Dynamics of Daily Glioblastoma Evolution During Chemoradiation Therapy on the 0.35T Magnetic Resonance Imaging-Linear Accelerator.

PURPOSE: Glioblastoma changes during chemoradiation therapy are inferred from magnetic resonance imaging (MRI) before and after treatment but are rarely investigated due to logistics of frequent MRI. Using a combination MRI-linear accelerator (MRI-linac), we evaluated changes during daily chemoradiation therapy. METHODS AND MATERIALS: Patients with glioblastoma were prospectively imaged daily during chemoradiation therapy on 0.35T MRI-linac and at 3 timepoints with and without contrast on standalone high-field MRI. Tumor or edema (lesion) and resection cavity dynamics throughout treatment were analyzed and compared with standalone T1 postcontrast (T1+C) and T2 volumes. RESULTS: Of 36 patients included in this analysis, 8 had cavity only, 12 had lesion only, and 16 had both cavity and lesion. Of these, 64% had lesion growth and 46% had cavity shrinkage during treatment on MRI-linac scans. The average MRI-linac migration distance was 1.3 cm (range, 0-4.1 cm) for lesion and 0.6 cm (range, 0.1-2.1 cm) for cavity. Standalone versus MRI-linac volumes correlated strongly with R2 values: 0.991 (T2 vs MRI-linac cavity), 0.972 (T1+C vs MRI-linac cavity), and 0.973 (T2 vs MRI-linac lesion). There was a moderate correlation between T1+C and MRI-linac lesion (R2 = 0.609), despite noncontrast MRI-linac inability to separate contrast enhancement from surrounding nonenhancing tumor and edema. From pretreatment to posttreatment in patients with all available scans (n = 35), T1+C and MRI-linac lesions changed together-shrank (n = 6), grew (n = 12), or unchanged (n = 8)-in 26 (74%) patients. Another 9 patients (26%) had growth on MRI-linac, although the T1+C component shrank. In no patient did T1+C lesion grow while MRI-linac lesion shrank. CONCLUSIONS: Anatomic changes are seen in patients with glioblastoma imaged daily on MRI-linac throughout the chemoradiation therapy course. As surgical resection cavities shrink, margins may be reduced to save normal brain. Patients with unresected or growing lesions may require margin expansions to cover changes. Limited volume glioblastoma boost trials could consider triggered gadolinium contrast administration for evaluation of adaptive radiation therapy when lesion growth is seen on noncontrast MRI-linac.

Patient and physician attitudes towards salvage stereotactic radiosurgery or radiotherapy for brain metastases.

Background: The experience of patients with brain metastases treated with stereotactic radiosurgery (SRS) may shape attitudes towards salvage therapy. Furthermore, physician attitudes towards salvage therapy may differ based on specialty and experience. Our objective is to compare physician attitudes and patient experiences with SRS. Methods: Eligible patients with brain metastases treated with one course of SRS or fractionated stereotactic radiotherapy (FSRT) without whole brain radiotherapy (WBRT) in the definitive or postoperative setting at a single institution were surveyed from 11/2021 to 11/2022 regarding their perspectives on salvage therapy. A separate 11-question multi-disciplinary physician survey was distributed to residents, fellows and attendings at seven additional academic institutions in the US. Chi-square test and Mann-Whitney U test were used to assess differences. Results: A total of 30 patients and 88 physicians were surveyed. Most patients reported being satisfied or very satisfied with initial SRS/FSRT (90%). When given an option between WBRT or SRS for salvage treatment, all patients favored SRS. The physicians consisted of radiation oncologists (69.3%), neurosurgeons (19.3%), medical oncologists (8.0%), and neuro-oncologists (3.4%). Most physicians were confident or very confident in their ability to discuss the risks and benefits of SRS for brain metastases (78.9%), but this was significantly lower if the patient had received prior SRS (56.6%, P<.001). In these cases, there were significant differences in response by medical specialty and confidence level (P<0.05). Conclusions: Patients and physicians view tumor control followed by long-term toxicity as the most important factors for salvage therapy after initial SRS for brain metastases.

