Authors
Xiaoxuan Zhang, Hongyi Wang, Hui Zhou, Dan Xiao, Haoxiang Xiong, Jimin Cheng, Yingying He, Qianjin Feng, Jie Yang
Published in
iScience. Volume 29. Issue 7. Pages 116651. Jul 17, 2026. Epub Jul 06, 2026.
Abstract
Early pathological examination for grading and invasion of non-muscle-invasive bladder cancer (NMIBC) via transurethral resection of bladder tumor (TURBT) specimens is a labor-intensive, subjective, and experience-dependent task, while the poor quality of TURBT specimens and the high heterogeneity of NMIBC tumors further limit diagnostic accuracy and efficiency. This study proposed a dual-channel multi-instance learning (DCMIL) model to simultaneously integrate the grading and invasion of NMIBC, while efficiently locating minor changes in the morphology and distribution of NMIBC cells in parallel. Developed on a multicenter dataset of 1332 whole slide images (WSIs) from TURBT specimens, DCMIL demonstrated outstanding accuracy and robust performance with areas under the curve of 0.851-0.983. In the reader study, DCMIL-assisted interpretation improved the diagnostic accuracy by an average of 13.31%-18.58% for inexperienced pathologists. Overall, DCMIL holds promise as a reliable initial assessment tool of NMIBC via TURBT specimens to support clinical decision-making.
PMID:
42436972
Bibliographic data and abstract were imported from PubMed on 12 Jul 2026.
Read full publication at:
Please sign in
to see all details.
Advertisement
Stats
- Recommendations n/a n/a positive of 0 vote(s)
- Views 8
- Comments 0