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Prospective Observational Study of Diffuse Large-cell B Lymphoma

NCT06241729 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 50

Last updated 2025-07-31

No results posted yet for this study

Summary

Diffuse large B-cell lymphoma (DLBCL) represents the most common type of non-Hodgkin lymphoma and is currently a curable malignant disease for many patients with immuno-chemotherapy frontline treatment. However, around 30-40 % of patients, are unresponsive or will experience early relapse. The prognosis of primary refractory patient is poor and the management and treatment are a significant challenge due to the disease heterogeneity and the complex genetic framework. The reasons for refractoriness are various and include genetic abnormalities, alterations in tumor and tumor microenvironment. Patient related factors such as comorbidities can also influence treatment outcome. Recently the progress in Machine learning (ML) showed its usefulness in the procedures used to analyze large and complex datasets. In medicine, machine learning is used to create some predictive tools based on data-driven analytic approach and integration of various risk factors and parameters. Machine learning, as a subdomain of artificial intelligence (AI), has the capability to autonomously uncover patterns within datasets. It offers algorithms that can learn from examples to perform a task automatically.The investigators tested in a previous study five machine learning algorithms to establish a model for predicting the risk of primary refractory DLBCL using parameters obtained from a monocentric dataset. The investigators observed that NB Categorical classifier was the best alternative for building a model in order to predict primary refractory disease in DLBCL patients and the second was XGBoost.The investigators plan to extend this previous study by further exploring the two best-performing models (NBC Classifier and XGBoost), progressively incorporating a larger number of patients in a prospective way.

Conditions

  • Lymphoma, B-Cell

Interventions

OTHER

Algorithms to predict the probability of a primary refractory state

Follow-up of a cohort of patients with diffuse large-cell B lymphoma from 2024 using algorithms to predict the probability of a primary refractory state

Sponsors & Collaborators

  • Grand Hôpital de Charleroi

    lead OTHER

Principal Investigators

  • Delphine Pranger, MD · Grand Hôpital de Charleroi

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-01-03
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • Belgium

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06241729 on ClinicalTrials.gov