ArticleEuropean journal of radiology artificial intelligence2026
A two-stage foundation model for bladder tumor segmentation: An international multi-site study
Article in European journal of radiology artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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