Predicting patterns of failure in temporal lobe GBMs: possible implications on radiotherapy treatment portals

A new approach to delineating clinical target volume for radiotherapy of glioblastoma: A phase II trial

Purpose

No consensus has currently been reached regarding the optimal radiation volume for radiotherapy of glioblastoma. Here, we have proposed a new delineation approach to delineating clinical target volume based on the relationship between the growth patterns of glioblastoma and neural pathways. Its safety and efficacy were evaluated in a phase II clinical trial.

Methods

A total of 69 patients with histologically confirmed glioblastoma were enrolled. All patients underwent tumor resection, followed by focal radiotherapy and concomitant temozolomide (TMZ), and then received six cycles of adjuvant TMZ. The gross tumor volume (GTV) was defined as the surgical resection cavity plus any residual enhancing tumor, on contrast enhanced T1-weighted MRI. The clinical target volume (CTV) was delineated through our new approach.

Results

The median recurrence-free survival (RFS) and overall survival (OS) were 11.4 months and 18.2 months, which were better than the previous reports. Relapse was found in 47 patients, of whom 41 patients (87.2%) failed in central, two patients (4.3%) failed in field, and four patients (8.5%) failed in distance. No marginal recurrence was found. Our regimen showed a trend of lower rates of marginal recurrence, and the brain volume of high-dose radiation fields in our regimen was similar to that of EORTC (p = 0.257).

Conclusions

We have proposed a novel method for the delineation of clinical target volume by referencing the nerve fiber bundles for radiotherapy of glioblastoma. The results of the present phase II clinical trial suggest that this approach may be feasible and effective.

Classification of Progression Patterns in Glioblastoma: Analysis of Predictive Factors and Clinical Implications

Background

This study was designed to explore the progression patterns of IDH-wildtype glioblastoma (GBM) at first recurrence after chemoradiotherapy.

Methods

Records from 247 patients who underwent progression after diagnosis of IDH-wildtype GBM was retrospectively reviewed. Progression patterns were classified as either local, distant, subependymal or leptomeningeal dissemination based on the preoperative and serial postoperative radiographic images. The clinical and molecular characteristics of different progression patterns were analyzed.

Results

A total of 186 (75.3%) patients had local progression, 15 (6.1%) patients had distant progression, 33 (13.3%) patients had subependymal dissemination, and 13 (5.3%) patients had leptomeningeal dissemination. The most favorable survival occurred in patients with local progression, while no significant difference of survival was found among patients with distant progression, subependymal or leptomeningeal dissemination who were thereby reclassified into non-local group. Multivariable analysis showed that chemotherapy was a protective factor for non-local progression, while gender of male, subventricular zone (SVZ) involvement and O⁶-methylguanine-DNA-methyltransferase (MGMT) promoter methylation were confirmed as risk factors for non-local progression (P < 0.05). Based on the factors screened by multivariable analysis, a nomogram was constructed which conferred high accuracy in predicting non-local progression. Patients in non-local group could be divided into long- and short-term survivors who differed in the rates of SVZ involvement, MGMT promoter methylation and reirradiation (P < 0.05), and a nomogram integrating these factors showed high accuracy in predicting long-term survivors.

Conclusion

Patients harboring different progression patterns conferred distinct clinical and molecular characteristics. Our nomograms could provide theoretical references for physicians to make more personalized and precise treatment decisions.

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Background

Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.

Methods

This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.

Results

Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.

Conclusion

Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.