ArticleCancers2022
Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 13 citations in OpenAlex.
- Prediction of intensive care unit requirement and in-hospital mortality in Fournier's gangrene: a comparative analysis of conventional statistical and machine learning models.Updates in surgery · 2026Article
- Big Data Analytics in Urologic Oncology: A Comprehensive Review of Large-scale Database Research and Clinical Applications.Current urology reports · 2026Review
- Explainable machine learning predicts overall survival in female bladder cancer patients after radical cystectomy.Discover oncology · 2025Article
- Machine learning-based construction of an oxidative stress-related model reveals molecular subtypes and tumor microenvironment infiltration signatures in colon cancer.Discover oncology · 2025Article
- A Scoping Review on Calibration Methods for Cancer Simulation Models.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Personalized Biopsy Schedules Using an Interval-Censored Cause-Specific Joint Model.Statistics in medicine · 2025Article
- Using machine learning for predicting cancer-specific mortality in bladder cancer patients undergoing radical cystectomy: a SEER-based study.BMC cancer · 2025Article
- Clinical decision system for renal cell carcinoma integrating interpretable machine learning algorithms.Frontiers in surgery · 2025Article
- The Present and Future of Artificial Intelligence in Urological Cancer.Journal of clinical medicine · 2023Review
- An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study.Cancers · 2022Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrognostication is essential to determine the risk profile of patients with urologic cancers.
methodsWe utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability.
resultsWe achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable.
conclusionsA personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.
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