ArticleInternational journal of computer assisted radiology and surgery2023
Deep learning-based overall survival prediction model in patients with rare cancer: a case study for primary central nervous system lymphoma.
Article in International journal of computer assisted radiology and surgery, 2023. 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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6 citing papers in PubMed, 13 citations in OpenAlex.
- Development and Performance Analysis of a Semi-Supervised Gait Recognition Model for Pediatric Abnormalities Using a Hybrid Dataset.Bioengineering (Basel, Switzerland) · 2026Article
- Data Augmentation and Synthetic Data Generation in Rare Disease Research: A Scoping Review.Medical sciences (Basel, Switzerland) · 2025Article
- Machine learning-based prognostic model for human immunodeficiency virus-associated cutaneous T-cell lymphoma: A Surveillance, Epidemiology, and End Results database analysis.The Journal of international medical research · 2025Article
- AI-Driven Innovations in Neuroradiology and Neurosurgery: Scoping Review of Current Evidence and Future Directions.Cancers · 2025Review
- Development and Validation of Survival Prediction Models for Patients With Pineoblastomas Using Deep Learning: A SEER-Based Study.Cancer reports (Hoboken, N.J.) · 2025Article
- MRI-Based Machine Learning for Prediction of Clinical Outcomes in Primary Central Nervous System Lymphoma.Life (Basel, Switzerland) · 2024Article
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposePrimary central nervous system lymphoma (PCNSL) is a rare, aggressive form of extranodal non-Hodgkin lymphoma. To predict the overall survival (OS) in advance is of utmost importance as it has the potential to aid clinical decision-making. Though radiomics-based machine learning (ML) has demonstrated the promising performance in PCNSL, it demands large amounts of manual feature extraction efforts from magnetic resonance images beforehand. deep learning (DL) overcomes this limitation.
methodsIn this paper, we tailored the 3D ResNet to predict the OS of patients with PCNSL. To overcome the limitation of data sparsity, we introduced data augmentation and transfer learning, and we evaluated the results using r stratified k-fold cross-validation. To explain the results of our model, gradient-weighted class activation mapping was applied.
resultsWe obtained the best performance (the standard error) on post-contrast T1-weighted (T1Gd)-area under curve [Formula: see text], accuracy [Formula: see text], precision [Formula: see text], recall [Formula: see text] and F1-score [Formula: see text], while compared with ML-based models on clinical data and radiomics data, respectively, further confirming the stability of our model. Also, we observed that PCNSL is a whole-brain disease and in the cases where the OS is less than 1 year, it is more difficult to distinguish the tumor boundary from the normal part of the brain, which is consistent with the clinical outcome.
conclusionsAll these findings indicate that T1Gd can improve prognosis predictions of patients with PCNSL. To the best of our knowledge, this is the first time to use DL to explain model patterns in OS classification of patients with PCNSL. Future work would involve collecting more data of patients with PCNSL, or additional retrospective studies on different patient populations with rare diseases, to further promote the clinical role of our model.
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