AI-Based Clinical Rule Discovery for NMIBC Recurrence through Tsetlin Machines
This provides a more accurate and transparent decision-support tool for clinicians managing NMIBC recurrence, addressing the unreliability of current clinical tools.
The paper tackled the problem of predicting recurrence in non-muscle-invasive bladder cancer (NMIBC) by developing an interpretable AI model using Tsetlin Machines, achieving an F1-score of 0.80, which outperformed existing methods like XGBoost (0.78) and EORTC risk tables (0.42).
Bladder cancer claims one life every 3 minutes worldwide. Most patients are diagnosed with non-muscle-invasive bladder cancer (NMIBC), yet up to 70% recur after treatment, triggering a relentless cycle of surgeries, monitoring, and risk of progression. Clinical tools like the EORTC risk tables are outdated and unreliable - especially for intermediate-risk cases. We propose an interpretable AI model using the Tsetlin Machine (TM), a symbolic learner that outputs transparent, human-readable logic. Tested on the PHOTO trial dataset (n=330), TM achieved an F1-score of 0.80, outperforming XGBoost (0.78), Logistic Regression (0.60), and EORTC (0.42). TM reveals the exact clauses behind each prediction, grounded in clinical features like tumour count, surgeon experience, and hospital stay - offering accuracy and full transparency. This makes TM a powerful, trustworthy decision-support tool ready for real-world adoption.