- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT06241729
Prospective Observational Study of Diffuse Large-cell B Lymphoma (LBDGCréfract)
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
Study Overview
Status
Conditions
Intervention / Treatment
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 Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Marie Detrait, MD, PhD
- Phone Number: 8456 0031 71 10
- Email: marie.detrait@ghdc.be
Study Locations
-
-
Hainaut
-
Charleroi, Hainaut, Belgium, 6000
- Recruiting
- Grand Hopital de Charleroi
-
Contact:
- Delphine Pranger, MD
- Email: delphine.pranger@ghdc.be
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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
Exclusion Criteria:
- under 18 years old
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
Patients with diffuse large-cell B lymphoma
Patients with diffuse large-cell B lymphoma in a single-centre cohort at Grand Hôpital de Charleroi
|
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
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
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 years
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
The idendification of risk factors for refractory disease in DLBCL patients.
Time Frame: 3 years
|
statistical analysis with cox model on variables
|
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
|
kaplan Meier test and curves
|
3 years
|
The cohort survival rate at the end of the study.
Time Frame: 3 years
|
Survival rate for the cohort at the end of the study
|
3 years
|
Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Delphine Pranger, MD, Grand Hopital de Charleroi
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Estimated)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- LBDGC - refractory
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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