ArticleBiomedicines2024
Prediction of Non-Muscle Invasive Papillary Urothelial Carcinoma Relapse from Hematoxylin-Eosin Images Using Deep Multiple Instance Learning in Patients Treated with Bacille Calmette-Guérin Immunotherapy.
Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 5 citations in OpenAlex.
- Computational pathology model to predict recurrence-free survival in NMPUC patients on BCG-therapy.NPJ precision oncology · 2026Article
- Natural Molecule-Derived Nanogels from Hematoxylin and l-lysine for Biomedical Use with Antimicrobial Properties.International journal of molecular sciences · 2024Article
- Computational pathology in bladder cancer: A scoping review.Bladder cancer (Amsterdam, Netherlands)Review
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Authors and funding
6 authors at 1 institution in 1 country.
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Abstract
The limited reproducibility of the grading of non-muscle invasive papillary urothelial carcinoma (NMIPUC) necessitates the search for more robust image-based predictive factors. In a cohort of 157 NMIPUC patients treated with Bacille Calmette-Guérin (BCG) immunotherapy, we explored the multiple instance learning (MIL)-based classification approach for the prediction of 2-year and 5-year relapse-free survival and the multiple instance survival learning (MISL) framework for survival regression. We used features extracted from image patches sampled from whole slide images of hematoxylin-eosin-stained transurethral resection (TUR) NPMIPUC specimens and tested several patch sampling and feature extraction network variations to optimize the model performance. We selected the model showing the best patient survival stratification for further testing in the context of clinical and pathological variables. MISL with the multiresolution patch sampling technique achieved the best patient risk stratification (concordance index = 0.574,
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