High-resolution 3T to 7T ADC map synthesis with a hybrid CNN-transformer model.

BACKGROUND: 7 Tesla (7T) apparent diffusion coefficient (ADC) maps derived from diffusion-weighted imaging (DWI) demonstrate improved image quality and spatial resolution over 3 Tesla (3T) ADC maps. However, 7T magnetic resonance imaging (MRI) currently suffers from limited clinical unavailability, higher cost, and increased susceptibility to artifacts. PURPOSE: To address these issues, we propose a hybrid CNN-transformer model to synthesize high-resolution 7T ADC maps from multimodal 3T MRI. METHODS: The Vision CNN-Transformer (VCT), composed of both Vision Transformer (ViT) blocks and convolutional layers, is proposed to produce high-resolution synthetic 7T ADC maps from 3T ADC maps and 3T T1-weighted (T1w) MRI. ViT blocks enabled global image context while convolutional layers efficiently captured fine detail. The VCT model was validated on the publicly available Human Connectome Project Young Adult dataset, comprising 3T T1w, 3T DWI, and 7T DWI brain scans. The Diffusion Imaging in Python library was used to compute ADC maps from the DWI scans. A total of 171 patient cases were randomly divided into 130 training cases, 20 validation cases, and 21 test cases. The synthetic ADC maps were evaluated by comparing their similarity to the ground truth volumes with the following metrics: peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and mean squared error (MSE). In addition, RESULTS: The results are as follows: PSNR: 27.0 ± 0.9 dB, SSIM: 0.945 ± 0.010, and MSE: 2.0E-3 ± 0.4E-3. Both qualitative and quantitative results demonstrate that VCT performs favorably against other state-of-the-art methods. We have introduced various efficiency improvements, including the implementation of flash attention and training on 176×208 resolution images. These enhancements have resulted in the reduction of parameters and training time per epoch by 50% in comparison to ResViT. Specifically, the training time per epoch has been shortened from 7.67 min to 3.86 min. CONCLUSION: We propose a novel method to predict high-resolution 7T ADC maps from low-resolution 3T ADC maps and T1w MRI. Our predicted images demonstrate better spatial resolution and contrast compared to 3T MRI and prediction results made by ResViT and pix2pix. These high-quality synthetic 7T MR images could be beneficial for disease diagnosis and intervention, producing higher resolution and conformal contours, and as an intermediate step in generating synthetic CT for radiation therapy, especially when 7T MRI scanners are unavailable.

The Utility of Spectroscopic MRI in Stereotactic Biopsy and Radiotherapy Guidance in Newly Diagnosed Glioblastoma.

Current diagnostic and therapeutic approaches for gliomas have limitations hindering survival outcomes. We propose spectroscopic magnetic resonance imaging as an adjunct to standard MRI to bridge these gaps. Spectroscopic MRI is a volumetric MRI technique capable of identifying tumor infiltration based on its elevated choline (Cho) and decreased N-acetylaspartate (NAA). We present the clinical translatability of spectroscopic imaging with a Cho/NAA ≥ 5x threshold for delineating a biopsy target in a patient diagnosed with non-enhancing glioma. Then, we describe the relationship between the undertreated tumor detected with metabolite imaging and overall survival (OS) from a pilot study of newly diagnosed GBM patients treated with belinostat and chemoradiation. Each cohort (control and belinostat) were split into subgroups using the median difference between pre-radiotherapy Cho/NAA ≥ 2x and the treated T1-weighted contrast-enhanced (T1w-CE) volume. We used the Kaplan-Meier estimator to calculate median OS for each subgroup. The median OS was 14.4 months when the difference between Cho/NAA ≥ 2x and T1w-CE volumes was higher than the median compared with 34.3 months when this difference was lower than the median. The T1w-CE volumes were similar in both subgroups. We find that patients who had lower volumes of undertreated tumors detected via spectroscopy had better survival outcomes.

