Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Cancer Survival Analysis01:21

Cancer Survival Analysis

442
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
442

You might also read

Related Articles

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

Sort by
Same author

Future Directions: Artificial Intelligence and Digital Tools in Bladder Cancer Care.

The Urologic clinics of North America·2026
Same author

Large multicenter validation of urine RNA profile for urothelial carcinoma detection and surveillance.

The Journal of clinical investigation·2026
Same author

Clinical Utility of PROSTest: A Prospective Study Suggesting Reduction in Unnecessary MRI and Biopsy in Men Evaluated for Prostate Cancer.

Cancers·2026
Same author

CystoDS: a multiclass endoscopy image dataset for artificial intelligence-assisted bladder cancer detection.

Scientific data·2026
Same author

Field-effect-informed urine liquid biopsy for bladder cancer.

Cell·2026
Same author

Population-adjusted network meta-analyses provide new insights into the efficacy of treatment alternatives for metastatic castration-sensitive prostate cancer.

Journal of comparative effectiveness research·2026
Same journal

RETRACTED: Sabir et al. DNA Based and Stimuli-Responsive Smart Nanocarrier for Diagnosis and Treatment of Cancer: Applications and Challenges. <i>Cancers</i> 2021, <i>13</i>, 3396.

Cancers·2026
Same journal

Correction: Adeluola et al. Chemoprevention of 4-NQO-Induced Oral Cancer by the Combination of Resveratrol and EGCG: In Vivo, In Silico and In Vitro Studies. <i>Cancers</i> 2026, <i>18</i>, 1098.

Cancers·2026
Same journal

Correction: Peñalver et al. Guidelines for Diagnosis, Treatment, and Follow-Up of Patients with Follicular Lymphoma-Spanish Lymphoma Group (GELTAMO) 2026. <i>Cancers</i> 2026, <i>18</i>, 395.

Cancers·2026
Same journal

Correction: Accorsi Buttini et al. Development of a Simplified Geriatric Score-4 (SGS-4) to Predict Outcomes After Allogeneic Hematopoietic Stem Cell Transplantation in Patients Aged over 50. <i>Cancers</i> 2025, <i>17</i>, 3278.

Cancers·2026
Same journal

Age-Stratified Long-Term Outcomes of Immune Checkpoint Inhibitors for Stage IV Melanoma and NSCLC in The Netherlands: A Population-Based Study.

Cancers·2026
Same journal

Targeting Ferroptosis in Glioblastoma: Molecular Mechanisms, Tumor Microenvironment, and Therapeutic Opportunities.

Cancers·2026
See all related articles

Related Experiment Video

Updated: Sep 5, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

216

Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.

Okyaz Eminaga1, Eugene Shkolyar1, Bernhard Breil2

  • 1Department of Urology, Stanford University School of Medicine, Stanford, CA 94305, USA.

Cancers
|July 9, 2022
PubMed
Summary
This summary is machine-generated.

This study developed an AI-driven prognostic model for urologic cancers using the SEER database. The model accurately predicts cancer-specific mortality, aiding personalized patient risk assessment and surveillance management.

Keywords:
artificial intelligencedata-driven solutionmachine learningsurveillance managementsurvival modelingurologic cancers

More Related Videos

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

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

188
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

373

Related Experiment Videos

Last Updated: Sep 5, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
05:33

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

216
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

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

188
Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence
08:05

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

373

Area of Science:

  • Oncology
  • Medical Informatics
  • Biostatistics

Background:

  • Accurate prognostication is crucial for stratifying patients with urologic cancers.
  • Risk profiling aids in clinical decision-making and treatment planning.

Purpose of the Study:

  • To develop and validate an artificial intelligence (AI) model for predicting cancer-specific mortality in urologic cancers.
  • To assess the model's accuracy and identify optimal follow-up durations for patient surveillance.

Main Methods:

  • Utilized the SEER national cancer registry database (approx. 2 million patients).
  • Developed a prognostic model using machine learning algorithms and clinical parameters.
  • Validated the model on an out-of-held test set, assessing fitness with Kaplan-Meier estimates and concordance index (c-index).
  • Conducted a simulation study to determine minimum follow-up duration and risk stability time points.

Main Results:

  • Achieved a well-calibrated prognostic model with a high c-index of 0.800 (95% CI: 0.795-0.805).
  • Simulation identified optimal follow-up durations varying by tumor stage and affected organs.
  • Determined time points for reliable risk stability assessment.

Conclusions:

  • AI-powered personalized temporal survival estimation is feasible for urologic cancers.
  • The developed model shows potential for enhancing clinical settings, particularly in surveillance management.
  • This approach can lead to more tailored patient care and monitoring strategies.