The Impact of Stereotactic or Whole Brain Radiotherapy on Neurocognitive Functioning in Adult Patients with Brain Metastases: A Systematic Review and Meta-Analysis

Background & Objectives: Radiotherapy is standard treatment for patients with brain metastases (BMs), although it may lead to radiation-induced cognitive impairment. This review explores the impact of whole-brain radiotherapy (WBRT) or stereotactic radiosurgery (SRS) on cognition. Methods: The PRISMA guidelines were used to identify articles on PubMed and EmBase reporting on objective assessment of cognition before, and at least once after radiotherapy, in adult patients with nonresected BMs. Results: Of the 867 records screened, twenty articles (14 unique studies) were included. WBRT lead to decline in cognitive performance, which stabilized or returned to baseline in patients with survival of at least 9-15 months. For SRS, a decline in cognitive performance was sometimes observed shortly after treatment, but the majority of patients returned to or remained at baseline until a year after treatment. Conclusions: These findings suggest that after WBRT, patients can experience deterioration over a longer period of time. The cognitive side effects of SRS are transient. Therefore, this review advices to choose SRS as this will result in lowest risks for cognitive adverse side effects, irrespective of predicted survival. In an already cognitively vulnerable patient population with limited survival, this information can be used in communicating risks and aid in making educated decisions

Immunomodulation Through Low-Dose Radiation for Severe COVID-19:Lessons From the Past and New Developments

Low-dose radiation therapy (LD-RT) has historically been a successful treatment for pneumonia and is clinically established as an immunomodulating therapy for inflammatory diseases. The ongoing COVID-19 pandemic has elicited renewed scientific interest in LD-RT and multiple small clinical trials have recently corroborated the historical LD-RT findings and demonstrated preliminary efficacy and immunomodulation for the treatment of severe COVID-19 pneumonia. The present review explicates archival medical research data of LD-RT and attempts to translate this into modernized evidence, relevant for the COVID-19 crisis. Additionally, we explore the putative mechanisms of LD-RT immunomodulation, revealing specific downregulation of proinflammatory cytokines that are integral to the development of the COVID-19 cytokine storm induced hyperinflammatory state. Radiation exposure in LD-RT is minimal compared to radiotherapy dosing standards in oncology care and direct toxicity and long-term risk for secondary disease are expected to be low. The recent clinical trials investigating LD-RT for COVID-19 confirm initial treatment safety. Based on our findings we conclude that LD-RT could be an important treatment option for COVID-19 patients that are likely to progress to severity. We advocate the further use of LD-RT in carefully monitored experimental environments to validate its effectiveness, risks and mechanisms of LD-RT

Exploring contrast generalisation in deep learning-based brain MRI-to-CT synthesis

BACKGROUND: Synthetic computed tomography (sCT) has been proposed and increasingly clinically adopted to enable magnetic resonance imaging (MRI)-based radiotherapy. Deep learning (DL) has recently demonstrated the ability to generate accurate sCT from fixed MRI acquisitions. However, MRI protocols may change over time or differ between centres resulting in low-quality sCT due to poor model generalisation. PURPOSE: investigating domain randomisation (DR) to increase the generalisation of a DL model for brain sCT generation. METHODS: CT and corresponding T 1-weighted MRI with/without contrast, T 2-weighted, and FLAIR MRI from 95 patients undergoing RT were collected, considering FLAIR the unseen sequence where to investigate generalisation. A "Baseline" generative adversarial network was trained with/without the FLAIR sequence to test how a model performs without DR. Image similarity and accuracy of sCT-based dose plans were assessed against CT to select the best-performing DR approach against the Baseline. RESULTS: The Baseline model had the poorest performance on FLAIR, with mean absolute error (MAE) = 106 ± 20.7 HU (mean ±σ). Performance on FLAIR significantly improved for the DR model with MAE = 99.0 ± 14.9 HU, but still inferior to the performance of the Baseline+FLAIR model (MAE = 72.6 ± 10.1 HU). Similarly, an improvement in γ-pass rate was obtained for DR vs Baseline. CONCLUSION: DR improved image similarity and dose accuracy on the unseen sequence compared to training only on acquired MRI. DR makes the model more robust, reducing the need for re-training when applying a model on sequences unseen and unavailable for retraining

Quantifying the post-radiation accelerated brain aging rate in glioma patients with deep learning

Background and purpose: Changes of healthy appearing brain tissue after radiotherapy (RT) have been previously observed. Patients undergoing RT may have a higher risk of cognitive decline, leading to a reduced quality of life. The experienced tissue atrophy is similar to the effects of normal aging in healthy individuals. We propose a new way to quantify tissue changes after cranial RT as accelerated brain aging using the BrainAGE framework. Materials and methods: BrainAGE was applied to longitudinal MRI scans of 32 glioma patients. Utilizing a pre-trained deep learning model, brain age is estimated for all patients’ pre-radiotherapy planning and follow-up MRI scans to acquire a quantification of the changes occurring in the brain over time. Saliency maps were extracted from the model to spatially identify which areas of the brain the deep learning model weighs highest for predicting age. The predicted ages from the deep learning model were used in a linear mixed effects model to quantify aging of patients after RT. Results: The linear mixed effects model resulted in an accelerated aging rate of 2.78 years/year, a significant increase over a normal aging rate of 1 (p < 0.05, confidence interval = 2.54–3.02). Furthermore, the saliency maps showed numerous anatomically well-defined areas, e.g.: Heschl's gyrus among others, determined by the model as important for brain age prediction. Conclusion: We found that patients undergoing RT are affected by significant post-radiation accelerated aging, with several anatomically well-defined areas contributing to this aging. The estimated brain age could provide a method for quantifying quality of life post-radiotherapy