Metabolic signatures derived from whole-brain MR-spectroscopy identify early tumor progression in high-grade gliomas using machine learning.

PURPOSE: Recurrence for high-grade gliomas is inevitable despite maximal safe resection and adjuvant chemoradiation, and current imaging techniques fall short in predicting future progression. However, we introduce a novel whole-brain magnetic resonance spectroscopy (WB-MRS) protocol that delves into the intricacies of tumor microenvironments, offering a comprehensive understanding of glioma progression to inform expectant surgical and adjuvant intervention. METHODS: We investigated five locoregional tumor metabolites in a post-treatment population and applied machine learning (ML) techniques to analyze key relationships within seven regions of interest: contralateral normal-appearing white matter (NAWM), fluid-attenuated inversion recovery (FLAIR), contrast-enhancing tumor at time of WB-MRS (Tumor), areas of future recurrence (AFR), whole-brain healthy (WBH), non-progressive FLAIR (NPF), and progressive FLAIR (PF). Five supervised ML classification models and a neural network were developed, optimized, trained, tested, and validated. Lastly, a web application was developed to host our novel calculator, the Miami Glioma Prediction Map (MGPM), for open-source interaction. RESULTS: Sixteen patients with histopathological confirmation of high-grade glioma prior to WB-MRS were included in this study, totaling 118,922 whole-brain voxels. ML models successfully differentiated normal-appearing white matter from tumor and future progression. Notably, the highest performing ML model predicted glioma progression within fluid-attenuated inversion recovery (FLAIR) signal in the post-treatment setting (mean AUC = 0.86), with Cho/Cr as the most important feature. CONCLUSIONS: This study marks a significant milestone as the first of its kind to unveil radiographic occult glioma progression in post-treatment gliomas within 8 months of discovery. These findings underscore the utility of ML-based WB-MRS growth predictions, presenting a promising avenue for the guidance of early treatment decision-making. This research represents a crucial advancement in predicting the timing and location of glioblastoma recurrence, which can inform treatment decisions to improve patient outcomes.

Racial Disparities in Glioblastoma Genomic Alterations: A Comprehensive Analysis of a Multi-Institution Cohort of 2390 Patients.

BACKGROUND: Although molecular biomarkers have significantly advanced precision oncology in glioblastoma, the prevalence of these biomarkers by race remains underexplored. This study aims to characterize the genomic alterations in glioblastoma across Asian, Black, and White patients, offering insights into racial disparities that may influence treatment outcomes and disease progression. METHODS: Analyzing data from the American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange database V13.0, this study examined 2390 primary glioblastoma samples from unique patients. Genomic alterations in 566 cancer-related genes were assessed using targeted next-generation sequencing panels from 3 large cancer institutes. The patient cohort included 112 Asians, 67 Blacks, and 2211 Whites. Statistical significance of associations between genomic alterations and race was evaluated using the chi-squared test, with the Benjamini-Hochberg method applied to control for multiple testing adjustments. RESULTS: Significant racial differences were observed in the frequency of genomic alterations. Asians exhibited a higher frequency of TP53 alterations (52.68%, P < 0.001), Blacks showed more frequent alterations in NRAS (7.46%, P < 0.001), MTOR (10.45%, P = 0.039), and TET2 (8.96%, P = 0.039), and Whites had a higher occurrence of PTEN alterations (48.67%, P = 0.045). Additionally, Black patients had an elevated rate of RET deletions (14.29%, P < 0.001). CONCLUSIONS: This study identifies significant racial disparities in the alteration frequencies of 6 key glioblastoma genes: NRAS, TP53, MTOR, TET2, PTEN, and RET. These findings underscore the need for racial considerations in glioblastoma treatment strategies and highlight potential avenues for targeted therapeutic interventions. Further research is needed to explore the clinical implications of these genomic disparities.

Salvage Stereotactic Radiosurgery for Recurrent WHO Grade 2 and 3 Meningiomas: A Multicenter Study (STORM).

