Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy
This work addresses a critical adverse effect in cancer treatment for clinicians, though it appears incremental as it builds on existing knowledge of cytokine similarities.
The paper tackled the problem of detecting cytokine release syndrome (CRS) during CAR-T cell therapy by developing a meta-review informed machine learning method that analyzes cytokine profiles, resulting in interpretable and effective identification of CRS onset using real-world clinical data.
Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising results in cancer treatment. When emerging, CRS could be identified by the analysis of specific cytokine and chemokine profiles that tend to exhibit similarities across patients. In this paper, we exploit these similarities using machine learning algorithms and set out to pioneer a meta--review informed method for the identification of CRS based on specific cytokine peak concentrations and evidence from previous clinical studies. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. During evaluation with real--world CRS clinical data, we emphasize the potential of our proposed method of producing interpretable results, in addition to being effective in identifying the onset of cytokine storm.