ArticleDiagnostics (Basel, Switzerland)2022
Machine Learning in Prediction of Bladder Cancer on Clinical Laboratory Data.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 36 citations in OpenAlex.
- An efficient graph attention framework enhances bladder cancer prediction.Scientific reports · 2025Article
- Ensemble machine learning models for predicting bone metastasis in bladder cancer.Frontiers in oncology · 2025Article
- Point-Based Prediction Model for Bladder Cancer Risk in Diabetes: A Random Survival Forest-Guided Approach.Journal of clinical medicine · 2024Article
- Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach.Advances and applications in bioinformatics and chemistry : AABC · 2024Article
- A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Article
- Machine learning-based identification of colorectal advanced adenoma using clinical and laboratory data: a phase I exploratory study in accordance with updated World Endoscopy Organization guidelines for noninvasive colorectal cancer screening tests.Frontiers in oncology · 2024Article
- Predictive Value of the Total Bilirubin and CA50 Screened Based on Machine Learning for Recurrence of Bladder Cancer Patients.Cancer management and research · 2024Article
- Bladder cancer biomarkers: current approaches and future directions.Frontiers in oncology · 2024Review
- Machine learning identifies a 5-serum cytokine panel for the early detection of chronic atrophy gastritis patients.Cancer biomarkers : section A of Disease markers · 2024Article
- Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models.Cancer reports (Hoboken, N.J.) · 2023Article
- Screening for Osteoporosis from Blood Test Data in Elderly Women Using a Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2023Article
- Subtyping intractable functional constipation in children using clinical and laboratory data in a classification model.Frontiers in pediatrics · 2023Article
- Prediction of anticancer drug resistance using a 3D microfluidic bladder cancer model combined with convolutional neural network-based image analysis.Frontiers in bioengineering and biotechnology · 2023Article
- Predicting Recurrence of Non-Muscle-Invasive Bladder Cancer: Current Techniques and Future Trends.Cancers · 2022Review
- Clinlabomics: leveraging clinical laboratory data by data mining strategies.BMC bioinformatics · 2022Review
- Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach.Journal of personalized medicine · 2022Article
- Classification of retinoblastoma-1 gene mutation with machine learning-based models in bladder cancer.Heliyon · 2022Article
- Deep Learning in Cytopathology: The Potential for Multimodal Synergy of Clinical and Cytological Input Data.Journal of cytologyReview
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bladder cancer has been increasing globally. Urinary cytology is considered a major screening method for bladder cancer, but it has poor sensitivity. This study aimed to utilize clinical laboratory data and machine learning methods to build predictive models of bladder cancer. A total of 1336 patients with cystitis, bladder cancer, kidney cancer, uterus cancer, and prostate cancer were enrolled in this study. Two-step feature selection combined with WEKA and forward selection was performed. Furthermore, five machine learning models, including decision tree, random forest, support vector machine, extreme gradient boosting (XGBoost), and light gradient boosting machine (GBM) were applied. Features, including calcium, alkaline phosphatase (ALP), albumin, urine ketone, urine occult blood, creatinine, alanine aminotransferase (ALT), and diabetes were selected. The lightGBM model obtained an accuracy of 84.8% to 86.9%, a sensitivity 84% to 87.8%, a specificity of 82.9% to 86.7%, and an area under the curve (AUC) of 0.88 to 0.92 in discriminating bladder cancer from cystitis and other cancers. Our study provides a demonstration of utilizing clinical laboratory data to predict bladder cancer.
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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.