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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| True Progression (TP) | Participants with suspected postoperative glioma progression who are ultimately diagnosed with true progression based on histopathological findings when available or comprehensive longitudinal clinical and imaging follow-up according to the RANO 2.0 criteria. All participants undergo routine MRI, three-dimensional arterial spin labeling (3D-ASL), and time-dependent diffusion MRI (TDD-MRI). |
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| Pseudoprogression (PsP) | Participants with suspected postoperative glioma progression who are ultimately diagnosed with pseudoprogression based on histopathological findings when available or comprehensive longitudinal clinical and imaging follow-up according to the RANO 2.0 criteria. All participants undergo routine MRI, three-dimensional arterial spin labeling (3D-ASL), and time-dependent diffusion MRI (TDD-MRI). |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Multiparametric MRI (3D-ASL and TDD-MRI) | Diagnostic Test | 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. |
| Measure | Description | Time Frame |
|---|---|---|
| AUC of the Combined 3D-ASL and TDD-MRI Model | 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. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| Measure | Description | Time Frame |
|---|---|---|
| AUC of rCBFmean | Area under the receiver operating characteristic curve (AUC) of mean relative cerebral blood flow (rCBFmean) for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| AUC of rCBFmax |
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Inclusion Criteria:
Exclusion Criteria:
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Adult patients with histopathologically confirmed glioma who develop suspected tumor progression during postoperative follow-up at Lanzhou University Second Hospital and undergo routine MRI, three-dimensional arterial spin labeling (3D-ASL), and time-dependent diffusion MRI (TDD-MRI).
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Lanzhou University Second Hospital | Lanzhou | Gansu | 730030 | China |
Individual participant data will not be shared because the study data contain potentially identifiable clinical and imaging information. De-identified aggregate results may be reported in publications.
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| ID | Term |
|---|---|
| D005910 | Glioma |
| D001932 | Brain Neoplasms |
| ID | Term |
|---|---|
| D018302 | Neoplasms, Neuroepithelial |
| D017599 | Neuroectodermal Tumors |
| D009373 | Neoplasms, Germ Cell and Embryonal |
| D009370 | Neoplasms by Histologic Type |
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| ID | Term |
|---|---|
| D000081364 | Multiparametric Magnetic Resonance Imaging |
| ID | Term |
|---|---|
| D008279 | Magnetic Resonance Imaging |
| D014054 | Tomography |
| D003952 | Diagnostic Imaging |
| D019937 | Diagnostic Techniques and Procedures |
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|
Area under the receiver operating characteristic curve (AUC) of maximum relative cerebral blood flow (rCBFmax) for differentiating true progression from pseudoprogression. |
| From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| AUC of rCBFmin | Area under the receiver operating characteristic curve (AUC) of minimum relative cerebral blood flow (rCBFmin) for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| AUC of ADC20Hz | Area under the receiver operating characteristic curve (AUC) of ADC20Hz derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| AUC of ADC40Hz | Area under the receiver operating characteristic curve (AUC) of ADC40Hz derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| AUC of Cellularity | Area under the receiver operating characteristic curve (AUC) of the Cellularity parameter derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| AUC of Diameter | Area under the receiver operating characteristic curve (AUC) of the Diameter parameter derived from time-dependent diffusion MRI for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| Sensitivity of the Combined 3D-ASL and TDD-MRI Model | Sensitivity of the combined 3D-ASL and TDD-MRI model for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| Specificity of the Combined 3D-ASL and TDD-MRI Model | Specificity of the combined 3D-ASL and TDD-MRI model for differentiating true progression from pseudoprogression. | 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 | Youden index of the combined 3D-ASL and TDD-MRI model for differentiating true progression from pseudoprogression. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| Optimal Cutoff of the Combined 3D-ASL and TDD-MRI Model | Optimal cutoff value of the combined 3D-ASL and TDD-MRI model determined from receiver operating characteristic curve analysis. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| AUC Difference Between the Combined Model and the Best Individual MRI Parameter | Difference in AUC between the combined 3D-ASL and TDD-MRI model and the best-performing individual MRI parameter. | From enrollment through completion of clinical and imaging follow-up (up to 12 months) |
| Hosmer-Lemeshow Goodness-of-Fit P Value | Hosmer-Lemeshow goodness-of-fit test P value for evaluating calibration 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 | Clinical net benefit of the combined 3D-ASL and TDD-MRI model assessed using decision curve analysis. | After completion of model construction and statistical analysis |
| Interobserver Agreement for MRI Parameter Measurements | Interobserver agreement for quantitative MRI parameter measurements assessed using the intraclass correlation coefficient (ICC). | At image analysis after completion of MRI examinations |
| D009369 | Neoplasms |
| D009375 | Neoplasms, Glandular and Epithelial |
| D009380 | Neoplasms, Nerve Tissue |
| D016543 | Central Nervous System Neoplasms |
| D009423 | Nervous System Neoplasms |
| D009371 | Neoplasms by Site |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
| D009422 | Nervous System Diseases |
| D003933 | Diagnosis |