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Related Concept Videos

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Related Experiment Video

Updated: Dec 18, 2025

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
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Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine

Lei Wang1, ZhiQiang Zhao2, YanHong Luo1

  • 1Department of Health Statistics, Public Health department of Shanxi Medical University, Shan Xi Provincial Key Laboratory of Major Diseases Risk Assessment, China.

Computer Methods and Programs in Biomedicine
|June 17, 2020
PubMed
Summary

Accurate prediction of Diffuse large B-cell lymphoma (DLBCL) recurrence is crucial for treatment. This study developed a highly accurate predictive model using machine learning to identify patients at high risk of early recurrence.

Keywords:
Classification and possibility predictionImbalanced dataIndicatorsMachine learningRelapsed/refractory DLBCL

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Area of Science:

  • Oncology
  • Biostatistics
  • Machine Learning

Background:

  • Relapsed/refractory Diffuse large B-cell lymphoma (DLBCL) has limited treatment options and poor survival rates.
  • Accurate prediction of recurrence risk is needed to guide chemotherapy and improve long-term remission.

Purpose of the Study:

  • To establish predictive models for classifying DLBCL patients with complete remission based on their 2-year recurrence risk.
  • To identify significant indicators for predicting early recurrence in DLBCL patients.

Main Methods:

  • Assessed 518 DLBCL patients, analyzing 52 variables.
  • Selected 17 key variables using Lasso, Adaptive Lasso, and Elastic net methods.
  • Employed ensemble learning (AdaBoost, Voting, Stacking) with machine learning (SVM, BPANN, Random Forest) and SMOTE for imbalanced data classification and probability modeling.

Main Results:

  • Disease stage and five other variables were significant recurrence indicators.
  • The SVM with AdaBoost ensemble model demonstrated superior performance in classification (Sensitivity=97.3%, AUC=96%).
  • Both SVM with AdaBoost and Random Forest models showed excellent performance in probability prediction using SMOTE data.

Conclusions:

  • Developed a highly accurate predictive model for DLBCL recurrence.
  • Identified six key indicators that can serve as early recurrence signals for DLBCL patients.