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| ID | Type | Description | Link |
|---|---|---|---|
| NCI-2018-01890 | Registry Identifier | CTRP (Clinical Trial Reporting Program) | |
| 6B-17-2 | Other Identifier | USC / Norris Comprehensive Cancer Center | |
| P30CA014089 | U.S. NIH Grant/Contract | View source | |
| R33CA225400 | U.S. NIH Grant/Contract | View source |
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Slow accrual
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| Name | Class |
|---|---|
| National Cancer Institute (NCI) | NIH |
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Dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) is a potentially powerful diagnostic tool for the management of brain cancer and other conditions in which the blood-brain barrier is compromised. This trial studies how well precise DCE MRI works in diagnosing participants with high grade glioma that has come back or melanoma that has spread to the brain. The specially-tailored acquisition and reconstruction (STAR) DCE MRI could provide improved assessment of brain tumor status and response to therapy.
PRIMARY OBJECTIVES:
I. To optimize and technically validate specially-tailored acquisition and reconstruction (STAR) DCE-MRI based on the accuracy and reproducibility of whole-brain tracer-kinetic (TK) parameter maps.
SECONDARY OBJECTIVES:
I. To develop a robust clinical implementation of STAR DCE-MRI. II. To clinically evaluate STAR DCE-MRI in patients with brain tumors.
OUTLINE: Participants are assigned to 1 of 2 cohorts.
COHORT I: Participants with recurrent high-grade glioma undergo STAR DCE-MRI every 2 months, and just prior to and 4-6 weeks after starting bevacizumab treatment. If there is concern for tumor progression (i.e. increased contrast enhancement), more frequent MRI scans will be scheduled.
COHORT II: Participants with melanoma brain metastases undergo STAR DCE-MRI at baseline and 4-6 weeks after therapy. Participants may undergo more frequent MRI if there is concern for tumor progression.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Cohort I (STAR DCE-MRI) | Experimental | Participants with recurrent high-grade glioma undergo STAR DCE-MRI every 2 months, and just prior to and 4-6 weeks after starting bevacizumab treatment. Participants may undergo more frequent MRI if there is concern for tumor progression. |
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| Cohort II (STAR DCE-MRI) | Experimental | Participants with melanoma brain metastases undergo STAR DCE-MRI at baseline and 4-6 weeks after therapy. Participants may undergo more frequent MRI if there is concern for tumor progression. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Dynamic Contrast-Enhanced Magnetic Resonance Imaging | Other | Undergo STAR DCE-MRI |
|
| Measure | Description | Time Frame |
|---|---|---|
| Volume transfer constant (Ktrans) | The raw data will be acquired at the voxel level. Then the analytic parameters will be extracted from voxel-wise data such as the mean, median, interquartile range, skewness and kurtosis. Receiver-operating characteristic curves (ROC) will be used to illustrate the univariate prediction accuracy for each parameter in predicting the clinically determined outcome. The pattern of change with different clinical response status will be visually illustrated using spaghetti plots or other graphical approaches. Classification and Regression Tree (CART) with 10-fold cross validation will be used for building the final prediction model and determine the diagnostic cut point(s). CART analysis will also include demographics, comorbidity information, and relevant biological variables including sex. The final model accuracy will be assessed using area under the curve (AUC) when fitting a ROC curve using predicted outcome against the actual outcome. | Up to 3 years |
| Fractional plasma volume (vp) | The raw data will be acquired at the voxel level. Then the analytic parameters will be extracted from voxel-wise data such as the mean, median, interquartile range, skewness and kurtosis. ROC will be used to illustrate the univariate prediction accuracy for each parameter in predicting the clinically determined outcome. The pattern of change with different clinical response status will be visually illustrated using spaghetti plots or other graphical approaches. CART with 10-fold cross validation will be used for building the final prediction model and determine the diagnostic cut point(s). CART analysis will also include demographics, comorbidity information, and relevant biological variables including sex. The final model accuracy will be assessed using AUC when fitting a ROC curve using predicted outcome against the actual outcome. | Up to 3 years |
| Fractional extravascular-extracellular space volume (ve) | The raw data will be acquired at the voxel level. Then the analytic parameters will be extracted from voxel-wise data such as the mean, median, interquartile range, skewness and kurtosis. ROC will be used to illustrate the univariate prediction accuracy for each parameter in predicting the clinically determined outcome. The pattern of change with different clinical response status will be visually illustrated using spaghetti plots or other graphical approaches. CART with 10-fold cross validation will be used for building the final prediction model and determine the diagnostic cut point(s). CART analysis will also include demographics, comorbidity information, and relevant biological variables including sex. The final model accuracy will be assessed using AUC when fitting a ROC curve using predicted outcome against the actual outcome. |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Krishna Nayak, PhD | University of Southern California | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| USC / Norris Comprehensive Cancer Center | Los Angeles | California | 90033 | United States |
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| Bevacizumab Injection | Other | Bevacizumab will be give to participants who have recurrent high-grade glioma as part of standard of care. |
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| Up to 3 years |
| Model-free initial area under the contrast agent concentration curve (iAUC) | The raw data will be acquired at the voxel level. Then the analytic parameters will be extracted from voxel-wise data such as the mean, median, interquartile range, skewness and kurtosis. ROC will be used to illustrate the univariate prediction accuracy for each parameter in predicting the clinically determined outcome. The pattern of change with different clinical response status will be visually illustrated using spaghetti plots or other graphical approaches. CART with 10-fold cross validation will be used for building the final prediction model and determine the diagnostic cut point(s). CART analysis will also include demographics, comorbidity information, and relevant biological variables including sex. The final model accuracy will be assessed using AUC when fitting a ROC curve using predicted outcome against the actual outcome. | Up to 3 years |
| ID | Term |
|---|---|
| D001932 | Brain Neoplasms |
| D008545 | Melanoma |
| ID | Term |
|---|---|
| D016543 | Central Nervous System Neoplasms |
| D009423 | Nervous System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D018358 | Neuroendocrine Tumors |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
| D009380 | Neoplasms, Nerve Tissue |
| D018326 | Nevi and Melanomas |
| D012878 | Skin Neoplasms |
| D012871 | Skin Diseases |
| D017437 | Skin and Connective Tissue Diseases |
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| ID | Term |
|---|---|
| D000068258 | Bevacizumab |
| ID | Term |
|---|---|
| D061067 | Antibodies, Monoclonal, Humanized |
| D000911 | Antibodies, Monoclonal |
| D000906 | Antibodies |
| D007136 | Immunoglobulins |
| D007162 | Immunoproteins |
| D001798 | Blood Proteins |
| D011506 | Proteins |
| D000602 | Amino Acids, Peptides, and Proteins |
| D012712 | Serum Globulins |
| D005916 | Globulins |
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