Immunotherapy has significantly advanced cancer treatment by providing durable clinical benefits and improved patient survival. Despite these successes, challenges remain as a considerable number of tumors exhibit resistance due to immune evasion mechanisms and the immunosuppressive characteristics intrinsic to the tumor microenvironment. Particularly, "cold" tumors, characterized by insufficient immune cell infiltration, often demonstrate limited response to immunotherapy. Immunoradiotherapy, combining the immunostimulatory effects of radiotherapy with therapeutic immune checkpoint inhibitors, emerges as an effective strategy addressing these limitations. Recent studies confirm that radiotherapy can promote antigen release, amplify antigen presentation, enhance T cell infiltration and activation, and stimulate systemic anti-tumor immune responses (the "abscopal effect"). However, due to significant variability between patients and tumor heterogeneity, clinical implementation and optimization of immunoradiotherapy remain challenging and require personalized, data-driven approaches.
This Research Topic aims to investigate and highlight the critical roles of artificial intelligence (AI) technologies in optimizing immunoradiotherapy approaches, with an emphasis on enhancing precision oncology practices for advanced malignant tumors. Efforts will focus on addressing key research questions: How can AI and radiomics predict individual patient responsiveness to immunoradiotherapy? Can deep learning approaches facilitate real-time monitoring of tumor immune dynamics and inform adaptive treatment strategies? How can multi-omics analyses driven by AI contribute to predictive biomarker identification and validation, thus improving patient stratification and outcomes? Answering these questions demands the integration of multidisciplinary insights to refine and personalize treatment, mitigate toxicities, and align therapeutic interventions with specific patient profiles.
To gather further insights within the intersection of AI advancements and immunoradiotherapy applications in advanced malignant tumors, we welcome articles addressing, but not limited to, the following themes:
1. AI-assisted radiomics and imaging biomarker discovery for predictive and prognostic purposes in immunoradiotherapy
2. Deep learning frameworks to accurately predict patient treatment response and optimize therapeutic regimens
Integration of multi-omics (genomics, transcriptomics, and proteomics) with AI techniques for biomarker identification and validation
3. AI-driven approaches for optimizing radiotherapy parameters, including dosing, fractionation, and adaptive treatment designs
4. Clinical applications and validation of AI-driven predictive models and decision support systems in everyday oncology practice
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.
Immunotherapy has significantly advanced cancer treatment by providing durable clinical benefits and improved patient survival. Despite these successes, challenges remain as a considerable number of tumors exhibit resistance due to immune evasion mechanisms and the immunosuppressive characteristics intrinsic to the tumor microenvironment. Particularly, "cold" tumors, characterized by insufficient immune cell infiltration, often demonstrate limited response to immunotherapy. Immunoradiotherapy, combining the immunostimulatory effects of radiotherapy with therapeutic immune checkpoint inhibitors, emerges as an effective strategy addressing these limitations. Recent studies confirm that radiotherapy can promote antigen release, amplify antigen presentation, enhance T cell infiltration and activation, and stimulate systemic anti-tumor immune responses (the "abscopal effect"). However, due to significant variability between patients and tumor heterogeneity, clinical implementation and optimization of immunoradiotherapy remain challenging and require personalized, data-driven approaches.
This Research Topic aims to investigate and highlight the critical roles of artificial intelligence (AI) technologies in optimizing immunoradiotherapy approaches, with an emphasis on enhancing precision oncology practices for advanced malignant tumors. Efforts will focus on addressing key research questions: How can AI and radiomics predict individual patient responsiveness to immunoradiotherapy? Can deep learning approaches facilitate real-time monitoring of tumor immune dynamics and inform adaptive treatment strategies? How can multi-omics analyses driven by AI contribute to predictive biomarker identification and validation, thus improving patient stratification and outcomes? Answering these questions demands the integration of multidisciplinary insights to refine and personalize treatment, mitigate toxicities, and align therapeutic interventions with specific patient profiles.
To gather further insights within the intersection of AI advancements and immunoradiotherapy applications in advanced malignant tumors, we welcome articles addressing, but not limited to, the following themes:
1. AI-assisted radiomics and imaging biomarker discovery for predictive and prognostic purposes in immunoradiotherapy
2. Deep learning frameworks to accurately predict patient treatment response and optimize therapeutic regimens
Integration of multi-omics (genomics, transcriptomics, and proteomics) with AI techniques for biomarker identification and validation
3. AI-driven approaches for optimizing radiotherapy parameters, including dosing, fractionation, and adaptive treatment designs
4. Clinical applications and validation of AI-driven predictive models and decision support systems in everyday oncology practice
Please note: Manuscripts consisting solely of bioinformatics, computational analysis, or predictions of public databases which are not accompanied by validation (independent clinical or patient cohort, or biological validation in vitro or in vivo, which are not based on public databases) are not suitable for publication in this journal.