Pineal parenchymal tumors (PPTs) are uncommon in general and rare in the adult population. Currently, the optimal treatment for PPT of intermediate differentiation (PPTID) in older patients is unknown. Stereotactic radiosurgery (SRS) has been used as both primary and adjuvant therapy, with single or fractionated doses using Gamma Knife (Elekta, Stockholm, Sweden) or CyberKnife (Accuray Inc., Madison, WI). This article presents the case of a 77-year-old woman with a biopsy-confirmed PPTID. She was treated with single fraction VMAT (volumetric modulated arc therapy)-based SRS on a Varian TrueBeam Edge linac (Varian Medical Systems, Palo Alto, CA) using multiple non-coplanar arcs. Contouring and treatment planning were performed on contrast-enhanced MRI and CT images. Accurate patient set-up and immobilization were achieved with an open-faced thermoplastic mask, real-time motion management using an optical surface monitoring system, and kV CBCT prior to each arc. The patient has shown excellent response for tumor size with frequent follow-up up to 84 months, but 25 months later developed double-vision and headaches with MRI revealing decreasing tumor size but enhancement and FLAIR changes in the adjacent brain parenchyma suggestive of radiation necrosis. These changes stabilized as of 32 months of follow-up, and then started to decrease by 35 months.

Purpose: To evaluate the effectiveness of abdominal compression (AC) as a respiratory motion management method for the heart and stomach during stereotactic arrhythmia radioablation (STAR). Methods: 4D computed tomography (4DCT) scans of patients imaged with AC or without AC (free-breathing: FB) were obtained from ventricular-tachycardia (VT) (n = 3), lung cancer (n = 18), and liver cancer (n = 18) patients. Patients treated for VT were imaged both FB and with AC. Lung and liver patients were imaged once with FB or with AC, respectively. The heart, left ventricle (LV), LV components (LVCs), and stomach were contoured on each phase of the 4DCTs. Centre of mass (COM) translations in the left/right (LR), ant/post (AP), and sup/inf (SI) directions were measured for each structure. Minimum distances between LVCs and the stomach over the respiratory cycle were also measured on each 4DCT phase. Mann-Whitney U-tests were performed between AC and FB datasets with a significance of alpha = 0.05. Results: No statistical difference (all p values were >0.05) was found in COM translations between FB and AC patient datasets for all contoured cardiac structures. A reduction in COM translation with AC relative to FB was patient, direction, and structure specific for the three VT patients. A significant decrease in the AP range of motion of the stomach was observed under AC compared to FB. No statistical difference was found between minimum distances to the stomach and LVCs between FB and AC. Conclusions: AC was not a consistent motion management method for STAR, nor does not uniformly affect the separation distance between LVCs and the stomach. If AC is employed in future STAR protocols, the motion of the target volume and its relative distance to the stomach should be compared on two 4DCTs: one while the patient is FB and one under AC.

