ArticleScientific reports2019
Automated tumour budding quantification by machine learning augments TNM staging in muscle-invasive bladder cancer prognosis.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- AI-based modeling of treatment decisions in benign prostatic hyperplasia: a transformer-based comparative study.BMC medical informatics and decision making · 2026Article
- Distance-based evaluation of tumor budding in colorectal cancer.Virchows Archiv : an international journal of pathology · 2026Article
- Graph Neural Network-Based Multi-Scale Whole Slide Image Fusion for pT Staging of Muscle-Invasive Bladder Cancer.Cancer science · 2026Article
- CDX2 expression dynamics in tumor clusters: a morpho-molecular biomarker in rectal cancer pretreatment biopsies revealed by sequential immunofluorescence.Scientific reports · 2026Article
- Evaluation of inflammatory markers in survival analysis of patients undergoing radical cystectomy using machine learning.World journal of urology · 2025Article
- Survival Prediction in Stomach Cancer with Deep Learning: Unveiling Model Decisions with LIME and SHAP.Asian Pacific journal of cancer prevention : APJCP · 2025Article
- Leveraging immuno-fluorescence data to reduce pathologist annotation requirements in lung tumor segmentation using deep learning.Scientific reports · 2024Article
- Development and validation of a deep learning model for predicting postoperative survival of patients with gastric cancer.BMC public health · 2024Article
- Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning.Frontiers in immunology · 2024Article
- Artificial Intelligence in Digital Pathology for Bladder Cancer: Hype or Hope? A Systematic Review.Cancers · 2023Review
- Recent Advancements in Deep Learning Using Whole Slide Imaging for Cancer Prognosis.Bioengineering (Basel, Switzerland) · 2023Review
- Which data subset should be augmented for deep learning? a simulation study using urothelial cell carcinoma histopathology images.BMC bioinformatics · 2023Article
- Global research trends of the application of artificial intelligence in bladder cancer since the 21st century: a bibliometric analysis.Frontiers in oncology · 2023Article
- EPDR1 levels and tumor budding predict and affect the prognosis of bladder carcinoma.Frontiers in oncology · 2022Article
- Prognostic Impact of Tumor Budding in Intrahepatic Cholangiocellular Carcinoma.Journal of Cancer · 2022Article
- [Research status and prospect of artificial intelligence technology in the diagnosis of urinary system tumors].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- Assessment of Immunological Features in Muscle-Invasive Bladder Cancer Prognosis Using Ensemble Learning.Cancers · 2021Article
- Tumour budding in solid cancers.Nature reviews. Clinical oncology · 2021Review
- Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.World journal of urology · 2020Review
- Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk.PloS one · 2020Article
Corrections and comments
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6 authors.
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Abstract
Tumour budding has been described as an independent prognostic feature in several tumour types. We report for the first time the relationship between tumour budding and survival evaluated in patients with muscle invasive bladder cancer. A machine learning-based methodology was applied to accurately quantify tumour buds across immunofluorescence labelled whole slide images from 100 muscle invasive bladder cancer patients. Furthermore, tumour budding was found to be correlated to TNM (p = 0.00089) and pT (p = 0.0078) staging. A novel classification and regression tree model was constructed to stratify all stage II, III, and IV patients into three new staging criteria based on disease specific survival. For the stratification of non-metastatic patients into high or low risk of disease specific death, our decision tree model reported that tumour budding was the most significant feature (HR = 2.59, p = 0.0091), and no clinical feature was utilised to categorise these patients. Our findings demonstrate that tumour budding, quantified using automated image analysis provides prognostic value for muscle invasive bladder cancer patients and a better model fit than TNM staging.
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