ClinicalTrials.gg
RecruitingNCT07087080Updated Jul 31, 2025

Plasma Metabolomics as a Tool to Distinguish PET-positive Malignant From PET-positive Benign Nodules

An interventional study of Blood sampling in Solitary Pulmonary Nodules and Lung Cancer, sponsored by Hasselt University. Recruiting at 1 site in Belgium. Per ClinicalTrials.gov, last updated 2025-07-31.

Sponsored by Hasselt University · Not applicable, Interventional, and Diagnostic

From the registry’s dates

  • Registered 3 years 2 months after the study started (first participant enrolled Mar 2022, registered May 2025).
  • Started Mar 2022; still recruiting 4 years 8 months later.
Phase
Not applicable
Study type
Interventional
Enrollment
350
Allocation
Not applicable
Sex
All
01

Study summary

Positron emission tomography-computed tomography (PET-CT) is an important technique in lung cancer staging, where almost no lung lesion goes undetected. However, PET-CT often fails to discriminate between malignant and non-malignant PET-positive solitary pulmonary nodules (SPNs) with a specificity of only 23%. 40-50% of those patients are advised to repeat their CT after three to six months to follow up on their lesions' progression, delaying a clear and correct cancer diagnosis and subsequent therapy. In more than 10% of the patients with an SPN on the PET-CT scan, an uncertain lung cancer diagnosis based on the PET-positive lesion leads to surgery that appears to be unnecessary.

This project aims to use the plasma glutamate concentration as a biomarker to complement PET-CT in the discrimination between malignant and non-malignant PET-positive SPNs. The investigators will validate a plasma glutamate determination by high- performance liquid chromatography (HPLC) since this test needs to be rapid, cheap, minimally invasive, and available in every hospital. In addition to the analysis of plasma glutamate, other plasma metabolites will be screened to check for other potential biomarkers to discriminate between malignant and non-malignant PET-positive SPNs. Together with the PET-CTs' basic parameters, a quick measurement of fasted plasma glutamate and potentially other biomarker levels right before undergoing a PET-CT scan will support a more rapid lung cancer diagnosis and treatment, resulting in less risk for disease progression. In conclusion, our approach will improve the accuracy of lung cancer diagnosis, and avoid unnecessary surgery.

Read the detailed description

PET-CT is an indispensable technique in lung cancer staging, where almost no lung lesion goes undetected since its sensitivity reaches 96%. However, PET-CT often fails to discriminate between malignant and non-malignant PET-positive SPNs with a specificity of only 23%. 40-50% of those patients are advised to repeat their CT after three to six months to follow up on their lesions' progression, delaying a clear and correct cancer diagnosis and subsequent therapy. More than 10% of the patients with a non-metastasized SPN on the PET-CT scan receive an unnecessary surgery due to this diagnostic uncertainty.

Due to these current challenges, the investigators will be searching for metabolite biomarkers that allow discrimination between benign and malignant SPNs. Biomarkers are defined as "characteristics that are objectively measured and evaluated as indicators of normal biological processes, pathological processes, or pharmacological responses to therapeutic interventions". This research will focus on metabolomics-based/metabolite biomarkers, as metabolic reprogramming is one of the hallmarks of cancer cells. As soon as the disease arises, cancer cells will reprogram their metabolism in order to meet the increased proliferation rate and energy consumption. Changes in metabolism result in changes in the concentration of metabolic end-products, metabolites, both intra- and extracellular. This principle allows for the detection of malignant SPNs by measuring metabolite concentrations in plasma.

The clinical applicability of metabolite biomarkers became clear from previously published work. Using proton nuclear magnetic resonance (1H-NMR) based metabolomics, a prior study of our research group demonstrated that a single plasma glutamate analysis could increase the PET-CT scans' discriminative specificity from 23% to 81% in a PET-positive patient population. However, further development and optimization are still needed to reach a clinical phase.

