Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Novel Hybrid Peptide ML-2 with Antibacterial and Antibiofilm Activity Against Pseudomonas aeruginosa.

Current microbiology·2026

Same author

Microalloying Strategies Enable Heat-Resistant Aluminum Alloys via Microstructural Design at Atomic Length Scale.

Advanced materials (Deerfield Beach, Fla.)·2026

Same author

Microenvironmental response-based treatment of osteoarthritis is a highly effective and durable program: a review.

Journal of orthopaedic surgery and research·2025

Same author

Influencing factors and dynamic changes of COVID-19 vaccine hesitancy in China: From the perspective of machine learning analysis.

Human vaccines & immunotherapeutics·2025

Same author

The application and challenges of antimicrobial drug-loaded scaffold materials for the treatment of bone infections.

Nanoscale horizons·2025

Same author

Antimicrobial peptide Mt 5 inhibits human hepatocellular carcinoma cell HepG2 proliferation.

Biochemical and biophysical research communications·2024

Search research articles

Related Experiment Video

Updated: Feb 17, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

An explainable machine learning model for predicting bladder tumor aecurrence risk.

Shenghua Wu¹, Ying Wang¹, Jingbing He¹

¹Zhejiang Dinghai Hospital (Zhoushan Branch of Shanghai Ruijin Hospital), Zhoushan, Zhejiang, China.

Frontiers in Oncology

|February 16, 2026

Summary

This study developed an explainable XGBoost model to predict bladder tumor recurrence, achieving 99.4% accuracy. The model uses seven key features to aid clinicians in risk stratification and personalized patient surveillance.

Keywords:

LASSO regression XGBoost bladder neoplasms interpretability machine learning neoplasm recurrence risk assessment

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

An Orthotopic Bladder Cancer Model for Gene Delivery Studies

An Orthotopic Bladder Cancer Model for Gene Delivery Studies

Published on: December 1, 2013

Related Experiment Videos

Last Updated: Feb 17, 2026

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

An Orthotopic Bladder Cancer Model for Gene Delivery Studies

An Orthotopic Bladder Cancer Model for Gene Delivery Studies

Published on: December 1, 2013

Area of Science:

Urology
Oncology
Machine Learning in Medicine

Background:

Bladder cancer recurrence post-surgery is common.
Accurate prediction of recurrence remains a clinical challenge.

Purpose of the Study:

Develop and validate an explainable machine learning model.
Predict bladder tumor recurrence after surgical treatment.

Main Methods:

Retrospective cohort study of 504 bladder tumor patients.
Utilized LASSO regression for feature selection and evaluated 11 machine learning algorithms.
Assessed model performance using AUC, recall, accuracy, F1-score, precision, and NPV.

Main Results:

XGBoost model with seven features achieved an AUC of 0.994.
Key predictors included BMI, tumor diameter, morphology, smoking status, invasion signs, tumor number, and location.
SHAP analysis highlighted BMI and tumor diameter as primary predictive factors.

Conclusions:

The seven-feature XGBoost model accurately predicts bladder tumor recurrence.
Explainable AI approach aids in clinical risk stratification.
Facilitates individualized surveillance planning for bladder cancer patients.