Clinical trial · Observational
Prospective Observational Study of Diffuse Large-cell B Lymphoma
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
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 15, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260915-000001
Summary
Brief summary (as posted)
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
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lymphoma, B-Cell | B-Cell Malignant Neoplasm | CURATED_BROADER | 0.80 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Algorithms to predict the probability of a primary refractory state | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- label
- Patients with diffuse large-cell B lymphoma
- description
- Patients with diffuse large-cell B lymphoma in a single-centre cohort at Grand Hôpital de Charleroi
- interventionNames
- Other: Algorithms to predict the probability of a primary refractory state
Primary outcomes (1)
- measure
- The area under the curve from receiver operator characteristic (ROC_AUC) in percent for each algorithm.
- timeFrame
- 3 years
- description
- Metric for algorithms evaluation, this metric has the capability to encapsulate the effectiveness of a classifier in a single measurement
Secondary outcomes (3)
- measure
- The idendification of risk factors for refractory disease in DLBCL patients.
- timeFrame
- 3 years
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
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
References
Publications (0)
Data not yet available