Prospective Observational Study of Diffuse Large-cell B Lymphoma

NCT06241729 | Recruiting

• 1 other identifier: LBDGC - refractory
• Study Type: observational
• Target: 50
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (1)

• The area under the curve from receiver operator characteristic (ROC_AUC) in percent for each algorithm.

  Metric for algorithms evaluation, this metric has the capability to encapsulate the effectiveness of a classifier in a single measurement

  3 years

Secondary Outcomes (3)

• The idendification of risk factors for refractory disease in DLBCL patients.

  3 years

• The Overall Survival and Progression Free Survival in the cohort by Kaplan Meier at the end of the study.

  3 years

• The cohort survival rate at the end of the study.

  3 years

Study Arms (1)

Patients with diffuse large-cell B lymphoma

Patients with diffuse large-cell B lymphoma in a single-centre cohort at Grand Hôpital de Charleroi

Other: Algorithms to predict the probability of a primary refractory state

Interventions

Algorithms to predict the probability of a primary refractory state OTHER

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

Patients with diffuse large-cell B lymphoma

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients with diffuse large-cell B lymphoma treated in the haematology department at the Grand Hôpital de Charleroi for the first time between January 2024 and December 2026.

You may qualify if:

• patients with diffuse large-cell B lymphoma treated in the haematology department at the Grand Hôpital de Charleroi for the first time
• able to understand the information and sign their consent form

You may not qualify if:

• under 18 years old

Sponsors & Collaborators

• Grand Hôpital de Charleroi: lead

Study Sites (1)

Grand Hôpital de Charleroi

Charleroi, Hainaut, 6000, Belgium

RECRUITING

MeSH Terms

Conditions

Lymphoma, B-Cell

Condition Hierarchy (Ancestors)

Lymphoma, Non-Hodgkin; Lymphoma; Neoplasms by Histologic Type; Neoplasms; Lymphoproliferative Disorders; Lymphatic Diseases; Hemic and Lymphatic Diseases; Immunoproliferative Disorders; Immune System Diseases

Study Officials

• Delphine Pranger, MD

  Grand Hôpital de Charleroi

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Marie Detrait, MD, PhD

CONTACT

0031 71 10; marie.detrait@ghdc.be

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 12, 2024
• First Posted: February 5, 2024
• Study Start: January 3, 2023
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2026
• Last Updated: July 31, 2025

Record last verified: 2025-07

Locations

BE (1)