0130 Utilizing self-supervised machine learning to improve risk stratification in cutaneous squamous cell carcinoma
Year: 2025
Type: article
Abstract: Staging systems for cutaneous squamous cell carcinoma (cSCC) often fail to reliably predict poor outcomes. Up to 40% of poor-outcome cases are classified within low-risk groups (BWH), while high-risk cSCC cases show a 46% upstaging rate1,2. We developed the rank-aware contextual reasoning (RACR) multiple instance learning (MIL) approach to predict metastasis risk and identify novel histological phenotypes3. Our approach combines self-supervised histological features with hierarchical graph networks to capture the tumor microenvironment and structural context.
Authors: A. Choudhary, Z. Leibovit-Reiben, A.L. Stockard, A. Hwang, J. Kechter, K. Saboo, N. Comfere, S. Nelson, E. Johnson, L. Swanson, O. Sokumbi, P. Arnold, J. Canueto, D. DiCaudo, R. Iyer, A.R. Mangold
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,800
OpenAlex ID: W4412515876