NCT07066436

ImmunoMRI for Assessment of Tumor-associated Macrophages: A Prospective Study in Patients With Diffuse Large B-cell Lymphoma Receiving CAR T-cell or Bispecific Antibody Therapy

Not yet recruiting EARLY_PHASE1 Last updated 4 July 2025

What this trial tests

EARLY_PHASE1 trial testing MRI contrast-enhancing agents in DLBCL - Diffuse Large B Cell Lymphoma in 60 participants. Not yet recruiting.

Timeline

1 October 2025

Primary endpoint
1 March 2028

1 September 2028

Quick facts

Lead sponsor	Medical University of Vienna
Phase	EARLY_PHASE1
Status	Not yet recruiting
Study type	INTERVENTIONAL
Allocation	na
Design	single group
Masking	none
Primary purpose	diagnostic
Enrollment	60
Start date	1 October 2025
Primary completion	1 March 2028
Estimated completion	1 September 2028
Sites	1 location across Austria

Drugs / interventions tested

• MRI contrast-enhancing agents — full drug profile →

Conditions studied

• DLBCL - Diffuse Large B Cell Lymphoma — all drugs for DLBCL - Diffuse Large B Cell Lymphoma →

Sponsor

Medical University of Vienna

Who can join

18 and older, any sex, with DLBCL - Diffuse Large B Cell Lymphoma. Patients with the condition only — healthy volunteers not accepted.

What's being measured

Primary outcomes are the specific endpoints the trial is designed to prove or disprove.

• Correlation between immunoMRI and M1 and M2 TAM-directed IHC
  Time frame: Through study completion, 3 years
  To determine the associations between pre-treatment immunoMRI metrics and IHC-based TAM levels, median/mean T2\* will be calculated for each of the four IHC categories (score 1-4), separately for M1 and M2 TAMs, and Spearman rank correlation coefficients will be calculated.
• Prediction of complete remission (CR)
  Time frame: Through study completion, 3 years
  Median/mean pre-treatment T2\* and on-treatment T2\* changes (relative to baseline), will be compared between patients achieving/not achieving complete remission (CR). Logistic regression analyses will be used to test combinations of pre-treatment T2\* and △T2\*, and respective SUV (standardized uptake values) and MTV (metabolic tumor volumes) on PET and VOL (morphologic volumes) on MRI
• Prediction of 1-year overall survival (OS)
  Time frame: Through study completion, 3 years
  Median/mean pre-treatment T2\* and on-treatment T2\* changes (relative to baseline) will be compared between patients achieving/not achieving 1-year OS. Logistic regression analyses will be used to test combinations of pre-treatment T2\* and △T2\*, and respective SUV (standardized uptake values) and MTV (metabolic tumor volumes) on PET and VOL (morphologic volumes) on MRI, to predict 1-year OS.
• Prediction of 1-year PFS
  Time frame: Through study completion, 3 years
  Median/mean pre-treatment T2\* and on-treatment T2\* changes (relative to baseline) will be compared between patients achieving/not achieving 1-year OS. Logistic regression analyses will be used to test combinations of pre-treatment T2\* and △T2\*, and respective SUV (standardized uptake values) and MTV (metabolic tumor volumes) on PET and VOL (morphologic volumes) on MRI, to predict 1-year OS.
• Prediction of development of treatment toxicities (CRS, ICANS)
  Time frame: Through study completion, 3 years
  Median/mean pre-treatment T2\*pre and on-treatment △T2\* values will be compared between patients developing/not developing cytokine release syndrom (CRS) or immune effector cell-associated neurotoxicity syndrome (ICANS), and ROC analyses will be performed for each treatment group

Sponsor's own description

About 35% of patients with a type of blood cancer called diffuse large B-cell lymphoma don't respond well to standard treatment or their cancer comes back. When this happens, newer treatments like CAR T-cell therapy (using modified immune cells) or bispecific antibodies (special proteins that help the immune system fight cancer) are an option. However, these treatments are only successful in about half the patients. It is currently difficult to predict which patients will respond to these treatments or experience serious side effects. This makes it hard to choose the best treatment plan for a given patient.

In this project, a special type of magnetic resonance imaging (MRI) scan will be used to track immune cells called macrophages that live around tumors. These cells can either help fight cancer or help cancer grow. By understanding how these cells behave, it may be possible to predict treatment success. The MRI technique involves injecting an iron-based substance called ferumoxytol, which can be used as an MRI contrast agent, into patients' veins. This contrast agent gets absorbed by the macrophages, making them visible on MRI scans throughout the entire body - not just one tumor spot. Sixty patients will be scanned before and after treatment (30 getting CAR T-cells, 30 getting bispecific antibodies), and results will be compared with tissue samples.

The goals are to predict which patients will go into complete remission, predict who will survive longer without cancer progression, and identify patients at risk for serious side effects like cytokine release syndrome. If successful, this imaging technique could help to personalize treatment choices, potentially improving outcomes while avoiding unnecessary toxicity in patients who will not benefit from these intensive therapies.

Publications & conference data

No peer-reviewed publications indexed yet for this trial.