Imaging Biomarkers for Lung Cancer
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: University of Texas Southwestern Medical Center
Disqualifiers: Poorly controlled diabetes, others
No Placebo Group
Approved in 1 Jurisdiction
Trial Summary
What is the purpose of this trial?
The purpose of this research study is to develop a method of using magnetic resonance imaging (MRI) to evaluate lung tumors and other thoracic malignancies. An MRI is a scanning device that uses magnets to make images (pictures) of the body. This study is being done to determine what series of reactions (metabolic pathways) pulmonary nodules use as they burn sugar as fuel for growth. The manner in which the tumor burns (metabolizes) sugar for fuel is being investigated by using a natural, slightly modified, sugar solution (13C-glucose) and studying a small sample of the tumor once it is removed at the time of surgery.
Will I have to stop taking my current medications?
The trial protocol does not specify whether you need to stop taking your current medications. However, if you have poorly controlled diabetes, you may not be eligible to participate.
What data supports the effectiveness of the treatment Imaging Biomarkers, MRI Biomarkers, Metabolic Imaging Biomarkers for lung cancer?
Research shows that using imaging features from MRI and PET scans can help predict how well patients with non-small cell lung cancer will do after treatment, by identifying those at higher risk of disease progression. This suggests that imaging biomarkers can be effective in evaluating and potentially improving treatment outcomes for lung cancer.12345
Is the use of imaging biomarkers, like MRI, safe for humans?
How does this imaging treatment for lung cancer differ from other treatments?
This treatment is unique because it uses advanced imaging techniques like PET-CT and MRI to provide detailed information about lung cancer, including its metabolic activity and anatomical structure, which helps in better diagnosis, staging, and treatment planning compared to traditional imaging methods.1011121314
Eligibility Criteria
This trial is for adults over 18 with known or suspected malignant lung lesions that need surgical removal. It's open to all races and ethnicities. People with poorly controlled diabetes or those who are not suitable for surgery cannot participate.Inclusion Criteria
I need surgery to remove or biopsy a suspected cancerous lesion.
I am over 18 years old.
Exclusion Criteria
My diabetes is not well-managed.
I cannot undergo surgery for my condition.
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
Pre-Surgery Imaging
Participants undergo DCE-MRI to determine eligibility for the [U-13C] glucose infusion
Within 5 days of scheduled surgery
1 visit (in-person)
Surgery and Infusion
Participants receive 13C-glucose solution intravenously during surgery for tumor removal
2-3 hours
1 visit (in-person)
Follow-up
Participants are monitored for metabolic alterations using C-13 isotopomer analysis and metabolomics
4 weeks
Treatment Details
Interventions
- Imaging Biomarkers (Procedure)
Trial OverviewThe study aims to use MRI scans, along with a special sugar solution (13C-glucose), to understand how lung tumors metabolize sugar. Researchers will analyze tumor samples post-surgery to learn about the metabolic pathways of cancer growth.
Participant Groups
1Treatment groups
Experimental Treatment
Group I: SurgeryExperimental Treatment1 Intervention
The 13C-glucose solution will be given intravenously. It will be started at about the same time as the start of surgery, according to the study guidelines.
The 13C-glucose IV solution will be stopped once the surgeon has removed the tumor tissue.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
University of Texas Southwestern Medical CenterDallas, TX
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Who Is Running the Clinical Trial?
University of Texas Southwestern Medical CenterLead Sponsor
References
Prognostic value of dynamic MR imaging for non-small-cell lung cancer patients after chemoradiotherapy. [2022]To determine the prognostic value of dynamic MRI for non-small-cell lung cancer (NSCLC) patients after chemoradiotherapy.
Early response evaluation using primary tumor and nodal imaging features to predict progression-free survival of locally advanced non-small cell lung cancer. [2021]Prognostic biomarkers that can reliably predict early disease progression of non-small cell lung cancer (NSCLC) are needed for identifying those patients at high risk for progression, who may benefit from more intensive treatment. In this work, we aimed to identify an imaging signature for predicting progression-free survival (PFS) of locally advanced NSCLC. Methods: This retrospective study included 82 patients with stage III NSCLC treated with definitive chemoradiotherapy for whom both baseline and mid-treatment PET/CT scans were performed. They were randomly placed into two groups: training cohort (n=41) and testing cohort (n=41). All primary tumors and involved lymph nodes were delineated. Forty-five quantitative imaging features were extracted to characterize the tumors and involved nodes at baseline and mid-treatment as well as differences between two scans performed at these two points. An imaging signature was developed to predict PFS by fitting an L1-regularized Cox regression model. Results: The final imaging signature consisted of three imaging features: the baseline tumor volume, the baseline maximum distance between involved nodes, and the change in maximum distance between the primary tumor and involved nodes measured at two time points. According to multivariate analysis, the imaging model was an independent prognostic factor for PFS in both the training (hazard ratio [HR], 1.14, 95% confidence interval [CI], 1.04-1.24; P = 0.003), and testing (HR, 1.21, 95% CI, 1.10-1.33; P = 0.048) cohorts. The imaging signature stratified patients into low- and high-risk groups, with 2-year PFS rates of 61.9% and 33.2%, respectively (P = 0.004 [log-rank test]; HR, 4.13, 95% CI, 1.42-11.70) in the training cohort, as well as 43.8% and 22.6%, respectively (P = 0.006 [log-rank test]; HR, 3.45, 95% CI, 1.35-8.83) in the testing cohort. In both cohorts, the imaging signature significantly outperformed conventional imaging metrics, including tumor volume and SUVmax value (C-indices: 0.77-0.79 for imaging signature, and 0.53-0.73 for conventional metrics). Conclusions: Evaluation of early treatment response by combining primary tumor and nodal imaging characteristics may improve the prediction of PFS of locally advanced NSCLC patients.
