SynthesisCancer medicine2025
A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.
Synthesis in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on Machine Learning Techniques for Survival Analysis in Cancer.Cancer medicine · 2025Pooled it
- Interpretable Machine Learning Framework for Predicting Major Adverse Cardiovascular Events in Rheumatoid Arthritis Using Electronic Health Records: Multicenter Cohort Study.JMIR formative research · 2026Article
- Artificial intelligence models for survival prediction in colorectal cancer: a systematic review of time-to-event approaches.Annals of medicine and surgery (2012) · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Machine Learning-Derived Risk Groups and Clinical Implementation of Survival Prediction in Lung Cancer: Evidence from a Kazakh National Cohort.Diagnostics (Basel, Switzerland) · 2026Article
- The obesity paradox effect of triglyceride-glucose-body mass index (TyG-BMI) on all-cause mortality in patients with malignant tumors: a machine learning-driven cohort study.Translational cancer research · 2026Article
- Identifying Prognostic Factors in Brain Metastasis Patients Using MRI Morphological Features: A Machine Learning and Survival Analysis Approach.Diagnostics (Basel, Switzerland) · 2026Article
- Prediction of overall survival in primary lung adenocarcinoma using machine learning and deep learning: a cohort study in Latin America country cancer center.Frontiers in oncology · 2026Article
- Risk stratification for adjuvant radiotherapy in pathologic T3N0 rectal cancer using a DeepSurv-based survival model.Frontiers in oncology · 2026Article
- Pharmacoeconomic evaluation of first-line tislelizumab for extensive-stage small cell lung cancer using a comparative validation of traditional survival and machine learning models.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundMachine learning (ML) offers opportunities to overcome limitations of conventional survival analyses, which are commonly found in cancer studies. However, the choice and levels of performance of ML methods vary across studies. As a result, it is unclear whether they consistently outperform traditional statistical methods and whether one particular ML strategy may outperform others for survival analysis. The present study aimed to systematically review the literature around this emerging topic.
methodsThis systematic review was conducted using the PRISMA guidelines. Electronic databases were systematically searched using keywords related to Machine Learning, Survival Analysis and Cancer. RESULTS &
conclusionThe review included 196 studies, from which 39 comparable studies were used for the analysis. Improved predictive performance was seen from the use of ML in almost all cancer types. Predictive performance of the different ML methods varied across cancer types, and multi-task and deep learning methods appeared to yield superior performance; however, it was reported in only a minority of papers. This study also highlighted great variability in both methodologies and their implementations.
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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.