ArticleCancer medicine2016
Machine learning-based classification of diffuse large B-cell lymphoma patients by eight gene expression profiles.
Article in Cancer medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 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
- Automated FDG uptake/PET-CT fused scan diagnosis of various lymph node tumors using object detection AI techniques.Scientific reports · 2026Article
- Artificial intelligence for risk assessment and outcome prediction in malignant haematology.British journal of haematology · 2026Review
- Role of artificial intelligence in haematolymphoid diagnostics.Histopathology · 2025Review
- Assessment and prognostic significance of a serum cytokine panel in diffuse large B‑cell lymphoma.Oncology letters · 2024Article
- Diagnosis of acute myeloid leukaemia on microarray gene expression data using categorical gradient boosted trees.Heliyon · 2023Article
- The regulatory role of PDE4B in the progression of inflammatory function study.Frontiers in pharmacology · 2022Review
- Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy.Frontiers in cardiovascular medicine · 2022Article
- Role of Roflumilast Combined with ESHAP Chemotherapy in Relapsed/Refractory Patients with Diffuse Large B-Cell Lymphoma.Cancer research and treatment · 2022Article
- IQGAP2 acts as an independent prognostic factor and is related to immunosuppression in DLBCL.BMC cancer · 2021Article
- Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis.Biomolecules · 2021Article
- Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases.Clinical hematology international · 2021Review
- Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.Anesthesiology · 2020Article
- Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology.Cancers · 2020Review
- TIPE Regulates DcR3 Expression and Function by Activating the PI3K/AKT Signaling Pathway in CRC.Frontiers in oncology · 2020Article
- Cell-of-origin determined by both gene expression profiling and immunohistochemistry is the strongest predictor of survival in patients with diffuse large B-cell lymphoma.American journal of hematology · 2020Article
- Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer.Chinese medical journal · 2019Article
- Three inflammation-related genes could predict risk in prognosis and metastasis of patients with breast cancer.Cancer medicine · 2019Article
- TBX21 predicts prognosis of patients and drives cancer stem cell maintenance via the TBX21-IL-4 pathway in lung adenocarcinoma.Stem cell research & therapy · 2018Article
- Toll-like receptor 9 stimulation can induce IκBζ expression and IgM secretion in chronic lymphocytic leukemia cells.Haematologica · 2017Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Gene expression profiling (GEP) had divided the diffuse large B-cell lymphoma (DLBCL) into molecular subgroups: germinal center B-cell like (GCB), activated B-cell like (ABC), and unclassified (UC) subtype. However, this classification with prognostic significance was not applied into clinical practice since there were more than 1000 genes to detect and interpreting was difficult. To classify cancer samples validly, eight significant genes (MYBL1, LMO2, BCL6, MME, IRF4, NFKBIZ, PDE4B, and SLA) were selected in 414 patients treated with CHOP/R-CHOP chemotherapy from Gene Expression Omnibus (GEO) data sets. Cutoffs for each gene were obtained using receiver-operating characteristic curves (ROC) new model based on the support vector machine (SVM) estimated the probability of membership into one of two subgroups: GCB and Non-GCB (ABC and UC). Furtherly, multivariate analysis validated the model in another two cohorts including 855 cases in all. As a result, patients in the training and validated cohorts were stratified into two subgroups with 94.0%, 91.0%, and 94.4% concordance with GEP, respectively. Patients with Non-GCB subtype had significantly poorer outcomes than that with GCB subtype, which agreed with the prognostic power of GEP classification. Moreover, the similar prognosis received in the low (0-2) and high (3-5) IPI scores group demonstrated that the new model was independent of IPI as well as GEP method. In conclusion, our new model could stratify DLBCL patients with CHOP/R-CHOP regimen matching GEP subtypes effectively.
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