Lung cancer is a significant global health challenge, continuing to be the leading cause of cancer-related mortality worldwide. It contributes to approximately 18.7% of cancer-related deaths, with future incidence expected to surge by about 65% over the next two decades. Advances in diagnostic imaging techniques such as positron emission tomography (PET), computed tomography (CT), and magnetic resonance imaging (MRI) have provided more accurate disease prognostication. Despite progress in conventional treatment modalities, substantial challenges persist in effectively treating and managing lung cancer due to the disease's complexity and variability.

Recent breakthroughs in immunotherapy, particularly immune checkpoint inhibitors (ICIs) targeting PD-1, PD-L1, and CTLA-4, have reshaped treatment paradigms, offering hope to patients with limited treatment options. These therapies, in conjunction with radioimmunotherapy, are under investigation for their potential to deliver personalized and durable responses. In addition, advancements in computational oncology have facilitated the development of predictive models that are capable of integrating and analyzing multi-dimensional data. Machine learning and deep learning–based frameworks, radiomic analysis from imaging, epigenetic scoring systems (e.g., DNA methylation and m6A modification–related indices), and microbiome-derived predictors can enable more accurate risk stratification. Models incorporating clinical, radiological, and immunological variables can be explored for superior diagnostic and prognostic accuracy. However, the heterogeneity of tumors and the intricacies of the immune microenvironment pose significant hurdles. There remains an urgent need for the development of robust predictive and prognostic biomarkers, alongside advanced predictive and diagnostic models, to facilitate a personalized approach to lung cancer care.

This Research Topic aims to consolidate personalized immunotherapy and diagnostic innovations in lung cancer treatment and computational prognostic tools, with a focus on lung cancer. To gather further insights into personalized approaches to lung cancer treatment, we welcome articles addressing, but not limited to, the following themes:

• Innovations in immune checkpoint inhibitors and their application
• Integration of biomarker data into predictive algorithms using artificial intelligence and machine learning.
• Evaluation of diagnostic and predictive model performance in diverse patient populations and clinical settings
• Impact of tumor heterogeneity on treatment outcomes
• Strategies for overcoming resistance to current therapies

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Novel Immune Markers and Predictive Models for Diagnosis, Immunotherapy and Prognosis in Lung Cancer​​​​​​​

Novel Immune Markers and Predictive Models for Diagnosis, Immunotherapy and Prognosis in Lung Cancer