Lymphoma, B-CellLBDGCréfractNCT06241729
Prospective Observational Study of Diffuse Large-cell B Lymphoma
An observational study of Lymphoma, B-Cell, run by Grand Hôpital de Charleroi at 1 site in Belgium.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lymphoma, B-Cell
- What is being tested?
- Algorithms to predict the probability of a primary refractory state
- How many people
- 50 participants
- Where
- 1 siteBelgium
- Study dates
- 3 Jan 2023to 31 Dec 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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
Who cannot join
- under 18 years old
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Belgium1 site, 1 recruiting
- Recruiting
Grand Hôpital de Charleroi
Charleroi, Hainaut, 6000
Delphine Pranger, MD, delphine.pranger@ghdc.be
About this study
Official title: Supervised Machine Learning for the Prediction of Primary Refractory Status in Patients With Diffuse Large Cell B Lymphoma in a Monocentric Cohort at the Grand Hôpital de Charleroi
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.
Read the detailed description
Primary refractory disease affects approximately 30-40% of patients diagnosed with DLBCL and is a challenge in the management of this disease due to its poor prognosis. The prediction of refractory status could be very useful in the treatment strategy allowing early intervention. Indeed, several options are now available depending on patient and disease characteristics such as salvage chemotherapy and autologous HSCT, targeted therapies or CAR T-cell therapy. Supervised machine learning techniques are able to predict outcomes in a medical context and therefore seem very suitable for this matter.
An approach with machine learning seems particularly interesting because there are currently no statistical models efficient enough to provide decision-making support to clinicians. The investigators showed in a previous study that algorithms can be effective in predicting the refractory status of the disease from structured data from the patient's medical record. Due to the large number of available and effective salvage therapies, intervening quickly in the patient's therapeutic pathway seems to be the right option and the most personalized way to maximize the chances of cure while reducing those of toxicity. Based on clinical judgment of physicians and the best algorithms predictions, the physicians could choose an early treatment strategy for primary refractory DLBCL.
The investigators found in a previous study two interesting models (NBC and XGBoost) for predicting refractory disease on the validation set. The application of machine learning techniques can significantly contribute to the management of DLBCL patients. These algorithms hold the potential to assist clinicians in making informed decisions regarding treatment strategies, allowing for the personalization of therapies based on each patient. This study aims to validate these findings on a broader scale in a prospective cohort and the value of this technology in the intricate management of primary refractory disease in DLBCL patients.
- Conditions
- Lymphoma, B-Cell
- Being tested
- Algorithms to predict the probability of a primary refractory state
- Medical subject headings
- Lymphoma, B-Cell
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Algorithms to predict the probability of a primary refractory stateOther
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
| Study group | Role | What they receive |
|---|---|---|
| Patients with diffuse large-cell B lymphoma | Patients with diffuse large-cell B lymphoma in a single-centre cohort at Grand Hôpital de Charleroi Receives: Other: Algorithms to predict the probability of a primary refractory state |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 years
Secondary outcomes (3)
The idendification of risk factors for refractory disease in DLBCL patients.
statistical analysis with cox model on variables
Measured: 3 years
The Overall Survival and Progression Free Survival in the cohort by Kaplan Meier at the end of the study.
kaplan Meier test and curves
Measured: 3 years
The cohort survival rate at the end of the study.
Survival rate for the cohort at the end of the study
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted5 Feb 2024
- Study start3 Jan 2023
- Primary completion31 Dec 2026 (estimated)
- Study completion31 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lymphoma, B-CellStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Grand Hôpital de Charleroi
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Delphine Pranger, MD, Principal investigator, Grand Hôpital de Charleroi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06241729
- Sponsor's protocol ID
- LBDGC - refractory
- Acronym
- LBDGCréfract
- Status last verified
- Sep 2026
- First submitted
- 12 Jan 2024
- Last updated
- 14 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Supervised Machine Learning for the Prediction of Primary Refractory Status in Patients With Diffuse Large Cell B Lymphoma in a Monocentric Cohort at the Grand Hôpital de Charleroi
- ClinicalTrials.gov ID
- NCT06241729
- Acronym / sponsor ID
- LBDGCréfract / LBDGC - refractory
- Conditions
- Lymphoma, B-Cell
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Grand Hôpital de CharleroiUniversity, hospital or other
- Study start
- 3 Jan 2023 (actual)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 31 Dec 2026 (estimated)
- First posted
- 5 Feb 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Primary refractory disease affects approximately 30-40% of patients diagnosed with DLBCL and is a challenge in the management of this disease due to its poor prognosis. The prediction of refractory status could be very useful in the treatment strategy allowing early intervention. Indeed, several options are now available depending on patient and disease characteristics such as salvage chemotherapy and autologous HSCT, targeted therapies or CAR T-cell therapy. Supervised machine learning techniques are able to predict outcomes in a medical context and therefore seem very suitable for this matter.
