About this Research Topic
Non-Small Cell Lung Cancer (NSCLC) accounts for around 80% to 85% of all lung cancer cases, and is the leading cause of cancer death worldwide. Imaging such as computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) has a crucial role in detecting, diagnosing, and staging this disease. Additionally, imaging plays an important role in assessing patient response to therapy as well as surveilling potential tumor recurrence after treatment.
Although there has been significant progress towards improving early detection, staging, and treatment strategies for NSCLC, more can be learned to optimise imaging interventions as well as establish emerging approaches, such as radiogenomics, to improve the management of lung cancer. Thus, this Research Topic aims to shed light on the latest imaging advances in NSCLC and welcomes articles related, but not limited to:
1. Classification of histological subtypes of NSCLC
2. Prediction of EGFR mutation status in NSCLC
3. Radiological staging in NSCLC
4. Use of machine learning and artificial intelligence in diagnosis and treatment monitoring of NSCLC
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 Frontiers in Oncology.
Although there has been significant progress towards improving early detection, staging, and treatment strategies for NSCLC, more can be learned to optimise imaging interventions as well as establish emerging approaches, such as radiogenomics, to improve the management of lung cancer. Thus, this Research Topic aims to shed light on the latest imaging advances in NSCLC and welcomes articles related, but not limited to:
1. Classification of histological subtypes of NSCLC
2. Prediction of EGFR mutation status in NSCLC
3. Radiological staging in NSCLC
4. Use of machine learning and artificial intelligence in diagnosis and treatment monitoring of NSCLC
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 Frontiers in Oncology.
Keywords: NSCLC, EGFR, Artificial Intelligence, Diagnosis, Treatment
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.
