Longitudinal MRI findings in patients with newly diagnosed glioblastoma after intraoperative radiotherapy

Efficacy and Safety of Intraoperative Radiotherapy for High-Grade Gliomas: A Systematic Review and Meta-Analysis

BACKGROUND AND OBJECTIVES

High-grade gliomas (HGGs) are aggressive tumors of the central nervous system that cause significant morbidity and mortality. Despite advances in surgery and radiation therapy (RT), HGG still has a high incidence of recurrence and treatment failure. Intraoperative radiotherapy (IORT) has emerged as a promising therapeutic approach to achieve local tumor control while sparing normal brain tissue from radiation-induced damage.

METHODS

A systematic review and meta-analysis were conducted following PRISMA guidelines to evaluate the use of IORT for HGG. Eligible studies were included based on specific criteria, and data were independently extracted. Outcomes of interest included complications, IORT failure, survival rates at 12 and 24 months, and mortality.

RESULTS

Sixteen studies comprising 436 patients were included. The overall complication rate after IORT was 17%, with significant heterogeneity observed. The IORT failure rate was 77%, while the survival rates at 12 and 24 months were 74% and 24%, respectively. The mortality rate was 62%.

CONCLUSION

This meta-analysis suggests that IORT may be a promising adjuvant treatment for selected patients with HGG. Despite the high rate of complications and treatment failures, the survival outcomes were comparable or even superior to conventional methods. However, the limitations of the study, such as the lack of a control group and small sample sizes, warrant further investigation through prospective randomized controlled trials to better understand the specific patient populations that may benefit most from IORT. However, the limitations of the study, such as the lack of a control group and small sample sizes, warrant further investigation. Notably, the ongoing RP3 trial (NCT02685605) is currently underway, with the aim of providing a more comprehensive understanding of IORT. Moreover, future research should focus on managing complications associated with IORT to improve its safety and efficacy in treating HGG.

Intraoperative radiotherapy in recurrent IDH-wildtype glioblastoma with gross total resection: A single-center retrospective study

BACKGROUND

Isocitrate dehydrogenase-wildtype (IDHwt) glioblastoma (GBM) is one of the most aggressive primary brain tumors. The recurrence of GBM is almost inevitable. As an adjuvant option to surgery, intraoperative radiotherapy (IORT) is gaining increasing attention in the treatment of glioma. This study is aimed to evaluate the therapeutic efficacy of IORT on recurrent IDHwt GBM.

METHODS

In total, 34 recurrent IDHwt GBM patients who received a second surgery were included in the analysis (17 in the surgery group and 17 in the surgery + IORT group).

RESULTS

The progression-free survival and overall survival after the second surgery were defined as PFS2 and OS2, respectively. The median PFS2 was 7.3 months (95% CI: 6.3-10.5) and 10.6 months (95% CI: 9.3-14.6) for those patients who received surgery and surgery + IORT, respectively. Patients in the surgery + IORT group also had a longer OS2 (12.8 months, 95% CI: 11.4-17.2) than those in the surgery group (9.3 months, 95% CI: 8.9-12.9). The Kaplan-Meier survival curves, analyzed by log-rank test, revealed a statistically significant difference in PFS2 and OS2 between both groups, suggesting that IORT plays an active role in the observed benefits for PFS2 and OS2. The effects of IORT on PFS2 and OS2 were further confirmed by multivariate Cox hazards regression analysis. Two patients in the surgery group developed distant glioma metastases, and no radiation-related complications were observed in the IORT group.

CONCLUSIONS

This study suggests that low-dose IORT may improve the prognosis of recurrent IDHwt GBM patients. Future prospective large-scale studies are needed to validate the efficacy and safety of IORT.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.

Assessment of the hypervascularized fraction of glioblastomas using a volume analysis of dynamic susceptibility contrast-enhanced MRI may help to identify pseudoprogression

PURPOSE

Although perfusion magnetic resonance imaging (MRI) is widely used to identify pseudoprogression, this advanced technique lacks clinical reliability. Our aim was to develop a parameter assessing the hypervascularized fraction of glioblastomas based on volume analysis of dynamic susceptibility contrast-enhanced MRI and evaluate its performance in the diagnosis of pseudoprogression.

METHODS

Patients with primary glioblastoma showing lesion progression on the first follow-up MRI after chemoradiotherapy were enrolled retrospectively. On both initial and first follow-up MRIs, the leakage-corrected cerebral blood volume (CBV) maps were post-processed using the conventional hot-spot method and a volume method, after manual segmentation of the contrast-enhanced delineated lesion. The maximum CBV (rCBVmax) was calculated with both methods. Secondly, the threshold of 2 was applied to the CBV values contained in the entire segmented volume, defining our new parameter: %rCBV>2. The probability of pseudoprogression based on rCBVmax and %rCBV>2 was calculated in logistic regression models and diagnostic performance assessed by receiving operator characteristic curves.

RESULTS

Out of 25 patients, 11 (44%) were classified with pseudoprogression and 14 (56%) with true progression based on the Response Assessement in Neuro-Oncology criteria. rCBVmax was lower for pseudoprogression (3.4 vs. 7.6; p = 0.033) on early follow-up MRI. %rCBV>2, was lower for pseudoprogression on both initial (57.5% vs. 71.3%; p = 0.033) and early follow-up MRIs (22.1% vs. 51.8%; p = 0.0006). On early follow-up MRI, %rCBV>2 had the largest area under the curve for the diagnosis of pseudoprogression: 0.909 [0.725-0.986].

CONCLUSION

The fraction of hypervascularization of glioblastomas as assessed by %rCBV>2 was lower in tumours that subsequently developed pseudoprogression both on the initial and early follow-up MRIs. This fractional parameter may help identify pseudoprogression with greater accuracy than rCBVmax.

Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent

Objective

Material & methods

Results

Conclusion

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Radiomics allows for prediction of pseudoprogression in high-grade gliomas.

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Use of contrast media boosts the performance of the Radiomics prediction model.