This prospective study will not only study plasma glutamate levels, but also the plasma levels of 61 additional metabolites. These 61 additional metabolites were identified in plasma in a previous study performed by our research group using 1H-NMR. Several of these metabolites, including lactate, acetate, cysteine, and asparagine, have already been shown to significantly contribute to malignant processes. Besides including more metabolites compared to the previously performed glutamate study, a technique other than 1H-NMR will be used. Even though 1H-NMR is characterized by its non-destructive and fast sample preparation and quantitative nature, it is a very costly technique that is usually unavailable in most hospitals. As this study aims to find plasma metabolites that can serve as clinical biomarkers for the distinction between benign and malignant SPNs, a more widely available technique will be used, called high-performance liquid chromatography (HPLC).

As the 61 metabolites of interest can be categorized into multiple chemical classes, such as amino acids and organic acids, different HPLC methods will be required. After the development of a suitable method for metabolite identification and quantification, plasma samples will be measured and metabolites will be quantified. By comparing metabolite concentrations in plasma samples derived from patients with a benign SPN and a malignant SPN, the investigators aim to find significant differences in metabolite concentrations between these two groups. Significantly altered metabolites could then potentially serve as plasma biomarkers for the distinction of benign and malignant SPNs, thereby improving diagnostic accuracy.

02

Conditions studied

  • Solitary Pulmonary Nodules
  • Lung Cancer

Keywords

  • Pulmonary nodules
  • Lung cancer
  • Plasma metabolomics
  • 1H-NMR
  • HPLC
03

In context

Solitary Pulmonary Nodule

93 studies on the registry are indexed under Solitary Pulmonary Nodule; 20 are open to participants now.

This study's planned enrollment of 350 is above the median of 81 across 44 interventional studies indexed under Solitary Pulmonary Nodule.

Browse Solitary Pulmonary Nodule studies →

Lead sponsor

Hasselt University is the lead sponsor of 204 studies on the registry; 31 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
Child (0–17), Adult (18–64), Older adult (65+)
Sexes eligible
All
Accepts healthy volunteers
No

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

Inclusion criteria

  • patients who undergo a PET-CT scan at ZOL for a lung nodule, who are willing to provide written

Exclusion criteria

Exclusion Criteria:

  • no fasting starting 6h prior to blood sampling;
  • medication intake on the morning of blood sampling;
  • fasting blood glucose concentration is higher than 200 mg/dL in the morning of blood sampling;
  • history of cancer during the past five years;
  • treatment for cancer during the past five years.
Terms in these criteria, in plain language(1)

Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.

Informed consent

Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.

You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.

05

Study design

Phase
Not applicable
Primary purpose
Diagnostic
Allocation
Not applicable
Intervention model
Single group
Masking
None (open label)
Enrollment
350 participants (estimated)

Study arms

  • Other
    Blood sampling

    Blood sampling

    Other: Blood sampling

Interventions

  • OtherBlood sampling

    Single blood sampling (16 ml) after study inclusion before undergoing the PET-CT scan

06

What researchers measure

Primary outcomes

  1. Fasted glutamate concentration

    Fasted glutamate, measured by High-Performance Liquid Chromatography (HPLC), will serve as metabolic biomarker in distinguishing between malignancy and non-malignancy in positron-emission tomography (PET) positive solitary pulmonary nodules (SPNs). To this end, fasted plasma glutamate concentration will be compared between patients with a final diagnosis of malignant and non-malignant PET-positive SPNs. Based on this, plasma glutamate cut-off values will be determined between the two groups of patients. Combining these cut-off values with PET-parameters can eventually increase the specificity of PET-CT.

    Time frame: baseline

Secondary outcomes

  1. Screening of 61 measurable plasma metabolites

    Screening 61 measurable plasma metabolites via multivariate statistics to find other potential biomarkers to differentiate between malignant and benign PET-positive SPNs. By analyzing more metabolites than glutamate, the investigators aim to increase the specificity even more. When significant differences in one or more metabolites are found, cut-off values will again be determined between the two groups of patients.

