Tumor recurrence or treatment-related changes following chemoradiation in patients with glioblastoma: does pathology predict outcomes?

Histopathologically confirmed radiation-induced damage of the brain - an in-depth analysis of radiation parameters and spatio-temporal occurrence

BACKGROUND

Radiation-induced damage (RID) after radiotherapy (RT) of primary brain tumors and metastases can be challenging to clinico-radiographically distinguish from tumor progression. RID includes pseudoprogression and radiation necrosis; the latter being irreversible and often associated with severe symptoms. While histopathology constitutes the diagnostic gold standard, biopsy-controlled clinical studies investigating RID remain limited. Whether certain brain areas are potentially more vulnerable to RID remains an area of active investigation. Here, we analyze histopathologically confirmed cases of RID in relation to the temporal and spatial dose distribution.

METHODS

Histopathologically confirmed cases of RID after photon-based RT for primary or secondary central nervous system malignancies were included. Demographic, clinical, and dosimetric data were collected from patient records and treatment planning systems. We calculated the equivalent dose in 2 Gy fractions (EQD22) and the biologically effective dose (BED2) for normal brain tissue (α/β ratio of 2 Gy) and analyzed the spatial and temporal distribution using frequency maps.

RESULTS

Thirty-three patients were identified. High-grade glioma patients (n = 18) mostly received one normofractionated RT series (median cumulative EQD22 60 Gy) to a large planning target volume (PTV) (median 203.9 ccm) before diagnosis of RID. Despite the low EQD22 and BED2, three patients with an accelerated hyperfractionated RT developed RID. In contrast, brain metastases patients (n = 15; 16 RID lesions) were often treated with two or more RT courses and with radiosurgery or fractionated stereotactic RT, resulting in a higher cumulative EQD22 (median 162.4 Gy), to a small PTV (median 6.7 ccm). All (n = 34) RID lesions occurred within the PTV of at least one of the preceding RT courses. RID in the high-grade glioma group showed a frontotemporal distribution pattern, whereas, in metastatic patients, RID was observed throughout the brain with highest density in the parietal lobe. The cumulative EQD22 was significantly lower in RID lesions that involved the subventricular zone (SVZ) than in lesions without SVZ involvement (median 60 Gy vs. 141 Gy, p = 0.01).

CONCLUSIONS

Accelerated hyperfractionated RT can lead to RID despite computationally low EQD22 and BED2 in high-grade glioma patients. The anatomical location of RID corresponded to the general tumor distribution of gliomas and metastases. The SVZ might be a particularly vulnerable area.

The Next Frontier in Health Disparities—A Closer Look at Exploring Sex Differences in Glioma Data and Omics Analysis, from Bench to Bedside and Back

Sex differences are increasingly being explored and reported in oncology, and glioma is no exception. As potentially meaningful sex differences are uncovered, existing gender-derived disparities mirror data generated in retrospective and prospective trials, real-world large-scale data sets, and bench work involving animals and cell lines. The resulting disparities at the data level are wide-ranging, potentially resulting in both adverse outcomes and failure to identify and exploit therapeutic benefits. We set out to analyze the literature on women’s data disparities in glioma by exploring the origins of data in this area to understand the representation of women in study samples and omics analyses. Given the current emphasis on inclusive study design and research, we wanted to explore if sex bias continues to exist in present-day data sets and how sex differences in data may impact conclusions derived from large-scale data sets, omics, biospecimen analysis, novel interventions, and standard of care management.

A Radiomics Model for Predicting Early Recurrence in Grade II Gliomas Based on Preoperative Multiparametric Magnetic Resonance Imaging

Objective

This study aimed to develop a radiomics model to predict early recurrence (<1 year) in grade II glioma after the first resection.

Methods

The pathological, clinical, and magnetic resonance imaging (MRI) data of patients diagnosed with grade II glioma who underwent surgery and had a recurrence between 2017 and 2020 in our hospital were retrospectively analyzed. After a rigorous selection, 64 patients were eligible and enrolled in the study. Twenty-two cases had a pathologically confirmed recurrent glioma. The cases were randomly assigned using a ratio of 7:3 to either the training set or validation set. T1-weighted image (T1WI), T2-weighted image (T2WI), and contrast-enhanced T1-weighted image (T1CE) were acquired. The minimum-redundancy-maximum-relevancy (mRMR) method alone or in combination with univariate logistic analysis were used to identify the most optimal predictive feature from the three image sequences. Multivariate logistic regression analysis was then used to develop a predictive model using the screened features. The performance of each model in both training and validation datasets was assessed using a receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

A total of 396 radiomics features were initially extracted from each image sequence. After running the mRMR and univariate logistic analysis, nine predictive features were identified and used to build the multiparametric radiomics model. The model had a higher AUC when compared with the univariate models in both training and validation data sets with an AUC of 0.966 (95% confidence interval: 0.949–0.99) and 0.930 (95% confidence interval: 0.905–0.973), respectively. The calibration curves indicated a good agreement between the predictable and the actual probability of developing recurrence. The DCA demonstrated that the predictive value of the model improved when combining the three MRI sequences.

Conclusion

Our multiparametric radiomics model could be used as an efficient and accurate tool for predicting the recurrence of grade II glioma.