Lymphoma, Non-HodgkinLNH-AI-ToolsNCT07546188
AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients.
An observational study of Lymphoma, Non-Hodgkin and Care Coordination, run by Grand Hôpital de Charleroi at 1 site in Belgium.
Trial summary
This research forms part of a continuous quality improvement initiative. It aims to assess patient compliance of oral therapies by artificial intelligence. It could overcome the limitations of current practices and enhance the responsiveness and accuracy of clinical interventions. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Lymphoma, Non-Hodgkin, Care Coordination
- What is being tested?
- Retrospective Group and Prospective Group
- How many people
- 210 participants
- Where
- 1 siteBelgium
- Study dates
- 15 Feb 2026to 15 Feb 2029
- 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: Patients treated in the Haematology Department at Charleroi General Hospital for Non-Hodgkin lymphoma.
Who may be able to join
- All patients aged 18 and over who are treated in the Haematology Department at the Grand Hôpital de Charleroi from November 2025 onwards
- Treated for a lymphoma, Non Hodgkin
- Capable of giving informed consent
Who cannot join
- All other patients who did not meet the eligibility criteria
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, 6060
Marie Detrait, MD, PhD, 0032 60 11 20 08, marie.detrait@ghdc.be
Marie Detrait, MedSC, 0032 60 11 00 89, aline.gillain@ghdc.be
About this study
Official title: AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients, Integrating the Complete Care Pathway and an Interoperable Clinical Interface With Algorithms Paired With Explainability Tools.
This research forms part of a continuous quality improvement initiative. It aims to assess patient compliance of oral therapies by artificial intelligence. It could overcome the limitations of current practices and enhance the responsiveness and accuracy of clinical interventions.
Read the detailed description
Non- Hodgkin Lymphomas require rigorous treatment protocols, including intensive intravenous chemotherapy or targeted oral therapies. Secondary immunosuppression necessitates oral anti-infective prophylaxis (such as valacyclovir or Bactrim forte) to prevent opportunistic complications. However, the literature reports figures of up to 50% of patients experiencing adherence difficulties on oral therapies, compromising treatment efficacy, increasing the risk of severe infections, prolonged hospitalizations, and consequently, additional costs for the healthcare system. This project proposes to develop an innovative artificial intelligence (AI) tool, based on real-world data, to detect early signs of non-adherence and enable targeted intervention by healthcare teams. Our approach combines analysis of clinical data (patient, disease, dispensing history, laboratory results, drug interactions) and machine learning algorithms (supervised machine learning and neural networks) to identify at-risk profiles. The tool will generate a real-time alert and offer the patient's referring physician and coordinating nurse tailored recommendations, such as an automated reminder, a dedicated nursing consultation, etc. An intuitive interface will allow clinicians and nurses to visualize compliance trends and act quickly. This project relies on a multidisciplinary team (hematologists, advanced practice nurses (APNs), data scientists, AI experts) and patient partners to validate the tool in real-world conditions.
- Conditions
- Lymphoma, Non-Hodgkin, Care Coordination
- Being tested
- Retrospective Group, Prospective Group
- Keywords
- Artificial intelligence, Non-Hodgkin lymphoma, Care pathway
- Medical subject headings
- Lymphoma, Non-Hodgkin
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Retrospective GroupOther
For the retrospective group of 20 patients.
Prospective GroupOther
Follow-up of the patients for the prospective group
| Study group | Role | What they receive |
|---|---|---|
| Retrospective cohort | A retrospective cohort from 2019 to 2024 comprising 350 lymphoma patients who were monitored on an empirical basis. Receives: Other: Retrospective Group | |
| Prospective cohort | A prospective cohort study involving up to 210 consecutive patients, starting in November 2025, with the aim of developing a decision-support tool using machine learning. Receives: Other: Prospective Group |
What is being measured
The primary outcome is the main question the study is designed to answer.
ROC-AUC
Description: ROC-AUC : Receiver Operating Characteristic - Area Under the Curve is a performance metric for binary classification prediction algorithms. ROC Curve: Plots the True Positive Rate (sensitivity) against the False Positive Rate (1-specificity) at various classification thresholds. AUC: The area under this curve (ranging from 0 to 1). A higher AUC indicates better model performance-1.0 is perfect, 0.5 is random guessing. ROC-AUC evaluates how well the model distinguishes between classes, regardless of the classification threshold.
Time Frame: When the data will be avalaible, at the end of 2027
Measured: 2027
Secondary outcomes (1)
F1-score
F1-Score is a performance metric for classification algorithms, the harmonic mean of Precision (correct positive predictions / total positive predictions) and Recall (correct positive predictions / actual positives).
Formula: F1 = 2 × (Precision × Recall) / (Precision + Recall) Range: 0 to 1, where 1 is perfect precision and recall, and 0 is the worst.
F1-Score balances precision and recall, making it ideal when you need to avoid both false positives and false negatives.
