ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
A Machine Learning Approach for Long-Term Prognosis of Bladder Cancer based on Clinical and Molecular Features.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Risk Stratification Using a Perioperative Nomogram for Predicting the Mortality of Bladder Cancer Patients Undergoing Radical Cystectomy.Journal of clinical medicine · 2025Article
- Prediction of bladder cancer prognosis and immune microenvironment assessment using machine learning and deep learning models.Heliyon · 2024Article
- Artificial intelligence application in the diagnosis and treatment of bladder cancer: advance, challenges, and opportunities.Frontiers in oncology · 2024Review
- Bladder cancer prognosis using deep neural networks and histopathology images.Journal of pathology informatics · 2022Article
- Applications of artificial intelligence in urological setting: a hopeful path to improved care.Journal of exercise rehabilitation · 2021Review
- Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosis.Computational and structural biotechnology journal · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Improving the consistency and reproducibility of bladder cancer prognoses necessitates the development of accurate, predictive prognostic models. Current methods of determining the prognosis of bladder cancer patients rely on manual decision-making, including factors with high intra- and inter-observer variability, such as tumor grade. To advance the long-term prediction of bladder cancer prognoses, we developed and tested a computational model to predict the 10-year overall survival outcome using population-based bladder cancer data, without considering tumor grade classification. The resulted predictive model demonstrated promising performance using a combination of clinical and molecular features, and was also strongly related to patient overall survival in Cox models. Our study suggests that machine learning methods can provide reliable long-term prognoses for bladder cancer patients, without relying on the less consistent tumor grade. If validated in clinical trials, this automated approach could guide and improve personalized management and treatment for bladder cancer patients.
Identifiers
32477683PMC7233061What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.