MGMT promoter methylation prediction based on multiparametric MRI via vision graph neural network.

Purpose: Glioblastoma (GBM) is aggressive and malignant. The methylation status of the O6-methylguanine-DNA methyltransferase (MGMT) promoter in GBM tissue is considered an important biomarker for developing the most effective treatment plan. Although the standard method for assessing the MGMT promoter methylation status is via bisulfite modification and deoxyribonucleic acid (DNA) sequencing of biopsy or surgical specimens, a secondary automated method based on medical imaging may improve the efficiency and accuracy of those tests. Approach: We propose a deep vision graph neural network (ViG) using multiparametric magnetic resonance imaging (MRI) to predict the MGMT promoter methylation status noninvasively. Our model was compared to the RSNA radiogenomic classification winners. The dataset includes 583 usable patient cases. Combinations of MRI sequences were compared. Our multi-sequence fusion strategy was compared with those using single MR sequences. Results: Our best model [Fluid Attenuated Inversion Recovery (FLAIR), T1-weighted pre-contrast (T1w), T2-weighted (T2)] outperformed the winning models with a test area under the curve (AUC) of 0.628, an accuracy of 0.632, a precision of 0.646, a recall of 0.677, a specificity of 0.581, and an F1 score of 0.661. Compared to the winning models with single MR sequences, our ViG utilizing fused-MRI showed a significant improvement statistically in AUC scores, which are FLAIR (p=0.042), T1w (p=0.017), T1wCE (p=0.001), and T2 (p=0.018). Conclusions: Our model is superior to challenge champions. A graph representation of the medical images enabled good handling of complexity and irregularity. Our work provides an automatic secondary check pipeline to ensure the correctness of MGMT methylation status prediction.

High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling.

Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion diagnosis, prognosis, and delineation. However, gradient power and hardware limitations prohibit recording thin slices or sub-1 mm resolution. Furthermore, long scan time is not clinically acceptable. Conventional high-resolution images generated using statistical or analytical methods include the limitation of capturing complex, high-dimensional image data with intricate patterns and structures. This study aims to harness cutting-edge diffusion probabilistic deep learning techniques to create a framework for generating high-resolution MRI from low-resolution counterparts, improving the uncertainty of denoising diffusion probabilistic models (DDPM).Approach. DDPM includes two processes. The forward process employs a Markov chain to systematically introduce Gaussian noise to low-resolution MRI images. In the reverse process, a U-Net model is trained to denoise the forward process images and produce high-resolution images conditioned on the features of their low-resolution counterparts. The proposed framework was demonstrated using T2-weighted MRI images from institutional prostate patients and brain patients collected in the Brain Tumor Segmentation Challenge 2020 (BraTS2020).Main results. For the prostate dataset, the bicubic interpolation model (Bicubic), conditional generative-adversarial network (CGAN), and our proposed DDPM framework improved the noise quality measure from low-resolution images by 4.4%, 5.7%, and 12.8%, respectively. Our method enhanced the signal-to-noise ratios by 11.7%, surpassing Bicubic (9.8%) and CGAN (8.1%). In the BraTS2020 dataset, the proposed framework and Bicubic enhanced peak signal-to-noise ratio from resolution-degraded images by 9.1% and 5.8%. The multi-scale structural similarity indexes were 0.970 ± 0.019, 0.968 ± 0.022, and 0.967 ± 0.023 for the proposed method, CGAN, and Bicubic, respectively.Significance. This study explores a deep learning-based diffusion probabilistic framework for improving MR image resolution. Such a framework can be used to improve clinical workflow by obtaining high-resolution images without penalty of the long scan time. Future investigation will likely focus on prospectively testing the efficacy of this framework with different clinical indications.

A multi-site phase I trial of Veliparib with standard radiation and temozolomide in patients with newly diagnosed glioblastoma multiforme (GBM).

