About this Research Topic
Lung cancer is still one of the most common malignancies with a high global mortality rate with over 2 million cases confirmed by the World Health Organization in 2018. Although there has been progress in diagnosing and treating lung cancer, patients still have poor prognosis with a 5-year survival rate typically from 4-17% which is dependent on the stage of the cancer and regional differences. The majority of lung cancer patients are at the advanced stages of the disease at the time of their diagnosis and therefore, have less chances of early treatment that could have improved their survival rate. Therefore, early detection of lung cancer remains imperative to improve the prognosis.
Though randomized clinical trials provide the highest level of scientific evidence for advancing patient care, they often leave gaps in our collective understanding. Real World Data and Real World Evidence can be used to address gaps in knowledge gained from clinical trials, detect rare events, inform safety labeling, and identify patient subgroups who are more or less likely to benefit from certain therapies, ultimately helping healthcare professionals to optimize treatment decisions. They can also help to increase our understanding around patterns of care and in different geographic and economic scenarios.
This Research Topic will focus on valuable real-world evidence highlighting the disease and patient characteristics, therapeutic outcomes (efficacy and safety) and treatment patterns in lung cancer. This collection will include Original Research and Reviews. Manuscripts should focus on but are not limited to:
1) Real-world data about ICIs outcome and safety
2) Real-world data about TKIs outcome and safety
3) Real-world data studies describing treatment efficacy in patients with lung cancer
4) Real-world data studies describing the safety and long-term adverse events of treatment in patients with lung cancer
5) Real-world data concerning molecular diagnostics and liquid biopsy for lung cancer
6) Real world data concerning radiotherapy outcomes in lung cancer patients with oligometastatic/oligoprogressive disease
Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
Though randomized clinical trials provide the highest level of scientific evidence for advancing patient care, they often leave gaps in our collective understanding. Real World Data and Real World Evidence can be used to address gaps in knowledge gained from clinical trials, detect rare events, inform safety labeling, and identify patient subgroups who are more or less likely to benefit from certain therapies, ultimately helping healthcare professionals to optimize treatment decisions. They can also help to increase our understanding around patterns of care and in different geographic and economic scenarios.
This Research Topic will focus on valuable real-world evidence highlighting the disease and patient characteristics, therapeutic outcomes (efficacy and safety) and treatment patterns in lung cancer. This collection will include Original Research and Reviews. Manuscripts should focus on but are not limited to:
1) Real-world data about ICIs outcome and safety
2) Real-world data about TKIs outcome and safety
3) Real-world data studies describing treatment efficacy in patients with lung cancer
4) Real-world data studies describing the safety and long-term adverse events of treatment in patients with lung cancer
5) Real-world data concerning molecular diagnostics and liquid biopsy for lung cancer
6) Real world data concerning radiotherapy outcomes in lung cancer patients with oligometastatic/oligoprogressive disease
Please note: manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) will not be accepted in any of the sections of Frontiers in Oncology.
Keywords: real-world data, lung cancer, nsclc
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
