Early Detection of Bladder Cancer in MRI Using Deep Learning Segmentation and Rule-Based Classification: a Pilot study

Motivation: Bladder cancer is often diagnosed at advanced stages due to limited early detection methods, leading to poor patient outcomes. Goal(s): To evaluate the efficacy of deep learning segmentation combined with rule-based classification in the early detection of bladder cancer using MRI. Approach: A study using T2WI of MRI performed for bladder cancer, analyzed using nnU-Net for segmentation and a rule-based classification system based on segmented voxel count. A total of 260 MRI datasets, including 120 bladder cancer cases, were used. Results: The author's approach demonstrated a sensitivity of 86.4% and specificity of 95% in detecting early-stage bladder cancer (AUROC, up to 0.88). Impact: Improved early detection of bladder cancer will enable early treatment, which will have a positive impact on treatment outcomes.