PURPOSE: A multi-site Phase I trial was conducted to determine the safety, maximum tolerated dose, and pharmacokinetics (PK) of Veliparib, a Poly (ADP-ribose) polymerase [PARP] enzyme inhibitor, when administered with temozolomide (TMZ) alone and then with temozolomide and radiation (RT) in patients with newly diagnosed glioblastoma. METHODS: Given the potential for myelosuppression when a PARP inhibitor is combined with chemotherapy, the first 6 patients accrued were given Veliparib 10 mg bid and TMZ 75 mg/m2/d daily for six weeks. If this was well tolerated, the same doses of Veliparib and TMZ would be tested along with standard radiation with plans to dose escalate the Veliparib in subsequent patient cohorts. Once a maximal tolerated dose was determined, a 78 patient phase II study was planned. Peripheral blood pharmacokinetics were assessed. RESULTS: Twenty-four patients were enrolled. In the first 6 patients who received 6 weeks of TMZ with Veliparib only one dose limiting toxicity (DLT) occurred. The next 12 patients received 6 weeks of RT + TMZ + veliparib and 4/12 (33%) had dose limiting hematologic toxicities. As a result, Veliparib was reduced by 50% to 10 mg BID every other week, but again 3/3 patients had dose limiting hematologic toxicities. The trial was then terminated. The mean clearance (± SD) CL/F of Veliparib for the initial dose (27.0 ± 9.0 L/h, n = 16) and at steady-state for 10 mg BID (23.5 ± 10.4 L/h, n = 18) were similar. Accumulation for BID dosing was 56% (± 33%). CONCLUSIONS: Although Veliparib 10 mg BID administered with TMZ 75 mg/m2 for six weeks was well tolerated, when this regimen was combined with standard partial brain irradiation it was severely myelosuppressive even when the dose was reduced by 50%. This study again highlights the potential of localized cranial radiotherapy to significantly increase hematologic toxicity of marginally myelosuppressive systemic therapies.

Hippocampus substructure segmentation using morphological vision transformer learning.

The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MR images, we developed a novel model, Hippo-Net, which uses a cascaded model strategy. The proposed model consists of two major parts: (1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. (2) An end-to-end morphological vision transformer network (Franchietal2020Pattern Recognit.102107246, Ranemetal2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW) pp 3710-3719) is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MR images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures. A total of 260 T1w MRI datasets from medical segmentation decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. In five-fold cross-validation, the Dice similarity coefficients were 0.900 ± 0.029 and 0.886 ± 0.031 for the hippocampus proper and parts of the subiculum, respectively. The mean surface distances (MSDs) were 0.426 ± 0.115 mm and 0.401 ± 0.100 mm for the hippocampus proper and parts of the subiculum, respectively. The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MR images. It may facilitate the current clinical workflow and reduce the physicians' effort.

A Multi-Site Phase I Trial of Veliparib with Standard Radiation and Temozolomide in Patients with Newly Diagnosed Glioblastoma Multiforme (GBM).

