Plasma Metabolomics as a Tool to Distinguish PET-positive Malignant From PET-positive Benign Nodules
Trial statusRecruiting
Trial phaseNot applicable
Trial typeInterventional
Biological sexAll
AgeNot listed
SponsorHasselt University
About this trial
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.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
patients who undergo a PET-CT scan at ZOL for a lung nodule, who are willing to provide written informed consent
Disqualifiers
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;
Trial design
Design model
Single group
Treatments tested in this trial
Blood sampling
Other interventionSingle blood sampling (16 ml) after study inclusion before undergoing the PET-CT scan
Treatment groups
350 Participants
are divided into 1 treatment groupGroup A: Blood samplingOther 1 intervention
Trial outcomes
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
baselineSecondary 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
baseline2
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
baselineSponsors and contacts
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Hasselt University
Lead sponsor
Ziekenhuis Oost-Limburg
Collaborator
Source ClinicalTrials.gov