ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2021
On Predicting Recurrence in Early Stage Non-small Cell Lung Cancer.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Treatment Patterns in Early Stage NSCLC in Asia: Findings From the ELEVATE Study.JTO clinical and research reports · 2026Article
- Opportunities and challenges in lung cancer care in the era of large language models and vision language models.Translational lung cancer research · 2025Review
- Decoding Recurrence in Early-Stage and Locoregionally Advanced Non-Small Cell Lung Cancer: Insights From Electronic Health Records and Natural Language Processing.JCO clinical cancer informatics · 2025Article
- Diagnostic and Prognostic Value of hsa_piR_022710, hsa_piR_019822, and hsa_piR_020840 in Early-Stage Non-Small-Cell Lung Cancer: Implications for Recurrence and Survival in Squamous Cell Carcinoma Patients.International journal of molecular sciences · 2025Article
- Artificial neural network-augmented dosiomic integration for predicting distant recurrence in NSCLC patients treated with SBRT.Frontiers in oncology · 2025Article
- Predictors of disease recurrence after radical resection and adjuvant chemotherapy in patients with stage IIb-IIIa squamous cell lung cancer: A retrospective analysis.World journal of experimental medicine · 2024Article
- Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine.Cancer informatics · 2024Article
- Comparison of deep learning-based recurrence-free survival with random survival forest and Cox proportional hazard models in Stage-I NSCLC patients.Cancer medicine · 2023Article
- Machine Learning-Assisted Recurrence Prediction for Patients With Early-Stage Non-Small-Cell Lung Cancer.JCO clinical cancer informatics · 2023Article
- Clinical factors influencing long-term survival in a real-life cohort of early stage non-small-cell lung cancer patients in Spain.Frontiers in oncology · 2023Article
- An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study.Cancers · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection and mitigation of disease recurrence in non-small cell lung cancer (NSCLC) patients is a nontrivial problem that is typically addressed either by rather generic follow-up screening guidelines, self-reporting, simple nomograms, or by models that predict relapse risk in individual patients using statistical analysis of retrospective data. We posit that machine learning models trained on patient data can provide an alternative approach that allows for more efficient development of many complementary models at once, superior accuracy, less dependency on the data collection protocols and increased support for explainability of the predictions. In this preliminary study, we describe an experimental suite of various machine learning models applied on a patient cohort of 2442 early stage NSCLC patients. We discuss the promising results achieved, as well as the lessons we learned while developing this baseline for further, more advanced studies in this area.
Indexed as
Identifiers
35308971PMC8861763W4287509026What OpenQuestion holds
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.