Dataset Information

Peritumoral edema status of glioblastoma identifies patients reaching long-term disease control with specific progression patterns after tumor resection and high-dose proton boost.

ABSTRACT:

Background

Glioblastoma peritumoral edema (PE) extent is associated with survival and progression pattern after tumor resection and radiotherapy (RT). To increase tumor control, proton beam was adopted to give high-dose boost (> 90 Gy). However, the correlation between PE extent and prognosis of glioblastoma after postoperative high-dose proton boost (HDPB) therapy stays unknown. We intend to utilize the PE status to classify the survival and progression patterns.

Methods

Patients receiving HDPB (96.6 GyE) were retrospectively evaluated. Limited peritumoral edema (LPE) was defined as PE extent < 3 cm with a ratio of PE extent to tumor maximum diameter of < 0.75. Extended progressive disease (EPD) was defined as progression of tumors extending > 1 cm from the tumor bed edge.

Results

After long-term follow-up (median 88.7, range 63.6-113.8 months) for surviving patients with (n = 13) and without (n = 32) LPE, the median overall survival (OS) and progression-free survival (PFS) were 77.2 vs. 16.7 months (p = 0.004) and 13.6 vs. 8.6 months (p = 0.02), respectively. In multivariate analyses combined with factors of performance, age, tumor maximum diameter, and tumor resection extent, LPE remained a significant factor for favorable OS and PFS. The rates of 5-year complete response, EPD, and distant metastasis with and without LPE were 38.5% vs. 3.2% (p = 0.005), 7.7% vs. 40.6% (p = 0.04), and 0% vs. 34.4% (p = 0.02), respectively.

Conclusions

The LPE status effectively identified patients with relative long-term control and specific progression patterns after postoperative HDPB for glioblastoma.

SUBMITTER: Liang HT

PROVIDER: S-EPMC8557163 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature

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Publications

Peritumoral edema status of glioblastoma identifies patients reaching long-term disease control with specific progression patterns after tumor resection and high-dose proton boost.

Liang Hsiang-Kuang Tony HT Mizumoto Masashi M Ishikawa Eiichi E Matsuda Masahide M Tanaka Keiichi K Kohzuki Hidehiro H Numajiri Haruko H Oshiro Yoshiko Y Okumura Toshiyuki T Matsumura Akira A Sakurai Hideyuki H

Journal of cancer research and clinical oncology 20210830 12

<h4>Background</h4>Glioblastoma peritumoral edema (PE) extent is associated with survival and progression pattern after tumor resection and radiotherapy (RT). To increase tumor control, proton beam was adopted to give high-dose boost (> 90 Gy). However, the correlation between PE extent and prognosis of glioblastoma after postoperative high-dose proton boost (HDPB) therapy stays unknown. We intend to utilize the PE status to classify the survival and progression patterns.<h4>Methods</h4>Patients ...[more]

PMID: 34459971

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Project description:ObjectiveThe goal of this study was to develop a nomogram that integrates clinical data to predict the likelihood of severe postoperative peritumoral brain edema (PTBE) following the surgical removal of intracranial meningioma.MethodWe included 152 patients diagnosed with meningioma who were admitted to the Department of Neurosurgery at the Affiliated People's Hospital of Jiangsu University between January 2016 and March 2023. Clinical characteristics were collected from the hospital's medical record system. Factors associated with severe postoperative PTBE were identified through univariate and LASSO regression analyses of clinical, pathological, and radiological features. A multivariate logistic regression analysis was then performed incorporating all features. Based on these analyses, we developed five predictive models using R software: conventional logistic regression, XGBoost, random forest, support vector machine (SVM), and k-nearest neighbors (KNN). Model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and conducting decision curve analysis (DCA). The most optimal model was used to create a nomogram for visualization. The nomogram was validated using both a validation set and clinical impact curve analysis. Calibration curves assessed the accuracy of the clinical-radiomics nomogram in predicting outcomes, with Brier scores used as an indicator of concordance. DCA was employed to determine the clinical utility of the models by estimating net benefits at various threshold probabilities for both training and testing groups.ResultsThe study involved 151 patients, with a prevalence of severe postoperative PTBE at 35.1%. Univariate logistic regression identified four potential risk factors, and LASSO regression identified four significant risk factors associated with severe postoperative PTBE. Multivariate logistic regression revealed three independent predictors: preoperative edema index, tumor enhancement intensity on MRI, and the number of large blood vessels supplying the tumor. Among all models, the conventional logistic model showed the best performance, with AUCs of 0.897 (95% CI: 0.829-0.965) and DCA scores of 0.719 (95% CI: 0.563-0.876) for each cohort, respectively. We developed a nomogram based on this model to predict severe postoperative PTBE in both training and testing cohorts. Calibration curves and Hosmer-Lemeshow tests indicated excellent agreement between predicted probabilities and observed outcomes. The Brier scores were 10.7% (95% CI: 6.7-14.7) for the training group and 25% (95% CI: 15.2-34.8) for the testing group. DCA confirmed that the nomogram provided superior net benefit across various risk thresholds for predicting severe postoperative PTBE, with a threshold probability range from 0 to 81%.ConclusionUtilizing conventional logistic regression within machine learning frameworks, we developed a robust prediction model. The clinical-radiological nomogram, based on conventional logistic regression, integrated clinical characteristics to enhance the prediction accuracy for severe PTBE in patients following intracranial meningioma resection. This nomogram showed promise in aiding clinicians to create personalized and optimal treatment plans by providing precise forecasts of severe PTBE.

Project description:BACKGROUND AND PURPOSE:Damage to the blood-brain barrier with subsequent contrast enhancement is a hallmark of glioblastoma. Non-enhancing tumor invasion into the peritumoral edema is, however, not usually visible on conventional magnetic resonance imaging. New quantitative techniques using relaxometry offer additional information about tissue properties. The aim of this study was to evaluate longitudinal relaxation R1, transverse relaxation R2, and proton density in the peritumoral edema in a group of patients with malignant glioma before surgery to assess whether relaxometry can detect changes not visible on conventional images. METHODS:In a prospective study, 24 patients with suspected malignant glioma were examined before surgery. A standard MRI protocol was used with the addition of a quantitative MR method (MAGIC), which measured R1, R2, and proton density. The diagnosis of malignant glioma was confirmed after biopsy/surgery. In 19 patients synthetic MR images were then created from the MAGIC scan, and ROIs were placed in the peritumoral edema to obtain the quantitative values. Dynamic susceptibility contrast perfusion was used to obtain cerebral blood volume (rCBV) data of the peritumoral edema. Voxel-based statistical analysis was performed using a mixed linear model. RESULTS:R1, R2, and rCBV decrease with increasing distance from the contrast-enhancing part of the tumor. There is a significant increase in R1 gradient after contrast agent injection (P < .0001). There is a heterogeneous pattern of relaxation values in the peritumoral edema adjacent to the contrast-enhancing part of the tumor. CONCLUSION:Quantitative analysis with relaxometry of peritumoral edema in malignant gliomas detects tissue changes not visualized on conventional MR images. The finding of decreasing R1 and R2 means shorter relaxation times closer to the tumor, which could reflect tumor invasion into the peritumoral edema. However, these findings need to be validated in the future.

Dataset Information

Peritumoral edema status of glioblastoma identifies patients reaching long-term disease control with specific progression patterns after tumor resection and high-dose proton boost.

Background

Methods

Results

Conclusions

Publications

Peritumoral edema status of glioblastoma identifies patients reaching long-term disease control with specific progression patterns after tumor resection and high-dose proton boost.

Similar Datasets

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