PURPOSE: The role of stereotactic radiosurgery (SRS) in the management of grade 2 and 3 meningiomas is not well elucidated. Unfortunately, local recurrence rates are high, and guidelines for management of recurrent disease are lacking. To address this knowledge gap, we conducted STORM (Salvage Stereotactic Radiosurgery for Recurrent WHO Grade 2 and 3 Meningiomas), a multicenter retrospective cohort study of patients treated with primary SRS for recurrent grade 2 and 3 meningiomas. METHODS AND MATERIALS: Data on patients with recurrent grade 2 and 3 meningioma treated with SRS at first recurrence were retrospectively collected from 8 academic centers in the United States. Patients with multiple lesions at the time of initial diagnosis or more than 2 lesions at the time of first recurrence were excluded from this analysis. Patient demographics and treatment parameters were extracted at time of diagnosis, first recurrence, and second recurrence. Oncologic outcomes, including progression-free survival (PFS) and overall survival, as well as toxicity outcomes, were reported at the patient level. RESULTS: From 2000 to 2022, 108 patients were identified (94% grade 2, 6.0% grade 3). A total of 106 patients (98%) had upfront surgical resection (60% gross-total resection) with 18% receiving adjuvant radiation therapy (RT). Median time to first progression was 2.5 years (IQR, 1.34-4.30). At first recurrence, patients were treated with single or fractionated SRS to a median marginal dose of 16 Gy to a maximum of 2 lesions (87% received single-fraction SRS). The median follow-up time after SRS was 2.6 years. The 1-, 2-, and 3-year PFS was 90%, 75%, and 57%, respectively, after treatment with SRS. The 1-, 2-, and 3-year overall survival was 97%, 94%, and 92%, respectively. In the multivariable analysis, grade 3 disease (HR, 6.80; 95% CI, 1.61-28.6), male gender (HR, 3.48; 95% CI, 1.47-8.26), and receipt of prior RT (HR, 2.69; 95% CI, 1.23-5.86) were associated with worse PFS. SRS dose and tumor volume were not correlated with progression. Treatment was well tolerated, with a 3.0% incidence of grade 2+ radiation necrosis. CONCLUSIONS: This is the largest multicenter study to evaluate salvage SRS in recurrent grade 2 and 3 meningiomas. In this select cohort of patients with primarily grade 2 meningioma with a potentially more favorable natural history of delayed, localized first recurrence amenable to salvage SRS, local control rates and toxicity profiles were favorable, warranting further prospective validation.

Validation of daily 0.35 T diffusion-weighted MRI for MRI-guided glioblastoma radiotherapy.

BACKGROUND: MRI-Linac systems enable daily diffusion-weighed imaging (DWI) MRI scans for assessing glioblastoma tumor changes with radiotherapy treatment. PURPOSE: Our study assessed the image quality of echoplanar imaging (EPI)-DWI scans compared with turbo spin echo (TSE)-DWI scans at 0.35 Tesla (T) and compared the apparent diffusion coefficient (ADC) values and distortion of EPI-DWI on 0.35 T MRI-Linac compared to high-field diagnostic MRI scanners. METHODS: The calibrated National Institute of Standards and Technology (NIST)/Quantitative Imaging Biomarkers Alliance (QIBA) Diffusion Phantom was scanned on a 0.35 T MRI-Linac, and 1.5 T and 3 T MRI with EPI-DWI. Five patients were scanned on a 0.35 T MRI-Linac with a TSE-DWI sequence, and five other patients were scanned with EPI-DWI on a 0.35 T MRI-Linac and a 3 T MRI. The quality of images was compared between the TSE-DWI and EPI-DWI on the 0.35 T MRI-Linac assessing signal-to-noise ratios and presence of artifacts. EPI-DWI ADC values and distortion magnitude were measured and compared between 0.35 T MRI-Linac and high-field MRI for both phantom and patient studies. RESULTS: The average ADC differences between EPI-DWI acquired on the 0.35 T MRI-Linac, 1.5 T and 3 T MRI scanners and published references in the phantom study were 1.7%, 0.4% and 1.0%, respectively. Comparing the ADC values based on EPI-DWI in glioblastoma tumors, there was a 3.36% difference between 0.35 and 3 T measurements. Susceptibility-induced distortions in the EPI-DWI phantoms were 0.46 ± 1.51 mm for 0.35 MRI-Linac, 0.98 ± 0.51 mm for 1.5 T MRI and 1.14 ± 1.88 mm for 3 T MRI; for patients -0.47 ± 0.78 mm for 0.35 T and 1.73 ± 2.11 mm for 3 T MRIs. The mean deformable registration distortion for a phantom was 1.1 ± 0.22 mm, 3.5 ± 0.39 mm and 4.7 ± 0.37 mm for the 0.35 T MRI-Linac, 1.5 T MRI, and 3 T MRI scanners, respectively; for patients this distortion was -0.46 ± 0.57 mm for 0.35 T and 4.2 ± 0.41 mm for 3 T. EPI-DWI 0.35 T MRI-Linac images showed higher SNR and lack of artifacts compared with TSE-DWI, especially at higher b-values up to 1000 s/mm2. CONCLUSION: EPI-DWI on a 0.35 T MRI-Linac showed superior image quality compared with TSE-DWI, minor and less distortions than high-field diagnostic scanners, and comparable ADC values in phantoms and glioblastoma tumors. EPI-DWI should be investigated on the 0.35 T MRI-Linac for prediction of early response in patients with glioblastoma.