Purpose: A multi-site Phase I trial was conducted to determine the safety, maximum tolerated dose, and pharmacokinetics (PK) of Veliparib, a Poly (ADP-ribose) polymerase [PARP] enzyme inhibitor, when administered with temozolomide (TMZ) alone and then with temozolomide and radiation (RT) in patients with newly diagnosed glioblastoma. Methods: Given the potential for myelosuppression when a PARP inhibitor is combined with chemotherapy, the first 6 patients accrued were given Veliparib 10 mg bid and TMZ 75 mg/m2/d daily for six weeks. If this was well tolerated, the same doses of Veliparib and TMZ would be tested along with standard radiation with plans to dose escalate the Veliparib in subsequent patient cohorts. Once a maximal tolerated dose was determined, a 78 patient phase II study was planned. Peripheral blood pharmacokinetics were assessed. Results: Twenty-four patients were enrolled. In the first 6 patients who received 6 weeks of TMZ with Veliparib only one dose limiting toxicity (DLT) occurred. The next 12 patients received 6 weeks of RT + TMZ + veliparib and 4/12 (33%) had dose limiting hematologic toxicities. As a result, Veliparib was reduced by 50% to 10 mg BID every other week, but again 3/3 patients had dose limiting hematologic toxicities. The trial was then terminated. The mean clearance (± SD) CL/F of Veliparib for the initial dose (27.0 ± 9.0 L/h, n = 16) and at steady-state for 10 mg BID (23.5 ± 10.4 L/h, n = 18) were similar. Accumulation for BID dosing was 56% (± 33%). Conclusions: Although Veliparib 10 mg BID administered with TMZ 75 mg/m2 for six weeks was well tolerated, when this regimen was combined with standard partial brain irradiation it was severely myelosuppressive even when the dose was reduced by 50%. This study again highlights the potential of localized cranial radiotherapy to significantly increase hematologic toxicity of marginally myelosuppressive systemic therapies.

Examining the Effect of ALK and EGFR Mutations on Survival Outcomes in Surgical Lung Brain Metastasis Patients.

In the context of the post-genomic era, where targeted oncological therapies like monoclonal antibodies (mAbs) and tyrosine-kinase inhibitors (TKIs) are gaining prominence, this study investigates whether these therapies can enhance survival for lung carcinoma patients with specific genetic mutations-EGFR-amplified and ALK-rearranged mutations. Prior to this study, no research series had explored how these mutations influence patient survival in cases of surgical lung brain metastases (BMs). Through a multi-site retrospective analysis, the study examined patients who underwent surgical resection for BM arising from primary lung cancer at Emory University Hospital from January 2012 to May 2022. The mutational statuses were determined from brain tissue biopsies, and survival analyses were conducted. Results from 95 patients (average age: 65.8 ± 10.6) showed that while 6.3% had anaplastic lymphoma kinase (ALK)-rearranged mutations and 20.0% had epidermal growth factor receptor (EGFR)-amplified mutations-with 9.5% receiving second-line therapies-these mutations did not significantly correlate with overall survival. Although the sample size of patients receiving targeted therapies was limited, the study highlighted improved overall survival and progression-free survival rates compared to earlier trials, suggesting advancements in systemic lung metastasis treatment. The study suggests that as more targeted therapies emerge, the prospects for increased overall survival and progression-free survival in lung brain metastasis patients will likely improve.

A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking.

Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.

Hippocampus Substructure Segmentation Using Morphological Vision Transformer Learning.

Background: The hippocampus plays a crucial role in memory and cognition. Because of the associated toxicity from whole brain radiotherapy, more advanced treatment planning techniques prioritize hippocampal avoidance, which depends on an accurate segmentation of the small and complexly shaped hippocampus. Purpose: To achieve accurate segmentation of the anterior and posterior regions of the hippocampus from T1 weighted (T1w) MRI images, we developed a novel model, Hippo-Net, which uses a mutually enhanced strategy. Methods: The proposed model consists of two major parts: 1) a localization model is used to detect the volume-of-interest (VOI) of hippocampus. 2) An end-to-end morphological vision transformer network is used to perform substructures segmentation within the hippocampus VOI. The substructures include the anterior and posterior regions of the hippocampus, which are defined as the hippocampus proper and parts of the subiculum. The vision transformer incorporates the dominant features extracted from MRI images, which are further improved by learning-based morphological operators. The integration of these morphological operators into the vision transformer increases the accuracy and ability to separate hippocampus structure into its two distinct substructures.A total of 260 T1w MRI datasets from Medical Segmentation Decathlon dataset were used in this study. We conducted a five-fold cross-validation on the first 200 T1w MR images and then performed a hold-out test on the remaining 60 T1w MR images with the model trained on the first 200 images. The segmentations were evaluated with two indicators, 1) multiple metrics including the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95), mean surface distance (MSD), volume difference (VD) and center-of-mass distance (COMD); 2) Volumetric Pearson correlation analysis. Results: In five-fold cross-validation, the DSCs were 0.900±0.029 and 0.886±0.031 for the hippocampus proper and parts of the subiculum, respectively. The MSD were 0.426±0.115mm and 0.401±0.100 mm for the hippocampus proper and parts of the subiculum, respectively. Conclusions: The proposed method showed great promise in automatically delineating hippocampus substructures on T1w MRI images. It may facilitate the current clinical workflow and reduce the physicians' effort.

