Imaging-based Biomarkers for Predicting and Evaluating Cancer Immunotherapy Response

Area of Science:

• Radiology and Imaging Science
• Oncology and Cancer Research
• Immunology and Immunotherapy

Background:

• Accurate patient selection is crucial for successful immunotherapy, as tumor microenvironments influence treatment permissiveness.
• Current biomarkers like programmed cell death ligand-1 (PD-L1) and microsatellite instability high/mismatch repair deficiency have limitations in predicting response.
• Integrating imaging with tumor immune environment characterization is essential for improved patient stratification.

Purpose of the Study:

• To explore advancements in imaging methodologies for characterizing tumor immune microenvironments.
• To evaluate the role of imaging in predicting patient response to cancer immunotherapy.
• To discuss the development of molecular imaging techniques for assessing immunotherapy targets.

Main Methods:

• Analysis of imaging data from immunotherapy responders and nonresponders using various modalities.
• Application of radiomics-based artificial intelligence for tumor microenvironment characterization.
• Development of molecular imaging probes to visualize immunotherapy targets like PD-L1 expression.

Main Results:

• Imaging criteria are being developed to predict patient response to immunotherapy.
• Radiomics and AI can characterize tumor microenvironments, predict immunotherapy response, and assess risks of immune-related adverse events.
• Molecular imaging techniques show promise in confirming target expression on tumors.

Conclusions:

• Advancements in imaging techniques are vital for defining tumor immunologic characteristics.
• Improved tumor characterization through imaging will enhance patient stratification for immunotherapy.
• This leads to better selection of patients who are more likely to benefit from immunotherapies.