Outcome of small lung nodules missed on hybrid PET/MRI in patients with primary malignancy. [2022]To assess outcomes of lung nodules missed on simultaneous positron emission tomography and magnetic resonance imaging (PET/MRI) compared to the reference standard PET and computed tomography (PET/CT) in patients with primary malignancy.
Imaging lung cancer. [2005]Imaging plays an essential role in diagnosing, staging, and following patients with lung cancer. Most tumors are found on chest radiographs, although further evaluation with thoracic computed tomography is performed to stage local disease. Additional radiologic studies, including radionuclide bone scan, brain computed tomography, or magnetic resonance imaging are typically used in select patients in the search for extrathoracic metastases. More recently, whole body positron emission tomography imaging has become an extremely useful tool in evaluating the primary tumor, regional lymph nodes, and distant sites of disease in lung cancer patients. With continued improvements in diagnostic imaging modalities, definition of risk groups, discovery of molecular markers, and development of new therapeutic strategies, improved survival rates should result in the future. This review focuses on the current imaging techniques used to evaluate patients with lung cancer.
Update of MR Imaging for Evaluation of Lung Cancer. [2018]Since MR imaging was introduced for the assessment of thoracic and lung diseases, various limitations have hindered its widespread adoption in clinical practice. Since 2000, various techniques have been developed that have demonstrated the usefulness of MR imaging for lung cancer evaluation, and it is now reimbursed by health insurance companies in many countries. This article reviews recent advances in lung MR imaging, focusing on its use for lung cancer evaluation, especially with regard to pulmonary nodule detection, pulmonary nodule and mass assessment, lung cancer staging and detection of recurrence, postoperative lung function prediction, and therapeutic response evaluation and prediction.
A radiogenomic dataset of non-small cell lung cancer. [2019]Medical image biomarkers of cancer promise improvements in patient care through advances in precision medicine. Compared to genomic biomarkers, image biomarkers provide the advantages of being non-invasive, and characterizing a heterogeneous tumor in its entirety, as opposed to limited tissue available via biopsy. We developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort of 211 subjects. The dataset comprises Computed Tomography (CT), Positron Emission Tomography (PET)/CT images, semantic annotations of the tumors as observed on the medical images using a controlled vocabulary, and segmentation maps of tumors in the CT scans. Imaging data are also paired with results of gene mutation analyses, gene expression microarrays and RNA sequencing data from samples of surgically excised tumor tissue, and clinical data, including survival outcomes. This dataset was created to facilitate the discovery of the underlying relationship between tumor molecular and medical image features, as well as the development and evaluation of prognostic medical image biomarkers.
Magnetic resonance imaging of hepatic neoplasms in the rat. [2017]Magnetic resonance imaging (MRI) at microscopic resolution was done on a live rat that had chemically induced hepatic neoplasms. Beginning at the anterior aspect of the liver, 16 contiguous transaxial slices (each 1.25 mm thick) were produced using three-dimensional Fourier transform sequences. The rat had been treated with diethylnitrosamine (200 mg/kg) at 70 days of age, and, subsequently, received periodic implants of 17a-ethynylestradiol for 60 weeks. Carr-Purcell-Meiboom-Gill (CPMG) sequences (repetition time = 2,000 and echo time = 20, 40, 60, 80 ms) were done to give quantitative measures of spin-spin relaxation times (T2). Pixel-by-pixel curve fitting from these multiple images yielded calculated T2 images. Histologic evaluation of three abnormal areas in the liver revealed solid and cystic hepatocellular adenomas. Although lesions were evident in early-echo images of the CPMG sequence, they were more apparent in the late-echo images. This was consistent with longer T2 relaxation times for the lesions. The voxels of dimensions (230 x 230 x 1,250 microns) permitted resolution of volume elements less than 0.07 mm3. This in turn permitted clear delineation of focal lesions less than 3 mm in diameter. The potential for MRI at microscopic resolution in toxicologic research is clearly demonstrated.