Spectroscopic MRI-Based Biomarkers Predict Survival for Newly Diagnosed Glioblastoma in a Clinical Trial.

Despite aggressive treatment, glioblastoma has a poor prognosis due to its infiltrative nature. Spectroscopic MRI-measured brain metabolites, particularly the choline to N-acetylaspartate ratio (Cho/NAA), better characterizes the extent of tumor infiltration. In a previous pilot trial (NCT03137888), brain regions with Cho/NAA ≥ 2x normal were treated with high-dose radiation for newly diagnosed glioblastoma patients. This report is a secondary analysis of that trial where spectroscopic MRI-based biomarkers are evaluated for how they correlate with progression-free and overall survival (PFS/OS). Subgroups were created within the cohort based on pre-radiation treatment (pre-RT) median cutoff volumes of residual enhancement (2.1 cc) and metabolically abnormal volumes used for treatment (19.2 cc). We generated Kaplan-Meier PFS/OS curves and compared these curves via the log-rank test between subgroups. For the subgroups stratified by metabolic abnormality, statistically significant differences were observed for PFS (p = 0.019) and OS (p = 0.020). Stratification by residual enhancement did not lead to observable differences in the OS (p = 0.373) or PFS (p = 0.286) curves. This retrospective analysis shows that patients with lower post-surgical Cho/NAA volumes had significantly superior survival outcomes, while residual enhancement, which guides high-dose radiation in standard treatment, had little significance in PFS/OS. This suggests that the infiltrating, non-enhancing component of glioblastoma is an important factor in patient outcomes and should be treated accordingly.

Mutant Isocitrate Dehydrogenase 1 Expression Enhances Response of Gliomas to the Histone Deacetylase Inhibitor Belinostat.

Histone deacetylase inhibitors (HDACis) are drugs that target the epigenetic state of cells by modifying the compaction of chromatin through effects on histone acetylation. Gliomas often harbor a mutation of isocitrate dehydrogenase (IDH) 1 or 2 that leads to changes in their epigenetic state presenting a hypermethylator phenotype. We postulated that glioma cells with IDH mutation, due to the presence of epigenetic changes, will show increased sensitivity to HDACis. This hypothesis was tested by expressing mutant IDH1 with a point alteration-converting arginine 132 to histidine-within glioma cell lines that contain wild-type IDH1. Glioma cells engineered to express mutant IDH1 produced D-2-hydroxyglutarate as expected. When assessed for response to the pan-HDACi drug belinostat, mutant IDH1-expressing glioma cells were subjected to more potent inhibition of growth than the corresponding control cells. Increased sensitivity to belinostat correlated with the increased induction of apoptosis. Finally, a phase I trial assessing the addition of belinostat to standard-of-care therapy for newly diagnosed glioblastoma patients included one patient with a mutant IDH1 tumor. This mutant IDH1 tumor appeared to display greater sensitivity to the addition of belinostat than the other cases with wild-type IDH tumors based on both standard magnetic resonance imaging (MRI) and advanced spectroscopic MRI criteria. These data together suggest that IDH mutation status within gliomas may serve as a biomarker of response to HDACis.

Applying a Radiation Therapy Volume Analysis Pipeline to Determine the Utility of Spectroscopic MRI-Guided Adaptive Radiation Therapy for Glioblastoma.

