Ulcerative colitis (UC) is a chronic disease characterized by inflammation. Despite the advent of anti-inflammatory biologics, most patients do not achieve a deep response to therapy. Furthermore, predicting response to therapy is challenging [1].
Quantitative histologic features of the inflammatory microenvironment in UC have potential to inform treatment selection and maintenance efficacy once a regimen has started.
To address this hypothesis, we used a machine learning (ML) approach to quantify histologic features of post-induction biopsies from patients with UC treated with adalimumab (ADA). We examined the association between these post-induction features and end-of-maintenance endoscopic response status (Mayo endoscopic score; MES<2).
Conference
UEG 2024
Partner
AbbVie
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