ArticleJournal of pathology informatics2018
Machine Learning Provides an Accurate Classification of Diffuse Large B-Cell Lymphoma from Immunohistochemical Data.
Article in Journal of pathology informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence for Risk Stratification in Diffuse Large B-Cell Lymphoma: A Systematic Review of Classification Models and Predictive Performances.Medical sciences (Basel, Switzerland) · 2025Pooled it
- Role of artificial intelligence in haematolymphoid diagnostics.Histopathology · 2025Review
- Advancements in Hematologic Malignancy Detection: A Comprehensive Survey of Methodologies and Emerging Trends.TheScientificWorldJournal · 2025Review
- Development and validation of a hierarchical approach for lymphoma classification using immunohistochemical markers.Cancer medicine · 2024Article
- Artificial Intelligence-Assisted Diagnostic Cytology and Genomic Testing for Hematologic Disorders.Cells · 2023Review
- Iterated cross validation method for prediction of survival in diffuse large B-cell lymphoma for small size dataset.Scientific reports · 2023Article
- Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology.Cancers · 2020Review
- Statistical Challenges in Development of Prognostic Models in Diffuse Large B-Cell Lymphoma: Comparison Between Existing Models - A Systematic Review.Clinical epidemiology · 2020Review
Corrections and comments
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Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe classification of diffuse large B-cell lymphomas into Germinal Center (GCB) and non-GC subtypes defines disease subgroups which are different both in terms of gene expression and prognosis. Given their clinical significance, several classification algorithms have been designed, some by making use of widely availability immunohistochemical techniques. Despite their high concordance with gene expression profiles (GEP) and prognostic value, these algorithms were based on technical and biological assumptions that could be improved in terms of performance for classification.
methodsIn order to overcome this limitation, a new algorithm was obtained by analyzing a previously published dataset of 475 patients by using an automatic classification tree method.
resultsThe resulting algorithm classifies correctly 91.6% of the cases when compared to GEP, displaying a Receiver-Operator Characteristic (ROC) area under the curve of 0.934. Noteworthy features of this algorithm include the capability to classify GEP-unclassifiable cases and a significant prognostic value, both in terms of overall survival (60 months for non-GC vs not reached for GCB,
conclusionBy using a machine learning classification method that avoids most pre-assumptions, the novel algorithm obtained is accurate and maintains relevant features for clinical implementation.
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Registered trials
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