Accurate radiation therapy (RT) targeting is crucial for glioblastoma treatment but may be challenging using clinical imaging alone due to the infiltrative nature of glioblastomas. Precise targeting by whole-brain spectroscopic MRI, which maps tumor metabolites including choline (Cho) and N-acetylaspartate (NAA), can quantify early treatment-induced molecular changes that other traditional modalities cannot measure. We developed a pipeline to determine how spectroscopic MRI changes during early RT are associated with patient outcomes to provide insight into the utility of adaptive RT planning. Data were obtained from a study (NCT03137888) where glioblastoma patients received high-dose RT guided by the pre-RT Cho/NAA twice normal (Cho/NAA ≥ 2x) volume, and received spectroscopic MRI scans pre- and mid-RT. Overlap statistics between pre- and mid-RT scans were used to quantify metabolic activity changes after two weeks of RT. Log-rank tests were used to quantify the relationship between imaging metrics and patient overall and progression-free survival (OS/PFS). Patients with lower Jaccard/Dice coefficients had longer PFS (p = 0.045 for both), and patients with lower Jaccard/Dice coefficients had higher OS trending towards significance (p = 0.060 for both). Cho/NAA ≥ 2x volumes changed significantly during early RT, putting healthy tissue at risk of irradiation, and warranting further study into using adaptive RT planning.

Using Brain Tumor MRI Structured Reporting to Quantify the Impact of Imaging on Brain Tumor Boards.

Multidisciplinary tumor boards (TB) are an essential part of brain tumor care, but quantifying the impact of imaging on patient management is challenging due to treatment complexity and a lack of quantitative outcome measures. This work uses a structured reporting system for classifying brain tumor MRIs, the brain tumor reporting and data system (BT-RADS), in a TB setting to prospectively assess the impact of imaging review on patient management. Published criteria were used to prospectively assign three separate BT-RADS scores (an initial radiology report, secondary TB presenter review, and TB consensus) to brain MRIs reviewed at an adult brain TB. Clinical recommendations at TB were noted and management changes within 90 days after TB were determined by chart review. In total, 212 MRIs in 130 patients (median age = 57 years) were reviewed. Agreement was 82.2% between report and presenter, 79.0% between report and consensus, and 90.1% between presenter and consensus. Rates of management change increased with increasing BT-RADS scores (0-3.1%, 1a-0%, 1b-66.7%, 2-8.3%, 3a-38.5%, 3b-55.9, 3c-92.0%, and 4-95.6%). Of 184 (86.8%) cases with clinical follow-up within 90 days after the tumor board, 155 (84.2%) of the recommendations were implemented. Structured scoring of MRIs provides a quantitative way to assess rates of agreement interpretation alongside how often management changes are recommended and implemented in a TB setting.

A Novel Approach to Determining Tumor Progression Using a Three-Site Pilot Clinical Trial of Spectroscopic MRI-Guided Radiation Dose Escalation in Glioblastoma.

Glioblastoma (GBM) is a fatal disease, with poor prognosis exacerbated by difficulty in assessing tumor extent with imaging. Spectroscopic MRI (sMRI) is a non-contrast imaging technique measuring endogenous metabolite levels of the brain that can serve as biomarkers for tumor extension. We completed a three-site study to assess survival benefits of GBM patients when treated with escalated radiation dose guided by metabolic abnormalities in sMRI. Escalated radiation led to complex post-treatment imaging, requiring unique approaches to discern tumor progression from radiation-related treatment effect through our quantitative imaging platform. The purpose of this study is to determine true tumor recurrence timepoints for patients in our dose-escalation multisite study using novel methodology and to report on median progression-free survival (PFS). Follow-up imaging for all 30 trial patients were collected, lesion volumes segmented and graphed, and imaging uploaded to our platform for visual interpretation. Eighteen months post-enrollment, the median PFS was 16.6 months with a median time to follow-up of 20.3 months. With this new treatment paradigm, incidence rate of tumor recurrence one year from treatment is 30% compared to 60-70% failure under standard care. Based on the delayed tumor progression and improved survival, a randomized phase II trial is under development (EAF211).

