ArticleFrontiers in oncology2021
Personally Tailored Survival Prediction of Patients With Follicular Lymphoma Using Machine Learning Transcriptome-Based Models.
Article in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Article
- Survival Machine Learning Methods Improve Prediction of Histologic Transformation in Follicular and Marginal Zone Lymphomas.Cancers · 2025Article
- Unraveling the complexity of follicular lymphoma: insights and innovations.American journal of cancer research · 2024Review
- Evaluation of 4 prognostic indices in follicular lymphoma treated in first line with immunochemotherapy.Blood advances · 2023Article
- HTGQC and shinyHTGQC: an R package and shinyR application for quality controls of HTG EDGE-seq protocols.GigaByte (Hong Kong, China) · 2022Article
Corrections and comments
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Authors and funding
15 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Follicular Lymphoma (FL) has a 10-year mortality rate of 20%, and this is mostly related to lymphoma progression and transformation to higher grades. In the era of personalized medicine it has become increasingly important to provide patients with an optimal prediction about their expected outcomes. The objective of this work was to apply machine learning (ML) tools on gene expression data in order to create individualized predictions about survival in patients with FL. Using data from two different studies, we were able to create a model which achieved good prediction accuracies in both cohorts (c-indexes of 0.793 and 0.662 in the training and test sets). Integration of this model with m7-FLIPI and age rendered high prediction accuracies in the test set (cox c-index 0.79), and a simplified approach identified 4 groups with remarkably different outcomes in terms of survival. Importantly, one of the groups comprised 27.35% of patients and had a median survival of 4.64 years. In summary, we have created a gene expression-based individualized predictor of overall survival in FL that can improve the predictions of the m7-FLIPI score.
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Registered trials
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