Effect of radiation therapy on cerebral cortical thickness in glioma patients: Treatment-induced thinning of the healthy cortex

Background: With overall survival of brain tumors improving, radiation induced brain injury is becoming an increasing issue. One of the effects of radiation therapy (RT) is thinning of the cerebral cortex, which could be one of the factors contributing to cognitive impairments after treatment. In healthy brain, cortex thickness varies between 1 and 4.5 mm. In this study, we assess the effect of RT on the thickness of the cerebral cortex and relate the changes to the local dose. Methods: We identified 28 glioma patients with optimal scan quality. Clinical CTs and MRIs at baseline and 1 year post-RT were collected and coregistered. The scans were processed via an automated image processing pipeline, which enabled measuring changes of the cortical thickness, which were related to local dose. Results: Three areas were identified where significant dose-dependent thinning occurred, with thinning rates of 5, 6, and 26 μm/Gy after 1 year, which corresponds to losses of 5.4%, 7.2%, and 21.6% per 30 Gy per year. The first area was largely located in the right inferior parietal, supramarginal, and superior parietal regions, the second in the right posterior cingulate and paracentral regions, and the third almost completely in the right lateral orbital frontal region. Conclusions: We have identified three areas susceptible to dose-dependent cortical thinning after radiation therapy. Should future prospective studies conclude that irradiation of these areas lead to cognitive decline, they need to be spared in order to prevent this debilitating consequence of treatment

Dose-dependent volume loss in subcortical deep grey matter structures after cranial radiotherapy

Background and purpose: The relation between radiotherapy (RT) dose to the brain and morphological changes in healthy tissue has seen recent increased interest. There already is evidence for changes in the cerebral cortex and white matter, as well as selected subcortical grey matter (GM) structures. We studied this relation in all deep GM structures, to help understand the aetiology of post-RT neurocognitive symptoms. Materials and methods: We selected 31 patients treated with RT for grade II-IV glioma. Pre-RT and 1 year post-RT 3D T1-weighted MRIs were automatically segmented, and the changes in volume of the following structures were assessed: amygdala, nucleus accumbens, caudate nucleus, hippocampus, globus pallidus, putamen, and thalamus. The volumetric changes were related to the mean RT dose received by each structure. Hippocampal volumes were entered into a population-based nomogram to estimate hippocampal age. Results: A significant relation between RT dose and volume loss was seen in all examined structures, except the caudate nucleus. The volume loss rates ranged from 0.16 to 1.37%/Gy, corresponding to 4.9-41.2% per 30 Gy. Hippocampal age, as derived from the nomogram, was seen to increase by a median of 11 years. Conclusion: Almost all subcortical GM structures are susceptible to radiation-induced volume loss, with higher volume loss being observed with increasing dose. Volume loss of these structures is associated with neurological deterioration, including cognitive decline, in neurodegenerative diseases. To support a causal relationship between radiation-induced deep GM loss and neurocognitive functioning in glioma patients, future studies are needed that directly correlate volumetrics to clinical outcomes

Irradiation of the subventricular zone and subgranular zone in high- and low-grade glioma patients: an atlas-based analysis on overall survival

Background: Neural stem cells in the subventricular zone (SVZ) and subgranular zone (SGZ) are hypothesized to support growth of glioma. Therefore, irradiation of the SVZ and SGZ might reduce tumor growth and might improve overall survival (OS). However, it may also inhibit the repair capacity of brain tissue. The aim of this retrospective cohort study is to assess the impact of SVZ and SGZ radiotherapy doses on OS of patients with high-grade (HGG) or low-grade (LGG) glioma. Methods: We included 273 glioma patients who received radiotherapy. We created an SVZ atlas, shared openly with this work, while SGZ labels were taken from the CoBrA atlas. Next, SVZ and SGZ regions were automatically delineated on T1 MR images. Dose and OS correlations were investigated with Cox regression and Kaplan-Meier analysis. Results: Cox regression analyses showed significant hazard ratios for SVZ dose (univariate: 1.029/Gy, P 30.33 Gy) vs 14.0 months (29.11 Gy) vs 15.5 months (<29.11 Gy) median OS, P <. 001). No correlations between dose and OS were found for LGG patients. Conclusion: Irradiation doses on neurogenic areas correlate negatively with OS in patients with HGG. Whether sparing of the SVZ and SGZ during radiotherapy improves OS, should be subject of prospective studies