Low-risk meningioma: Initial outcomes from NRG Oncology/RTOG 0539.

BACKGROUND: Three- and five-year progression-free survival (PFS) for low-risk meningioma managed with surgery and observation reportedly exceeds 90%. Herewith we summarize outcomes for low-risk meningioma patients enrolled on NRG/RTOG 0539. METHODS: This phase II trial allocated patients to one of three groups per World Health Organization grade, recurrence status, and resection extent. Low-risk patients had either gross total (GTR) or subtotal resection (STR) for a newly diagnosed grade 1 meningioma and were observed after surgery. The primary endpoint was 3-year PFS. Adverse events (AEs) were scored using Common Terminology Criteria for Adverse Events (CTCAE) version 3. RESULTS: Among 60 evaluable patients, the median follow-up was 9.1 years. The 3-, 5-, and 10-year rates were 91.4% (95% CI, 84.2 to 98.6), 89.4% (95% CI, 81.3 to 97.5), 85.0% (95% CI, 75.3 to 94.7) for PFS and 98.3% (95% CI, 94.9 to 100), 98.3%, (95% CI, 94.9 to 100), 93.8% (95% CI, 87.0 to 100) for overall survival (OS), respectively. With centrally confirmed GTR, 3/5/10y PFS and OS rates were 94.3/94.3/87.6% and 97.1/97.1/90.4%. With STR, 3/5/10y PFS rates were 83.1/72.7/72.7% and 10y OS 100%. Five patients reported one grade 3, four grade 2, and five grade 1 AEs. There were no grade 4 or 5 AEs. CONCLUSIONS: These results prospectively validate high PFS and OS for low-risk meningioma managed surgically but raise questions regarding optimal management following STR, a subcohort that could potentially benefit from adjuvant therapy.

Development and validation of a prognostic gene expression signature for lower-grade glioma following surgery and adjuvant radiotherapy.

BACKGROUND AND PURPOSE: Standard of care for lower-grade glioma (LGG) is maximal safe resection and risk-adaptive adjuvant therapy. While patients who benefit the most from adjuvant chemotherapy have been elucidated in prospective randomized studies, comparable insights for adjuvant radiotherapy (RT) are lacking. We sought to identify and validate patterns of gene expression that are associated with differential outcomes among LGG patients treated by RT from two large genomics databases. MATERIALS AND METHODS: Patients from The Cancer Genome Atlas (TCGA) with LGG (WHO grade II-III glioma) treated by surgery and adjuvant RT were randomized 1:1 to a discovery cohort or an internal validation cohort. Using the discovery cohort only, associations between tumor RNA-seq expression and progression-free survival (PFS) as well as overall survival (OS) were evaluated with adjustment for clinicopathologic covariates. A Genomic Risk Score (GRS) was then constructed from the expression levels of top genes also screened for involvement in glioma carcinogenesis. The prognostic value of GRS was further assessed in the internal validation cohort of TCGA and a second distinct database, compiled by the Chinese Glioma Genome Association (CGGA). RESULTS: From TCGA, 289 patients with LGG received adjuvant RT alone (38 grade II, 30 grade III) or chemoradiotherapy (CRT) (51 grade II, 170 grade III) between 2009 and 2015. From CGGA, 178 patients with LGG received adjuvant RT alone (40 grade II, 13 grade III) or CRT (41 grade II, 84 grade III) between 2004 and 2016. The genes comprising GRS are involved in MAP kinase activity, T cell chemotaxis, and cell cycle transition: MAP3K15, MAPK10, CCL3, CCL4, and ADAMTS1. High GRS, defined as having a GRS in the top third, was significantly associated with poorer outcomes independent of age, sex, glioma histology, WHO grade, IDH mutation, 1p/19q co-deletion, and chemotherapy status in the discovery cohort (PFS HR 1.61, 95% CI 1.10-2.36, P = 0.014; OS HR 2.74, 95% CI 1.68-4.47, P < 0.001). These findings were replicated in the internal validation cohort (PFS HR 1.58, 95% CI 1.05-2.37, P = 0.027; OS HR 1.84, 95% CI 1.13-3.00, P = 0.015) and the CGGA external validation cohort (OS HR 1.72, 95% CI 1.27-2.34, P < 0.001). Association between GRS and outcomes was observed only among patients who underwent RT, in both TCGA and CGGA. CONCLUSION: This study successfully identified an expression signature of five genes that stratified outcomes among LGG patients who received adjuvant RT, with two rounds of validation leveraging independent genomics databases. Expression levels of the highlighted genes were associated with PFS and OS only among patients whose treatment included RT, but not among those with omission of RT, suggesting that expression of these genes may be predictive of radiation treatment response. While additional prospective studies are warranted, interrogation of these genes may be considered in the multidisciplinary management of LGG.

