More and more medical centers are now combining high-resolution CT scans well with deep learning and artificial intelligence for lung cancer screening, resulting in significantly improved diagnostic sensitivity. Furthermore, the increased molecular alterations in lung cancer were demonstrated not only in tumor tissue, but also in other body organs. For example, circulating tumor DNA combined with next-generation sequencing is now becoming a popular method for lung cancer diagnosis and therapeutic monitoring. Therefore, the first focus of this topic is on such achievements in early diagnosis of lung cancer, especially non-invasive tests such as liquid biopsy.
Despite the earlier diagnosis, the prognosis of lung cancer outcome remains challenging due to high recurrence and metastasis rates even after radical resection. The second focus of our topic is novel biomarkers that are involved in lung cancer progression and help predict prognosis. Identifying potential biomarkers could not only better elucidate the underlying mechanisms of lung cancer, but also aid in the therapeutic development. From this perspective, translational medicine and high-quality clinical trials are essential for clinical guidance.
This Research Topic aims to discuss the current knowledge and progress of tumor biomarkers for lung cancer diagnosis, prediction or prognosis, and to introduce the latest advances on potential targeted therapy. We welcome submissions covering, but not limited to, the following sub-topics:
1. Novel diagnostic/prognostic factors, biomarkers and/or risk factors in lung cancer
2. Novel drug targets and the related molecular mechanisms of oncogenic or tumor suppressor
3. Application of deep learning and artificial intelligence in lung cancer screening and diagnosis
4. Clinical studies involves the transformation of the final achievement of biomarker research, such as cohort study or RCT to provide therapeutic evidence of novel drugs targeting lung cancer
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
More and more medical centers are now combining high-resolution CT scans well with deep learning and artificial intelligence for lung cancer screening, resulting in significantly improved diagnostic sensitivity. Furthermore, the increased molecular alterations in lung cancer were demonstrated not only in tumor tissue, but also in other body organs. For example, circulating tumor DNA combined with next-generation sequencing is now becoming a popular method for lung cancer diagnosis and therapeutic monitoring. Therefore, the first focus of this topic is on such achievements in early diagnosis of lung cancer, especially non-invasive tests such as liquid biopsy.
Despite the earlier diagnosis, the prognosis of lung cancer outcome remains challenging due to high recurrence and metastasis rates even after radical resection. The second focus of our topic is novel biomarkers that are involved in lung cancer progression and help predict prognosis. Identifying potential biomarkers could not only better elucidate the underlying mechanisms of lung cancer, but also aid in the therapeutic development. From this perspective, translational medicine and high-quality clinical trials are essential for clinical guidance.
This Research Topic aims to discuss the current knowledge and progress of tumor biomarkers for lung cancer diagnosis, prediction or prognosis, and to introduce the latest advances on potential targeted therapy. We welcome submissions covering, but not limited to, the following sub-topics:
1. Novel diagnostic/prognostic factors, biomarkers and/or risk factors in lung cancer
2. Novel drug targets and the related molecular mechanisms of oncogenic or tumor suppressor
3. Application of deep learning and artificial intelligence in lung cancer screening and diagnosis
4. Clinical studies involves the transformation of the final achievement of biomarker research, such as cohort study or RCT to provide therapeutic evidence of novel drugs targeting lung cancer
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