Measured: When the data will be avalaible, at the end of 2027
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Apr 2026
- Study start15 Feb 2026
- Primary completion31 Dec 2027 (estimated)
- Study completion15 Feb 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Lymphoma, Non-HodgkinStudies 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
- 210 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Grand Hôpital de Charleroi
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Marie Detrait, MD, PhD, Principal investigator, Grand Hôpital de Charleroi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07546188
- Sponsor's protocol ID
- LNH-AI-Tools
- Acronym
- LNH-AI-Tools
- Status last verified
- Apr 2026
- First submitted
- 8 Apr 2026
- Last updated
- 22 Apr 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: At present, this research is being carried out in-house; following analysis, this option could be considered if the model can be adapted for use elsewhere.
Study at a glance
From the registry record, last updated by the sponsor on 22 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-based Predictive and Interventional System for Early Detection of Non-compliance Risks With Oral Therapies in Lymphoma Patients, Integrating the Complete Care Pathway and an Interoperable Clinical Interface With Algorithms Paired With Explainability Tools.
- ClinicalTrials.gov ID
- NCT07546188
- Acronym / sponsor ID
- LNH-AI-Tools / LNH-AI-Tools
- Conditions
- Lymphoma, Non-Hodgkin, Care Coordination
- Keywords
- Artificial intelligence, Non-Hodgkin lymphoma, Care pathway
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 210 participants (estimated)
- Lead sponsor
- Grand Hôpital de CharleroiUniversity, hospital or other
- Study start
- 15 Feb 2026 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 15 Feb 2029 (estimated)
- First posted
- 22 Apr 2026 (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.
This research forms part of a continuous quality improvement initiative. It aims to assess patient compliance of oral therapies by artificial intelligence. It could overcome the limitations of current practices and enhance the responsiveness and accuracy of clinical interventions.
Detailed description
Non- Hodgkin Lymphomas require rigorous treatment protocols, including intensive intravenous chemotherapy or targeted oral therapies. Secondary immunosuppression necessitates oral anti-infective prophylaxis (such as valacyclovir or Bactrim forte) to prevent opportunistic complications. However, the literature reports figures of up to 50% of patients experiencing adherence difficulties on oral therapies, compromising treatment efficacy, increasing the risk of severe infections, prolonged hospitalizations, and consequently, additional costs for the healthcare system. This project proposes to develop an innovative artificial intelligence (AI) tool, based on real-world data, to detect early signs of non-adherence and enable targeted intervention by healthcare teams. Our approach combines analysis of clinical data (patient, disease, dispensing history, laboratory results, drug interactions) and machine learning algorithms (supervised machine learning and neural networks) to identify at-risk profiles. The tool will generate a real-time alert and offer the patient's referring physician and coordinating nurse tailored recommendations, such as an automated reminder, a dedicated nursing consultation, etc. An intuitive interface will allow clinicians and nurses to visualize compliance trends and act quickly. This project relies on a multidisciplinary team (hematologists, advanced practice nurses (APNs), data scientists, AI experts) and patient partners to validate the tool in real-world conditions.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Retrospective cohort | – | Other: Retrospective GroupA retrospective cohort from 2019 to 2024 comprising 350 lymphoma patients who were monitored on an empirical basis. |
| Prospective cohort | – | Other: Prospective GroupA prospective cohort study involving up to 210 consecutive patients, starting in November 2025, with the aim of developing a decision-support tool using machine learning. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients treated in the Haematology Department at Charleroi General Hospital for Non-Hodgkin lymphoma.
Key inclusion criteria (3 listed)
- All patients aged 18 and over who are treated in the Haematology Department at the Grand Hôpital de Charleroi from November 2025 onwards
- Treated for a lymphoma, Non Hodgkin
- Capable of giving informed consent
Key exclusion criteria (1 listed)
- All other patients who did not meet the eligibility criteria
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 |
|---|---|---|
| Retrospective GroupFor the retrospective group of 20 patients. | Other | Retrospective cohort |
| Prospective GroupFollow-up of the patients for the prospective group | Other | Prospective cohort |
Primary outcome
ROC-AUC
Time frame: 2027
Description: ROC-AUC : Receiver Operating Characteristic - Area Under the Curve is a performance metric for binary classification prediction algorithms. ROC Curve: Plots the True Positive Rate (sensitivity) against the False Positive Rate (1-specificity) at various classification thresholds. AUC: The area under this curve (ranging from 0 to 1). A higher AUC indicates better model performance-1.0 is perfect, 0.5 is random guessing. ROC-AUC evaluates how well the model distinguishes between classes, regardless of the classification threshold. Time Frame: When the data will be avalaible, at the end of 2027
1 secondary outcome
F1-score
Time frame: When the data will be avalaible, at the end of 2027
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 22 Apr 2026.
- Individual participant dataSharing planned: No. At present, this research is being carried out in-house; following analysis, this option could be considered if the model can be adapted for use elsewhere.
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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.
Marie Detrait, MD, PhD 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, Non-Hodgkin and Care Coordination, 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 3 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 22 Apr 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 (0032 60 11 20 08, marie.detrait@ghdc.be), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
ROC-AUC (time frame: 2027). 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 22 Apr 2026.