Osteoradionecrosis of the craniotomy flap: a rare complication of stereotactic radiosurgery.

Osteoradionecrosis (ORN), ischemic necrosis of irradiated bone without evidence of persisting or recurrent tumor, is a known complication of radiation therapy. ORN of the skull has not been reported following stereotactic radiosurgery (SRS). We report two cases of ORN of the skull following SRS for recurrent meningiomas post-resection. Both patients developed ORN in their craniotomy flaps in areas that received high doses of radiation due to their proximity to the recurrent tumors. In each case, the ORN was asymptomatic and was detected on surveillance magnetic resonance imaging. Both patients were followed closely with imaging that ultimately revealed either stability or improvement in the ORN, confirming the diagnosis without the need for biopsy. The cases reveal a role for close imaging surveillance instead of immediate biopsy in patients with new enhancement involving bone in high-dose radiation treatment regions.

A multi-institutional pilot clinical trial of spectroscopic MRI-guided radiation dose escalation for newly diagnosed glioblastoma.

Background: Glioblastomas (GBMs) are aggressive brain tumors despite radiation therapy (RT) to 60 Gy and temozolomide (TMZ). Spectroscopic magnetic resonance imaging (sMRI), which measures levels of specific brain metabolites, can delineate regions at high risk for GBM recurrence not visualized on contrast-enhanced (CE) MRI. We conducted a clinical trial to assess the feasibility, safety, and efficacy of sMRI-guided RT dose escalation to 75 Gy for newly diagnosed GBMs. Methods: Our pilot trial (NCT03137888) enrolled patients at 3 institutions (Emory University, University of Miami, Johns Hopkins University) from September 2017 to June 2019. For RT, standard tumor volumes based on T2-FLAIR and T1w-CE MRIs with margins were treated in 30 fractions to 50.1 and 60 Gy, respectively. An additional high-risk volume based on residual CE tumor and Cho/NAA (on sMRI) ≥2× normal was treated to 75 Gy. Survival curves were generated by the Kaplan-Meier method. Toxicities were assessed according to CTCAE v4.0. Results: Thirty patients were treated in the study. The median age was 59 years. 30% were MGMT promoter hypermethylated; 7% harbored IDH1 mutation. With a median follow-up of 21.4 months for censored patients, median overall survival (OS) and progression-free survival were 23.0 and 16.6 months, respectively. This regimen appeared well-tolerated with 70% of grade 3 or greater toxicity ascribed to TMZ and 23% occurring at least 1 year after RT. Conclusion: Dose-escalated RT to 75 Gy guided by sMRI appears feasible and safe for patients with newly diagnosed GBMs. OS outcome is promising and warrants additional testing. Based on these results, a randomized phase II trial is in development.