiology, including data of international scope with comparisons of economically, socially, and biologically different patient groups. In industrialized nations, evolving social and cultural smoking patterns have led to rising or plateauing rates of lung cancer in women, lagging the long-declining smoking and cancer incidence rates in men. In contrast, emerging economies vary widely in smoking practices and cancer incidence but commonly also harbor risks from environmental exposures, particularly widespread air pollution. Recent research has also revealed clinical, radiologic, and pathologic correlates, leading to greater knowledge in molecular profiling and targeted therapeutics, as well as an emphasis on the rising incidence of adenocarcinoma histology. Furthermore, emergent evidence about the benefits of lung cancer screening has led to efforts to identify high-risk smokers and development of prediction tools. This review also includes a discussion on the epidemiologic characteristics of special groups including women and nonsmokers. Varying trends in smoking largely dictate international patterns in lung cancer incidence and mortality. With declining smoking rates in developed countries and knowledge gains made through molecular profiling of tumors, the emergence of new risk factors and disease features will lead to changes in the landscape of lung cancer epidemiology.read more
Citations
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Lung Cancer in Never Smokers - Different Disease
TL;DR: Current knowledge of lung cancers arising in never smokers versus smokers is summarized, suggesting that they are separate entities.
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Toxic Metal Implications on Agricultural Soils, Plants, Animals, Aquatic life and Human Health
Uchenna Okereafor,Mamookho Elizabeth Makhatha,Lukhanyo Mekuto,Nkemdinma Uche-Okereafor,Tendani Edith Sebola,Vuyo Mavumengwana +5 more
TL;DR: In this review, concerted efforts were made to condense the information contained in literature regarding toxic metal pollution and its implications in soil, water, plants, animals, marine life and human health.
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Epidemiology of lung cancer and lung cancer screening programs in China and the United States.
TL;DR: The authors compare trends of incidence and mortality of lung cancer in the US and China, and the impact on lung cancer screening programs in the two countries, and suggest inclusion of ambient air pollution exposure and gender into lung cancer risk prognostic models to better capture high-risk individuals.
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In vivo gene delivery mediated by non-viral vectors for cancer therapy.
Reza Mohammadinejad,Ali Dehshahri,Vijay Sagar Madamsetty,Masoumeh Zahmatkeshan,Shima Tavakol,Pooyan Makvandi,Danial Khorsandi,Abbas Pardakhty,Milad Ashrafizadeh,Elham Ghasemipour Afshar,Ali Zarrabi +10 more
TL;DR: The in vivo gene delivery mediated by non-viral vectors to treat cancer in different tissue and organs including brain, breast, lung, liver, stomach, and prostate is highlighted and the state-of-the-art and promising perspective of in vitro gene editing using non-Viral nano-vectors is delineated.
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Artificial Intelligence in Lung Cancer Pathology Image Analysis.
Shidan Wang,Donghan M. Yang,Ruichen Rong,Xiaowei Zhan,Junya Fujimoto,Hongyu Liu,John D. Minna,Ignacio I. Wistuba,Yang Xie,Guanghua Xiao +9 more
TL;DR: This review aims to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer, and points out some promising future directions for lung cancer pathologyimage analysis, including multi-task learning, transfer learning, and model interpretation.
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