Brain Neoplasms, Glioma
Conditions
Keywords
Three-Dimensional Arterial Spin Labeling, 3D-ASL, Time-Dependent Diffusion MRI, TDD-MRI, True Progression, Pseudoprogression, Magnetic Resonance Imaging, Diagnostic Accuracy, Multiparametric MRI, Glioma Surgery, RANO, Cerebral Blood Flow, Diffusion MRI
Brief summary
Background: Differentiating true progression (TP) from pseudoprogression (PsP) after glioma surgery remains a major clinical challenge because conventional magnetic resonance imaging (MRI) often cannot reliably distinguish these conditions. Objective: This prospective observational diagnostic accuracy study aims to evaluate the value of multiparametric imaging based on three-dimensional arterial spin labeling (3D-ASL) combined with time-dependent diffusion MRI (TDD-MRI) for differentiating TP from PsP in postoperative glioma patients. Methods: Consecutive adult patients with suspected tumor progression after glioma surgery will undergo routine MRI, 3D-ASL, and TDD-MRI examinations. Quantitative perfusion and diffusion parameters will be extracted, and a combined imaging model will be developed and evaluated. Final diagnosis will be established according to pathological findings when available or by longitudinal clinical and imaging follow-up based on the Response Assessment in Neuro-Oncology (RANO) criteria. Expected Outcomes: The primary outcome is the diagnostic performance of the combined imaging model, assessed by the area under the receiver operating characteristic curve (AUC). The study is expected to provide a noninvasive imaging strategy for distinguishing TP from PsP and to support clinical decision-making during postoperative follow-up.
Detailed description
Glioma is the most common primary malignant tumor of the central nervous system and is characterized by high invasiveness and a high recurrence rate. During postoperative follow-up after surgery and adjuvant therapy, newly developed or enlarged contrast-enhancing lesions may represent either true progression (TP) or treatment-related pseudoprogression (PsP). Because these entities require substantially different clinical management, accurate differentiation is essential. Histopathological confirmation remains the reference standard but is invasive and not feasible for all patients. Conventional MRI has limited diagnostic accuracy because TP and PsP often demonstrate overlapping imaging characteristics. Advanced functional MRI techniques have therefore attracted increasing interest for improving noninvasive diagnosis. Three-dimensional arterial spin labeling (3D-ASL) provides quantitative assessment of cerebral perfusion without exogenous contrast agents, whereas time-dependent diffusion MRI (TDD-MRI) characterizes tissue microstructure by measuring water diffusion under different diffusion times. These techniques provide complementary information regarding tumor vascularity and cellular architecture. This is a single-center, prospective observational diagnostic accuracy study conducted at Lanzhou University Second Hospital. Approximately 75 consecutive postoperative glioma patients with suspected disease progression will be enrolled. All participants will undergo routine MRI, 3D-ASL, and TDD-MRI examinations according to a standardized imaging protocol. Quantitative imaging parameters, including relative cerebral blood flow (rCBF), ADC20Hz, ADC40Hz, Cellularity, and Diameter, will be extracted after image preprocessing and lesion segmentation. Participants will not receive any additional therapeutic intervention as part of the study. Clinical management will be determined by treating physicians according to routine clinical practice. Final classification of TP or PsP will be established using pathological confirmation whenever available or comprehensive longitudinal clinical and imaging follow-up according to the RANO 2.0 criteria. The primary objective is to evaluate the diagnostic performance of the combined 3D-ASL and TDD-MRI model using the area under the receiver operating characteristic curve (AUC). Secondary objectives include evaluating the diagnostic performance of individual imaging parameters, comparing diagnostic models, assessing calibration and clinical utility, determining interobserver agreement, and exploring the influence of clinicopathological factors on model performance. The findings of this study are expected to establish a reliable, noninvasive multiparametric MRI strategy for differentiating TP from PsP after glioma surgery, thereby facilitating individualized postoperative management and reducing unnecessary invasive procedures.
