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Updated: Sep 30, 2026
An Orthotopic Bladder Tumor Model and the Evaluation of Intravesical saRNA Treatment
Published on: July 28, 2012
A two-stage foundation model for bladder tumor segmentation: An international multi-site study☆
Muhammad Awais1, Oguz Akin2, Giorgio Brembilla3
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, USA.
Background And Objective:
Accurate bladder tumor segmentation is crucial for muscle invasion assessment, neoadjuvant therapy response evaluation, and bladder preservation decision-making. Manual delineation is time-consuming and observer-dependent, highlighting an unmet clinical need for accurate and automated segmentation, particularly in large multi-site studies. This study aims to (i) develop an automated MRI-based foundation model for bladder tumor segmentation (BLA-T-Seg) using a two-stage framework, in which segmentation is first constrained to the bladder wall, including wall-contiguous tumors and then refined in a second stage focused specifically on the tumor, and (ii) rigorously assess generalizability via multi-site, independent external validation across diverse cohorts.
Methods:
This retrospective study included T2-weighted MRI scans from 236 patients with bladder cancer across two international sites. Experienced radiologists manually annotated bladder and tumor regions of interest on T2-weighted images, which served as the ground truth. BLA-T-Seg was developed by adapting the Segment Anything Model (SAM), fine-tuned to segment the bladder in stage 1 and the tumor in stage 2. The primary performance metric was the Dice similarity coefficient (DSC), evaluated across five studies (S1-S3: internal; S4-S5: external) and benchmarked against 10 state-of-the-art (SOTA) segmentation architectures.
Results:
For stage 1, BLA-T-Seg achieved good-to-excellent performance, with mean DSC values ranging from 0.85 to 0.93. For stage 2, BLA-T-Seg demonstrated moderate-to-good performance, with mean DSC values of 0.74-0.81. Cross-site validation showed minimal performance variation (≤0.01-0.04), confirmed by radar plot analyses. Compared with SOTA models on subset S1, BLA-T-Seg achieved the highest DSC of 0.84, outperforming the next best-performing model, Swin Transformer (DSC: 0.56).
Conclusion:
BLA-T-Seg enables accurate and generalizable bladder wall and tumor segmentation and outperforms SOTA alternatives.