    Time frame: baseline

  2. Combine metabolite values to radiomics features

    HPLC metabolite values will be combined with advanced PET-CT features (radiomics study). SPNs of the included patients will be segmented using the ACCURATE tool. Radiomic features will eventually be extracted from all segmented lesions using the RADIOMICS tool, yielding a total of about 500 features per lesion. In the case of overfitting, several feature reduction methods might be explored, such as LASSO, pairwise feature elimination, or random forest. The obtained radiomic features will first be analyzed individually and afterward analyzed in combination with the obtained metabolomic data. The investigators aim to determine whether a combination of radiomic and metabolomic data improves the specificity of SPN diagnosis compared to the use of these methods individually.

    Time frame: baseline

07

Study locations

1 of 1 sites recruiting
  • Ziekenhuis Oost-Limburg
    Genk, Limburg 3600, Belgium
    Recruiting
    • Jill Meynen · Contactjill.meynen@uhasselt.be · 003289804034
    • Liesbet Mesotten, prof. dr. · Contact
    • Liesbet Mesotten, prof. dr. · Principal investigator
    • Jill Meynen · Sub investigator
08

References and documents

Publications

  • Vanhove K, Giesen P, Owokotomo OE, Mesotten L, Louis E, Shkedy Z, Thomeer M, Adriaensens P. The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer. BMC Cancer. 2018 Sep 3;18(1):868. doi: 10.1186/s12885-018-4755-1. PubMed 30176828 ↗
  • Derveaux E, Geubbelmans M, Criel M, Demedts I, Himpe U, Tournoy K, Vercauter P, Johansson E, Valkenborg D, Vanhove K, Mesotten L, Adriaensens P, Thomeer M. NMR-Metabolomics Reveals a Metabolic Shift after Surgical Resection of Non-Small Cell Lung Cancer. Cancers (Basel). 2023 Apr 3;15(7):2127. doi: 10.3390/cancers15072127. PubMed 37046788 ↗
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Timeline

Tracking since Sep 25, 2026
ElapsedEstimatedEstimated, not yet reachedSimilar studies: half by the dot; the band is the middle halfHalf of similar studies reached it by the dot; the band is the middle half, from a quarter to three quarters

Started Mar 2022 (actual). Primary completion is estimated for Apr 2027, 60 months after the start.

Of 24 interventional studies with no phase under Solitary Pulmonary Nodule, half reached primary completion within 14 months of starting (the median), counting those still running; a quarter within 9 and three quarters within 43. This study's planned 60 months is above that.

How these figures are worked out, and which studies are left out of them.

Registered
May 20, 2025
First posted
Jul 25, 2025
StartActual
Mar 2, 2022
Primary completionEst.
Apr 1, 2027
CompletionEst.
Nov 1, 2027
Last update
Jul 31, 2025
What these dates mean
Start.
When the first participant enrolled; while estimated, when the study expects to open to recruitment.
Primary completion.
When the last participant was examined or treated to collect data for the study's main question, its primary outcome. A study that stopped early gives the date its data collection stopped.
Completion.
When the last participant's last visit took place, for every outcome, secondary ones and side effects included: the end of the study. Often later than primary completion, sometimes the same day.
Actual and Est.
Actual means the registry says the date has happened. Est. (estimated) is a date the sponsor plans or expects. It can still move, and the sponsor is expected to change it to actual once it has passed.
No changes since tracking began. The registry record was last updated on Jul 31, 2025, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT07087080
Lead sponsor
Hasselt University
Collaborators
Ziekenhuis Oost-Limburg
Responsible party
Prof. dr. Liesbet Mesotten (Prof. dr., Hasselt University) — Principal investigator
First posted
Jul 25, 2025
Start date
Mar 2, 2022
Primary completion
Apr 1, 2027 (estimated)
Completion
Nov 1, 2027 (estimated)
Last update
Jul 31, 2025

Study contacts

Jill Meynen, Master degree
Contact
jill.meynen@uhasselt.be
003289804034
Liesbet Mesotten, prof. dr.
Contact
Liesbet Mesotten, prof. dr.
principal investigator · Ziekenhuis Oost-Limburg
Jill Meynen
study chair · Hasselt University
Elien Derveaux, dr.
study chair · Hasselt University
Wouter Marchal, prof. dr.
study chair · Hasselt University

Oversight

Data monitoring committee
No
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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