Background:Metastatic complications are a major cause of cancer-related morbidity, with up to 40% of cancer patients experiencing at least one brain metastasis. Earlier detection may significantly improve patient outcomes and overall survival. We investigated machine learning (ML) models for early detection of brain metastases based on diffusion weighted imaging (DWI) radiomics.Methods:Longitudinal diffusion imaging from 116 patients previously treated with stereotactic radiosurgery (SRS) for brain metastases were retrospectively analyzed. Clinical contours from 600 metastases were extracted from radiosurgery planning computed tomography, and rigidly registered to corresponding contrast enhanced-T1 and apparent diffusion coefficient (ADC) maps. Contralateral contours located in healthy brain tissue were used as control. The dataset consisted of (I) radiomic features using ADC maps, (II) radiomic feature change calculated using timepoints before the metastasis manifested on contrast enhanced-T1, (III) primary cancer, and (IV) anatomical location. The dataset was divided into training and internal validation sets using an 80/20 split with stratification. Four classification algorithms [Linear Support Vector Machine (SVM), Random Forest (RF), AdaBoost, and XGBoost] underwent supervised classification training, with contours labeled either 'control' or 'metastasis'. Hyperparameters were optimized towards balanced accuracy. Various model metrics (receiver operating characteristic curve area scores, accuracy, recall, and precision) were calculated to gauge performance.Results:The radiomic and clinical data set, feature engineering, and ML models developed were able to identify metastases with an accuracy of up to 87.7% on the training set, and 85.8% on an unseen test set. XGBoost and RF showed superior accuracy (XGBoost: 0.877±0.021 and 0.833±0.47, RF: 0.823±0.024 and 0.858±0.045) for training and validation sets, respectively. XGBoost and RF also showed strong area under the receiver operating characteristic curve (AUC) performance on the validation set (0.910±0.037 and 0.922±0.034, respectively). AdaBoost performed slightly lower in all metrics. SVM model generalized poorly with the internal validation set. Important features involved changes in radiomics months before manifesting on contrast enhanced-T1.Conclusions:The proposed models using diffusion-based radiomics showed encouraging results in differentiating healthy brain tissue from metastases using clinical imaging data. These findings suggest that longitudinal diffusion imaging and ML may help improve patient care through earlier diagnosis and increased patient monitoring/follow-up. Future work aims to improve model classification metrics, robustness, user-interface, and clinical applicability.

Abstract Cranial irradiation is part of the standard of care for treating pediatric brain tumors. However, ionizing radiation can trigger serious long-term neurologic sequelae, including oligodendrocyte and brain white matter loss enabling neurocognitive decline in children surviving brain cancer. Oxidative stress-mediated oligodendrocyte precursor cell (OPC) radiosensitivity has been proposed as a possible explanation for this. Here, however, we demonstrate that antioxidants fail to improve OPC viability after irradiation, despite suppressing oxidative stress, suggesting an alternative etiology for OPC radiosensitivity. Using systematic approaches, we find that OPCs have higher irradiation-induced and endogenous γH2AX foci compared to neural stem cells, neurons, astrocytes and mature oligodendrocytes, and these correlate with replication-associated DNA double strand breakage. Furthermore, OPCs are reliant upon ATR kinase and Mre11 nuclease-dependent processes for viability, are more sensitive to drugs increasing replication fork collapse, and display synthetic lethality with PARP inhibitors after irradiation. This suggests an insufficiency for homology-mediated DNA repair in OPCs—a model that is supported by evidence of normal RPA but reduced RAD51 filament formation at resected lesions in irradiated OPCs. We therefore propose a DNA repair-centric mechanism of OPC radiosensitivity, involving chronically-elevated replication stress combined with ‘bottlenecks’ in RAD51-dependent DNA repair that together reduce radiation resilience.

Objective.Automated Stereotactic Radiosurgery (SRS) planning solutions improve clinical efficiency and reduce treatment plan variability. Available commercial solutions employ a template-based strategy that may not be optimal for all SRS patients. This study compares a novel beam angle optimized Volumetric Modulated Arc Therapy (VMAT) planning solution for multi-metastatic SRS to the commercial solution HyperArc.Approach.Stereotactic Optimized Automated Radiotherapy (SOAR) performs automated plan creation by combining collision prediction, beam angle optimization, and dose optimization to produce individualized high-quality SRS plans using Eclipse Scripting. In this retrospective study 50 patients were planned using SOAR and HyperArc. Assessed dose metrics included the Conformity Index (CI), Gradient Index (GI), and doses to organs-at-risk. Complexity metrics evaluated the modulation, gantry speed, and dose rate complexity. Plan dosimetric quality, and complexity were compared using double-sided Wilcoxon signed rank tests (α= 0.05) adjusted for multiple comparisons.Main Results.The median target CI was 0.82 with SOAR and 0.79 with HyperArc (p < .001). Median GI was 1.85 for SOAR and 1.68 for HyperArc (p < .001). The median V12Gy normal brain volume for SOAR and HyperArc were 7.76 cm3and 7.47 cm3respectively. Median doses to the eyes, lens, optic nerves, and optic chiasm were statistically significant favoring SOAR. The SOAR algorithm scored lower for all complexity metrics assessed.Significance.In-house developed automated planning solutions are a viable alternative to commercial solutions. SOAR designs high-quality patient-specific SRS plans with a greater degree of versatility than template-based methods.

