Abstract #1399

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

Ki Choon Sim^1,2, Min Ju Kim^1,2, Deuk Jae Sung^1,2, Beom Jin Park^1,2, Na Yeon Han^1,2, Yeo Eun Han^1,2, Keewon Shin^2,3, and Taehun Kim^2,3

¹Radiology, Korea University Anam Hospital, Seoul, Korea, Republic of, ²Advanced Medical Imaging Institute, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea, Republic of, ³Artificial Intelligence, Korea University Anam Hospital, Seoul, Korea, Republic of

Synopsis

Keywords: Diagnosis/Prediction, Bladder

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.

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