Emmanuel Oisakede, Academic Clinical Fellow Resident at Leeds Teaching Hospitals NHS Trust, shared a post on LinkedIn about a recent article he and his colleagues co-authored, adding:
“Fresh off the press!
Excited to share our latest publication in a Q1 Journal of Oncology/Hematology
Predictive models for immune checkpoint inhibitor response in cancer: A review of current approaches and future directions.’
As we continue advancing toward personalised cancer medicine, it’s crucial to fully harness the potential of immune checkpoint inhibitors. In this timely review, we take a deep dive into the current predictive models, including LORIS and SCORPIO and explore how emerging tools are reshaping cancer immunotherapy.
Highlights:
- SCORPIO and LORIS models outperform traditional single-biomarker methods.
- AI enhances prediction using clinical, genomic, and spatial biomarker data.
- Deep learning boosts PD-L1 scoring and immune cell profiling accuracy.
- Multi-modal frameworks achieve AUC values above 0.85 in some cancers.
A huge congratulations to the entire team for this effort and collaboration!”
Title: Predictive models for immune checkpoint inhibitor response in cancer: A review of current approaches and future directions
Authors: Emmanuel O. Oisakede, Oluwatosin Akinro, Oluwakemi Jumoke Bello, Claret Chinenyenwa Analikwu, Eghosasere Egbon, David B. Olawade
Read the Full Article on Critical Reviews in Oncology/Hematology
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