BACKGROUND AND PURPOSE:Linac-based stereotactic radiosurgery (SRS) planning for multi-metastatic cases is a complex and intensive process. A manual planning strategy starts with a template-based set of beam angles and applies modifications though a trial and error process. Beam angle optimization uses patient specific geometric heuristics to determine beam angles that provide optimal target coverage and avoid treating through Organs-at-Risk (OARs). This study expands on a collision prediction application developed using an application programming interface, integrating beam angle optimization and collision prediction into a Stereotactic Optimized Automated Radiotherapy (SOAR) planning algorithm. MATERIALS AND METHODS:Twenty-five patient plans, previously treated with SRS for multi-metastatic intracranial tumors, were selected for a retrospective plan study comparing the manual planning strategy to SOAR. The SOAR algorithm was used to select isocenters, table, collimator, and gantry angles, and target groupings for the optimized plans. Dose-volume metrics for relevant OARs and PTVs were compared using double-sided Wilcoxon signed rank tests (α = 0.05). A subset of five patients were included in an efficiency study comparing manual planning times to SOAR automated times. RESULTS:OAR dose metrics compared between planning strategies showed no statistical difference for the dataset of twenty-five plans. Differences in maximum PTV dose and the conformity index were improved for SOAR planning and statistically significant. The median SOAR planning time was 9.8 min compared to 55 min for the manual planning strategy. CONCLUSIONS:SOAR planning was comparable in plan quality to a manual planning strategy with the possibility for greatly improving planning efficiency through automation.

AbstractThis study used the visualization of hypo‐intense regions on liver‐specific MRI to directly quantify stereotactic body radiation therapy (SBRT) spatial delivery accuracy. Additionally, the interfractional motion of the liver region was determined and compared with the MRI‐based evaluation of liver SBRT spatial treatment delivery accuracy. Primovist®‐enhanced MRI scans were acquired from 17 patients, 8–12 weeks following the completion of liver SBRT treatment. Direct visualization of radiation‐induced focal liver reaction in the form of hypo‐intensity was determined. The auto‐delineation approach was used to localize these regions, and center‐of‐mass (COM) discrepancy was quantified between the MRI hypo‐intensity and the CT‐based treatment plan. To assess the interfractional motion of the liver region, a planning CT was registered to a Cone Beam CT obtained before each treatment fraction. The interfractional motion assessed from this approach was then compared against the localized hypo‐intense MRI regions. The mean ± SD COM discrepancy was 1.4 ± 1.3 mm in the left‐right direction, 2.6 ± 1.8 mm in an anteroposterior direction, and 1.9 ± 2.6 mm in the craniocaudal direction. A high correlation was observed between interfractional motion of visualized hypo‐intensity and interfractional motion of planning treatment volume (PTV); the quantified Pearson correlation coefficient was 0.96. The lack of correlation was observed between Primovist® MRI‐based spatial accuracy and interfractional motion of the liver, where Pearson correlation coefficients ranged from −0.01 to −0.26. The highest random and systematic errors quantified from interfractional motion were in the craniocaudal direction. This work demonstrates a novel framework for the direct evaluation of liver SBRT spatial delivery accuracy.