An approach with machine learning seems particularly interesting because there are currently no statistical models efficient enough to provide decision-making support to clinicians. The investigators showed in a previous study that algorithms can be effective in predicting the refractory status of the disease from structured data from the patient's medical record. Due to the large number of available and effective salvage therapies, intervening quickly in the patient's therapeutic pathway seems to be the right option and the most personalized way to maximize the chances of cure while reducing those of toxicity. Based on clinical judgment of physicians and the best algorithms predictions, the physicians could choose an early treatment strategy for primary refractory DLBCL.
The investigators found in a previous study two interesting models (NBC and XGBoost) for predicting refractory disease on the validation set. The application of machine learning techniques can significantly contribute to the management of DLBCL patients. These algorithms hold the potential to assist clinicians in making informed decisions regarding treatment strategies, allowing for the personalization of therapies based on each patient. This study aims to validate these findings on a broader scale in a prospective cohort and the value of this technology in the intricate management of primary refractory disease in DLBCL patients.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients with diffuse large-cell B lymphoma | – | Other: Algorithms to predict the probability of a primary refractory statePatients with diffuse large-cell B lymphoma in a single-centre cohort at Grand Hôpital de Charleroi |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (1 listed)
- under 18 years old
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Algorithms to predict the probability of a primary refractory stateFollow-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 | Other | Patients with diffuse large-cell B lymphoma |
Primary outcome
The area under the curve from receiver operator characteristic (ROC_AUC) in percent for each algorithm.
Time frame: 3 years
Metric for algorithms evaluation, this metric has the capability to encapsulate the effectiveness of a classifier in a single measurement
3 secondary outcomes
The idendification of risk factors for refractory disease in DLBCL patients.
Time frame: 3 years
The Overall Survival and Progression Free Survival in the cohort by Kaplan Meier at the end of the study.
Time frame: 3 years
The cohort survival rate at the end of the study.
Time frame: 3 years
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Sep 2026.
Related trials for Lymphoma, B-Cell
Recruiting
A Multicenter Study to Evaluate Next-Generation Sequencing (NGS) Testing and Monitoring of B-Cell Recovery to Guide Management Following Chimeric Antigen Receptor T-cell (CART) Induced Remission in Children and Young Adults With B Lineage Acute Lymph...
B-All, Acute Lymphoblastic Leukemia
RecruitingObservational study
An Observational Study of Glofitamab in Chinese Adult Participants With 2L Diffuse Large B-Cell Lymphoma
Diffuse Large B-Cell Lymphoma
RecruitingPhase 1
A Study Evaluating the Safety, Efficacy, and Pharmacokinetics of Mosunetuzumab and a Combined Regimen of Mosunetuzumab and Venetoclax in Participants With Relapsed or Refractory Chronic Lymphocytic Leukemia
Chronic Lymphocytic Leukemia or Small Lymphocytic Lymphoma
RecruitingPhase 3
A Study of Fludarabine Dosing in Children and Young Adults With B-cell Acute Lymphoblastic Leukemia
B-cell Acute Lymphoblastic Leukemia
Not yet recruiting
Busulfan-Based Versus TBI-Based Conditioning for Adult ALL-CR2 Allo-HSCT
Acute Lymphoblastic Leukemia, Hematopoietic Stem Cell Transplantation
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Delphine Pranger, MD principal investigator
Grand Hôpital de Charleroi
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Lymphoma, B-Cell, run by Grand Hôpital de Charleroi at 1 site in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 14 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Marie Detrait, MD, PhD (0031 71 10 ext. 8456, marie.detrait@ghdc.be), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The area under the curve from receiver operator characteristic (ROC_AUC) in percent for each algorithm. (time frame: 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 14 Sep 2026.