AI-driven radiomics and radiogenomics: supporting the assessment and differentiation of pseudoprogression in cellular immunotherapy for glioblastoma
Glioblastoma (GBM) is the most aggressive type of primary brain tumour in adults, and after treatment, it is also difficult to know whether a person’s health has improved. Pseudoprogression (PsP), particularly following radiotherapy and combination therapy with temozolomide and new immunotherapies, may be falsely identified as true progression (TP) by conventional MRI, thus leading to premature termination of treatment or unnecessary intensification of therapy. Although RANO, iRANO and RANO 2.0 have improved the assessment of response, structural MRI alone is unable to reveal the biological complexity of the tumour microenvironment. Artificial Intelligence (AI)-based radiomics and radiogenomics aid in the characterisation of GBM. Several Magnetic Resonance Imaging (MRI) sequences are used to obtain quantitative data, such as the conventional T1-weighted images, diffusion-weighted images, perfusion-weighted images and others, to acquire information on cell density, blood vessel distribution, immune cell concentration, molecular modifications and the effect of therapy. Combine imaging characteristics with liquid biopsy, genomic data and patient health records to enhance the accuracy of diagnosis and discover high-sensitivity surrogate markers for immune checkpoint inhibitor and CAR-T cell therapy clinical trials. Clinical translation still faces numerous limitations such as inconsistent research protocols, small study cohorts, insufficient external validation, inadequate model interpretability and inconsistent reference standards. In the future, many research groups will conduct multi-centre validation, standardize workflows, open-source reporting and release clinically interpretable models. The above ways can reduce the bias induced by PsP and improve differentiation between pseudoprogression and true progression to facilitate prompt treatment for most people.