Development of a novel auto-delineation methodology for observed hypointensity from focal liver reaction in hepatobiliary-specific contrast (Primovist) enhanced MRI acquired post Stereotactic Body Radiation Therapy (SBRT). Additionally, the methodology for the quantification of the threshold dose associated with the observed focal liver reaction was also established. An auto-delineation algorithm was created based on the correlation between intensity and radiation dose information. The error associated with the auto-delineation was quantified using virtual FLRs, as well as clinical patient scans. Patients underwent liver SBRT with a total dose prescription of 50 Gy in 5 fractions. An inherent correlation was established between the contrast-to-noise ratio (CNR) on MRI scans and expected performance of the algorithm using centre-of-mass (COM). Threshold dose associated with focal liver reaction was quantified for all ten patients and verified with associated most conformal isodose line. Based on the CNR vs COM error relationship, the expected median (range) auto-delineation COM error for ten patients was 0.5 (0 to 3.2) mm. The median threshold dose for ten clinical cases was 21.3 Gy based on the auto-delineation framework. This threshold dose was compared to the most conformal isodose line with the hypointensity; there was no significant difference observed (p = 0.6). We developed a framework for post-SBRT Primovist observed focal liver reaction localization. Furthermore, this study established an automated approach for the determination of the threshold dose associated with the hypointense region.

Purpose. Metastatic complications are responsible for 90% of cancer-associated mortality. Magnetic resonance imaging (MRI) can be used to observe the brain's microstructure and potentially correlate changes with metastasis occurrence. Diffusion weighted imaging (DWI) is an MRI technique that utilizes the kinetics of water molecules within the body. The aim of this study is to use DWI to characterize diffusion changes within brain metastases in cancer patients pre- and post-stereotactic radiosurgery (SRS). Methods. We retrospectively analyzed 113 metastases from 13 patients who underwent SRS for brain metastasis recurrence. Longitudinal apparent diffusion coefficient (ADC) maps were registered to Gd-T1 images and CT, and clinical metastasis ROIs from all SRS treatments were retrospectively transferred onto these ADC maps for analysis. Metastases were characterized based on pre-SRS diffusion pattern, primary cancer site, and post-SRS outcome. ADC values were calculated pre- and post-SRS. Results.ADC values were significantly elevated (980.2 x 10(-6) mm(2) s(-1) and 1040.3 x 10(-6) mm(2) s(-1) pre- and post-SRS, respectively) when compared to healthy brain tissue (826.8 x 10(-6) mm(2) s(-1)) for all metastases. Three identified pre-SRS patterns were significantly different before SRS and within 6 months post-SRS. No significant differences were observed between different primaries pre-SRS. Post-SRS, Lung metastases ADC decreased by 86.2 x 10(-6) mm(2) s(-1), breast metastases increased by 116.7 x 10(-6) mm(2) s(-1), and genitourinary metastases showed no significant ADC change. SRS outcomes showed ADC variability pre-treatment but no significant differences pre- and post-SRS, except at 6-9 months post-SRS where progressing metastases were elevated when compared to other response groups. Conclusion. This study provided a unique opportunity to characterize diffusion changes in brain metastases before their manifestation on standard Gd-T1 images and post-SRS. Identified patterns may improve early detection of brain metastases as well as predict their response to treatment.

Cardiac tachyarrhythmias are a major cause of morbidity and mortality. Treatments for these tachyarrhythmias include antiarrhythmic drugs, catheter ablation, surgical ablation, cardiac implantable electronic devices, and cardiac transplantation. Each of these treatment approaches is effective in some patients but there is considerable room for improvement, particularly with respect to the most common of the tachydysrhythmias, atrial fibrillation, and the most dangerous of the tachydysrhythmias, ventricular tachycardia (VT) or ventricular fibrillation. Noninvasive stereotactic ablative radiation therapy is emerging as an effective treatment for refractory tachyarrhythmias. Animal models have shown successful ablation of arrhythmogenic myocardial substrates with minimal short-term complications. Studies of stereotactic radioablation involving patients with refractory VT have shown a reduction in VT recurrence and promising early safety data. In this review, we provide the background for the application of stereotactic arrhythmia radioablation therapy along with promising results from early applications of the technology.