Cerebrospinal Fluid Testing in Leptomeningeal Progression of HER2-Negative Breast Cancer Reveals HER2 Positivity, Leading to HER2-Targeted Therapy: A Case Report.

The treatment of breast cancer is largely determined by protein expression assays of estrogen receptor, progesterone receptor, and Her2/neu (HER2) status. These prognostic markers may vary due to tumor heterogeneityor the evolution of prognostic markers throughout the course of treatment. This report presents a case of a patient who initially presented with HER2-negative breast cancer and had rapidly progressed on numerous lines of treatment. An analysis of cerebrospinal fluid via next-generation sequencing and biopsy of metastasis to the liver identified HER2-positive cancer, which allowed for the use of trastuzumab deruxtecan, a HER2-targeted therapy. This led to an excellent clinical response with improvement in performance status and quality of life. This case report demonstrates the importance of continuing to follow a patient's cancer pathology to open the doors for other opportunities for treatment. Cancer has the potential to evolve and there is a benefit of obtaining rebiopsies to ensure the correct targeted therapies are provided to the patient.

Convolutional Neural Networks for Glioma Segmentation and Prognosis: A Systematic Review.

Deep learning (DL) is poised to redefine the way medical images are processed and analyzed. Convolutional neural networks (CNNs), a specific type of DL architecture, are exceptional for high-throughput processing, allowing for the effective extraction of relevant diagnostic patterns from large volumes of complex visual data. This technology has garnered substantial interest in the field of neuro-oncology as a promising tool to enhance medical imaging throughput and analysis. A multitude of methods harnessing MRI-based CNNs have been proposed for brain tumor segmentation, classification, and prognosis prediction. They are often applied to gliomas, the most common primary brain cancer, to classify subtypes with the goal of guiding therapy decisions. Additionally, the difficulty of repeating brain biopsies to evaluate treatment response in the setting of often confusing imaging findings provides a unique niche for CNNs to help distinguish the treatment response to gliomas. For example, glioblastoma, the most aggressive type of brain cancer, can grow due to poor treatment response, can appear to grow acutely due to treatment-related inflammation as the tumor dies (pseudo-progression), or falsely appear to be regrowing after treatment as a result of brain damage from radiation (radiation necrosis). CNNs are being applied to separate this diagnostic dilemma. This review provides a detailed synthesis of recent DL methods and applications for intratumor segmentation, glioma classification, and prognosis prediction. Furthermore, this review discusses the future direction of MRI-based CNN in the field of neuro-oncology and challenges in model interpretability, data availability, and computation efficiency.

Dose-Painting Linear Accelerator Radiosurgery of Glomus Jugulare With Dosimetric Comparison to Gamma Knife.

