ArticleScientific reports2023
Long-term survival and second malignant tumor prediction in pediatric, adolescent, and young adult cancer survivors using Random Survival Forests: a SEER analysis.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- Digital Twin models to address long-term treatment toxicities in children and young adults with cancer.NPJ digital medicine · 2026Review
- Development and validation of machine learning models for predicting cancer-specific survival in colorectal signet ring cell carcinoma.BMC gastroenterology · 2026Article
- Ovarian cancer recurrence prediction: comparing confirmatory to real-world predictors with machine learning.ESMO real world data and digital oncology · 2026Article
- Predicting time-to-first cancer diagnosis across multiple cancer types.Scientific reports · 2025Article
- Balancing between A and I.Acta oncologica (Stockholm, Sweden) · 2025Article
- Prediction of mortality in patients with retinoblastoma based on random survival forest: a retrospective cohort analysis using SEER database.Translational cancer research · 2025Article
- AI-Powered Clinical Decision Support Systems in Disease Diagnosis, Treatment Planning, and Prognosis: A Systematic Review.Medical journal of the Islamic Republic of Iran · 2025Review
- Creating an interactive database for nasopharyngeal carcinoma management: applying machine learning to evaluate metastasis and survival.Frontiers in oncology · 2024Article
- Development and visualization of a risk prediction model for metabolic syndrome: a longitudinal cohort study based on health check-up data in China.Frontiers in nutrition · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Survival and second malignancy prediction models can aid clinical decision making. Most commonly, survival analysis studies are performed using traditional proportional hazards models, which require strong assumptions and can lead to biased estimates if violated. Therefore, this study aims to implement an alternative, machine learning (ML) model for survival analysis: Random Survival Forest (RSF). In this study, RSFs were built using the U.S. Surveillance Epidemiology and End Results to (1) predict 30-year survival in pediatric, adolescent, and young adult cancer survivors; and (2) predict risk and site of a second tumor within 30 years of the first tumor diagnosis in these age groups. The final RSF model for pediatric, adolescent, and young adult survival has an average Concordance index (C-index) of 92.9%, 94.2%, and 94.4% and average time-dependent area under the receiver operating characteristic curve (AUC) at 30-years since first diagnosis of 90.8%, 93.6%, 96.1% respectively. The final RSF model for pediatric, adolescent, and young adult second malignancy has an average C-index of 86.8%, 85.2%, and 88.6% and average time-dependent AUC at 30-years since first diagnosis of 76.5%, 88.1%, and 99.0% respectively. This study suggests the robustness and potential clinical value of ML models to alleviate physician burden by quickly identifying highest risk individuals.
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