Imaging biomarkers in oncology: Basics and application to MRI. [2021]Cancer remains a global killer alongside cardiovascular disease. A better understanding of cancer biology has transformed its management with an increasing emphasis on a personalized approach, so-called "precision cancer medicine." Imaging has a key role to play in the management of cancer patients. Imaging biomarkers that objectively inform on tumor biology, the tumor environment, and tumor changes in response to an intervention complement genomic and molecular diagnostics. In this review we describe the key principles for imaging biomarker development and discuss the current status with respect to magnetic resonance imaging (MRI).
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[Bronchial carcinoma: new radiologic methods]. [2006]New imaging techniques, technical modifications, and new applications of established imaging techniques are discussed with regard to their cost-effectiveness in improving the end result (cure, survival, quality of life). In detecting lung cancer, two methods seem most likely to overcome the known limits of chest radiography: digital radiography with postprocessing and, for risk groups, low-dose CT. Specific diagnosis depends on detection of tiny calcifications, increased vascularization (CT, MRI) or metabolic activity (PET). Clinical staging will be improved by very short time acquisition (MRI), combination of morphologic and biological information (cross sectional techniques) and by observing metabolic activity (PET).
Imaging of lung cancer. [2023]Lung cancer is the leading cause of cancer-related mortality globally. Imaging is essential in the screening, diagnosis, staging, response assessment, and surveillance of patients with lung cancer. Subtypes of lung cancer can have distinguishing imaging appearances. The most frequently used imaging modalities include chest radiography, computed tomography, magnetic resonance imaging, and positron emission tomography. Artificial intelligence algorithms and radiomics are emerging technologies with potential applications in lung cancer imaging.
Advances in Imaging and Automated Quantification of Malignant Pulmonary Diseases: A State-of-the-Art Review. [2022]Quantitative imaging in lung cancer is a rapidly evolving modality in radiology that is changing clinical practice from a qualitative analysis of imaging features to a more dynamic, spatial, and phenotypical characterization of suspected lesions. Some quantitative parameters, such as the use of 18F-FDG PET/CT-derived standard uptake values (SUV), have already been incorporated into current practice as it provides important information for diagnosis, staging, and treatment response of patients with lung cancer. A growing body of evidence is emerging to support the use of quantitative parameters from other modalities. CT-derived volumetric assessment, CT and MRI lung perfusion scans, and diffusion-weighted MRI are some of the examples. Software-assisted technologies are the future of quantitative analyses in order to decrease intra- and inter-observer variability. In the era of "big data", widespread incorporation of radiomics (extracting quantitative information from medical images by converting them into minable high-dimensional data) will allow medical imaging to surpass its current status quo and provide more accurate histological correlations and prognostic value in lung cancer. This is a comprehensive review of some of the quantitative image methods and computer-aided systems to the diagnosis and follow-up of patients with lung cancer.
PET and SPECT in the management of lung cancer. [2019]Lung cancer is the leading cause of cancer deaths in men and women. Most recently in 2001, the Health Care Financing Administration has expanded Medicare coverage for positron emission tomography (PET) to include the diagnosis, staging, and restaging of lung cancer. This review discusses the current metabolic imaging techniques, including the role of PET, single-photon emission computed tomography (SPECT), and the new hybrid PET in the diagnosis, staging, and treatment of lung cancer. The technological advantages, disadvantages, and benefits are compared. PET has the highest detection efficiency than gamma camera based devices. PET when merged with computed tomography (CT) forms the powerful hybrid PET-CT system, capable both of metabolic and anatomic imaging. Clinical imaging pathways based on these newer modalities for the management of lung cancer are proposed. Technological advances in metabolic imaging linked with therapy driven protocols and outcomes may further provide cutting edge modalities that positively impact on dismal lung cancer mortality statistics.
MR imaging of lung cancer. [2019]Since publication of the Radiologic Diagnostic Oncology Group Report in 1991, the clinical application of pulmonary magnetic resonance (MR) imaging to patients with lung cancer has been limited. Computed tomography has been much more widely available for staging of lung cancer in clinical situations. Currently, ventilation and perfusion scintigraphy is the only modality that demonstrates pulmonary function while 2-[fluorine-18]-fluoro-2-deoxy-D-glucose positron emission tomography is the only modality that reveals biological glucose metabolism of lung cancer. However, recent advancements in MR imaging have made it possible to evaluate morphological and functional information in lung cancer patients more accurately and quantitatively. Pulmonary MR imaging may hold significant potential to substitute for nuclear medicine examinations. In this review, we describe recent advances in MR imaging of lung cancer, focusing on (1) characterization of solitary pulmonary nodules; (2) differentiation from secondary change; evaluation of (3) medastinal invasion, (4) chest wall invasion, (5) lymph node metastasis, and (6) distant metastasis; and (7) pulmonary functional imaging. We believe that further basic studies, as well as clinical applications of newer MR techniques, will play an important role in the management of patients with lung cancer.