Interventions
DIAGNOSTIC_TESTMultiparametric MRI (3D-ASL and TDD-MRI)
Participants undergo standardized multiparametric magnetic resonance imaging, including routine MRI, three-dimensional arterial spin labeling (3D-ASL), and time-dependent diffusion MRI (TDD-MRI). Quantitative perfusion and diffusion parameters are extracted for evaluation of their diagnostic performance in differentiating true progression from pseudoprogression after glioma surgery. No experimental therapeutic intervention is administered as part of the study.
Sponsors
Lanzhou University Second Hospital
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Age 18 years or older. * Histopathologically confirmed glioma. * Received standard postoperative treatment or routine clinical treatment. * New or enlarged contrast-enhancing lesion on follow-up MRI suggestive of tumor progression. * Able to undergo routine MRI, three-dimensional arterial spin labeling (3D-ASL), and time-dependent diffusion MRI (TDD-MRI). * MRI image quality sufficient for image processing, registration, lesion segmentation, and quantitative parameter extraction. * Written informed consent provided by the participant or legally authorized representative.
Exclusion criteria
* Incomplete clinical, pathological, or imaging follow-up data. * MRI images with severe motion artifacts, susceptibility artifacts, or geometric distortion affecting image analysis. * Failure to complete routine MRI, 3D-ASL, or TDD-MRI examinations according to the study protocol. * Lesions too small or poorly defined for reliable three-dimensional volume-of-interest delineation and parameter extraction. * Any other condition judged by the investigators to make participation inappropriate.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| AUC of the Combined 3D-ASL and TDD-MRI Model | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of the combined three-dimensional arterial spin labeling (3D-ASL) and time-dependent diffusion MRI (TDD-MRI) model for differentiating true progression from pseudoprogression after glioma surgery. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| AUC of rCBFmean | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of mean relative cerebral blood flow (rCBFmean) for differentiating true progression from pseudoprogression. |
| AUC of rCBFmax | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of maximum relative cerebral blood flow (rCBFmax) for differentiating true progression from pseudoprogression. |
| AUC of rCBFmin | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of minimum relative cerebral blood flow (rCBFmin) for differentiating true progression from pseudoprogression. |
| AUC of ADC20Hz | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of ADC20Hz derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. |
| AUC of ADC40Hz | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of ADC40Hz derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. |
| AUC of Cellularity | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of the Cellularity parameter derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. |
| AUC of Diameter | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Area under the receiver operating characteristic curve (AUC) of the Diameter parameter derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. |
| Sensitivity of the Combined 3D-ASL and TDD-MRI Model | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Sensitivity of the combined 3D-ASL and TDD-MRI model for differentiating true progression from pseudoprogression. |
| Specificity of the Combined 3D-ASL and TDD-MRI Model | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Specificity of the combined 3D-ASL and TDD-MRI model for differentiating true progression from pseudoprogression. |
| Youden Index of the Combined 3D-ASL and TDD-MRI Model | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Youden index of the combined 3D-ASL and TDD-MRI model for differentiating true progression from pseudoprogression. |
| Optimal Cutoff of the Combined 3D-ASL and TDD-MRI Model | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Optimal cutoff value of the combined 3D-ASL and TDD-MRI model determined from receiver operating characteristic curve analysis. |
| AUC Difference Between the Combined Model and the Best Individual MRI Parameter | From enrollment through completion of clinical and imaging follow-up (up to 12 months) | Difference in AUC between the combined 3D-ASL and TDD-MRI model and the best-performing individual MRI parameter. |
| Hosmer-Lemeshow Goodness-of-Fit P Value | After completion of model construction and statistical analysis | Hosmer-Lemeshow goodness-of-fit test P value for evaluating calibration of the combined 3D-ASL and TDD-MRI model. |
| Clinical Net Benefit of the Combined 3D-ASL and TDD-MRI Model | After completion of model construction and statistical analysis | Clinical net benefit of the combined 3D-ASL and TDD-MRI model assessed using decision curve analysis. |
| Interobserver Agreement for MRI Parameter Measurements | At image analysis after completion of MRI examinations | Interobserver agreement for quantitative MRI parameter measurements assessed using the intraclass correlation coefficient (ICC). |
Countries
China
Outcome results
None listed