Predictive models for immune checkpoint inhibitor response in cancer: A review of current approaches and future...

Checkpoint inhibitors have revolutionised cancer treatment, yet only 20-30 % of patients achieve durable responses, highlighting the critical need for predictive models. This review focuses on PD-1/PD-L1 pathway inhibitors as monotherapy, examining current prediction frameworks spanning biomarker-based approaches, multi-omics integration, mathematical modelling, and artificial intelligence applications. Recent advances include SCORPIO and LORIS machine learning systems demonstrating superior statistical performance compared to traditional biomarkers, with area under curve values of 0.763. However, critical analysis reveals significant limitations in external validation across diverse healthcare settings, with many promising models failing to maintain performance outside their development institutions. Traditional pathological assessment by expert pathologists, including standardised PD-L1 scoring and tumour-infiltrating lymphocyte quantification, continues to form the foundation of clinical decision-making and provides essential validation for emerging AI approaches. Despite extensive research, established biomarkers show limited predictive accuracy, with PD-L1 demonstrating predictive value in only 28.9 % of FDA approvals. Multi-feature models incorporating genomic and clinical data show improved accuracy but face substantial validation challenges. Integration of spatial biomarkers and digital pathology has enhanced capabilities, achieving area under curve values of 0.84 in select studies. The most critical challenge is the "validation gap", many models show excellent single-institution performance but fail external validation, limiting clinical translation. Current obstacles include inadequate standardisation, interpretability concerns, and healthcare system integration difficulties. Future directions must prioritise rigorous multi-institutional validation studies, development of clinically implementable frameworks, and addressing practical deployment challenges to realise precision immunotherapy's potential.