ArticleCancers2022
Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI predicting recurrence in non-muscle-invasive bladder cancer: systematic review with study strengths and weaknesses.Frontiers in oncology · 2024Pooled it
- SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.International journal of surgery (London, England) · 2026Article
- Survival prediction for bladder cancer using multimodal data with quantum neural networks and transformer architectures.Scientific reports · 2026Article
- Graph Neural Network-Based Multi-Scale Whole Slide Image Fusion for pT Staging of Muscle-Invasive Bladder Cancer.Cancer science · 2026Article
- Quantum-enhanced multimodal prognostic transformer for skin disease progression prediction and visualization.Scientific reports · 2026Article
- Machine Learning in Biomarker-Driven Precision Oncology: Automated Immunohistochemistry Scoring and Emerging Directions in Genitourinary Cancers.Current oncology (Toronto, Ont.) · 2026Review
- Explainable Computational Imaging for Precision Oncology: An Interpretable Deep Learning Framework for Bladder Cancer Histopathology Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
- LncRNANon-coding RNA research · 2025Article
- Artificial intelligence-driven digital pathology in urological cancers: current trends and future directions.Prostate international · 2025Review
- Self-contrastive weakly supervised learning framework for prognostic prediction using whole slide images.PLOS digital health · 2025Article
- Deep Learning Predicts Lymphovascular Invasion Status in Muscle Invasive Bladder Cancer Histopathology.Annals of surgical oncology · 2025Article
- Automatic ovarian follicle detection using object detection models.Scientific reports · 2024Article
- Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN.Scientific reports · 2024Article
- NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.Cancer medicine · 2024Article
- Article
- Translation of tissue-based artificial intelligence into clinical practice: from discovery to adoption.Oncogene · 2023Review
- Artificial Intelligence Reveals Distinct Prognostic Subgroups of Muscle-Invasive Bladder Cancer on Histology Images.Cancers · 2023Article
- Article
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
18 authors.
Funding
Abstract
(1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.
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