Artificial Intelligence (AI) driven exploratory imaging biomarkers
Radiomics biomarkers have several advantages. The sensitive nature of radiomics analysis allows for early identification of treatment response, will help to identify the mode of action leading to a better understanding in the drug, stratification of responders vs non-responders and identification of the added effect in combination therapy.
Radiomics features can be linked to specific biomarkers, which could serve as a non-invasive patient selection tool, to allow for a faster patient recruitment process compared to traditional strategies using liquid or tissue biopsies.
The highly sensitive nature of a detailed radiomics evaluation on tumour response should provide a comprehensive understanding of the exact effect of the mRNA vaccines and has the potential to identify effective stratification of imaging biomarkers. Radiomics will collect the pre- and post-treatment imaging scans of all HPV+ cancer patients in the study and utilise specially designed software tools to detect the effect of the treatment by comparing the pre- and post-treatment scans. Results will be validated by correlating the quantitative measurements of the Radiomics evaluation to the clinical patient response or another biomarker were pre- and post-treatment measurements are available. Collating cancer imaging data from TIGER clinical studies and integrating this repository with relevant biomarker and T-cell response data, TIGER will substantially contribute to push forward on the identification of effective stratification imaging biomarkers or immune markers in cancer.