Objectives In this study, we outline our rationale for delivering a dose of ≥15 Gy in stereotactic radiosurgery (SRS) of glomus jugulare tumor (GJT) while ensuring the avoidance of complications associated with doses >13 Gy to the facial nerve. To avoid such complications, we initially utilized the Gamma Knife Perfexion (GK) system (Elekta Instrument AB, Stockholm, Sweden) at our institution but encountered challenges related to lengthy treatment times and difficulty in sculpting doses to minimize doses to spare the facial nerve. As a potential solution, we propose the use of HyperArc (Varian Medical Systems, Palo Alto, CA), a newly developed automated delivery platform for linear accelerator (LINAC)-based SRS. HyperArc offers the potential for faster treatment and more complex shaping of the radiotherapy dose with multiple arcs and multi-leaf collimators. Methods We retrospectively reviewed nine cases of patients with GJT treated with HyperArc. Patients' demographic and treatment data were collected. Additionally, simulated GK treatment plans were created and compared with HyperArc plans to assess time savings, PTV coverage, and plan quality. Results One male and eight female patients, with a mean age of 63.9 years, were included. Treatments were delivered on average in 29 minutes, achieving 95-100% of the tumor while limiting the facial nerve to <13 Gy. Treatments replanned using our GK system could achieve only 92-99% tumor coverage while respecting facial nerve constraints, with average treatment times of 180 minutes. Comparable plan quality parameters were attained with both modalities. Conclusions The HyperArc system provides a qualitatively satisfactory and rapid treatment delivery of a highly sculpted radiotherapy dose to maximize tumor coverage and minimize facial nerve complications.

Evolving concepts in margin strategies and adaptive radiotherapy for glioblastoma: A new future is on the horizon.

Chemoradiotherapy is the standard treatment after maximal safe resection for glioblastoma (GBM). Despite advances in molecular profiling, surgical techniques, and neuro-imaging, there have been no major breakthroughs in radiotherapy (RT) volumes in decades. Although the majority of recurrences occur within the original gross tumor volume (GTV), treatment of a clinical target volume (CTV) ranging from 1.5 to 3.0 cm beyond the GTV remains the standard of care. Over the past 15 years, the incorporation of standard and functional MRI sequences into the treatment workflow has become a routine practice with increasing adoption of MR simulators, and new integrated MR-Linac technologies allowing for daily pre-, intra- and post-treatment MR imaging. There is now unprecedented ability to understand the tumor dynamics and biology of GBM during RT, and safe CTV margin reduction is being investigated with the goal of improving the therapeutic ratio. The purpose of this review is to discuss margin strategies and the potential for adaptive RT for GBM, with a focus on the challenges and opportunities associated with both online and offline adaptive workflows. Lastly, opportunities to biologically guide adaptive RT using non-invasive imaging biomarkers and the potential to define appropriate volumes for dose modification will be discussed.

Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system.

PURPOSE: MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35 T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3D-T1w) and dynamic contrast-enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35 T MRI-Linac. METHODS AND MATERIALS: The protocol implemented was used to acquire 3D-T1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35 T MRI-Linac. The detection of post-contrast-enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35 T MRI-Linac to images obtained using a 3 T scanner. The DCE data were tested temporally and spatially using data from a flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes. RESULTS: The 3D-T1w contrast-enhancement volumes were visually and volumetrically similar between 0.35 T MRI-Linac and 3 T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54 % decrease and 8.6 % increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. CONCLUSION: Our findings support the feasibility of obtaining post-contrast 3D-T1w and DCE data from patients with glioblastoma using a 0.35 T MRI-Linac system.

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma.

Glioblastoma changes during chemoradiotherapy are inferred from high-field MRI before and after treatment but are rarely investigated during radiotherapy. The purpose of this study was to develop a deep learning network to automatically segment glioblastoma tumors on daily treatment set-up scans from the first glioblastoma patients treated on MRI-linac. Glioblastoma patients were prospectively imaged daily during chemoradiotherapy on 0.35T MRI-linac. Tumor and edema (tumor lesion) and resection cavity kinetics throughout the treatment were manually segmented on these daily MRI. Utilizing a convolutional neural network, an automatic segmentation deep learning network was built. A nine-fold cross-validation schema was used to train the network using 80:10:10 for training, validation, and testing. Thirty-six glioblastoma patients were imaged pre-treatment and 30 times during radiotherapy (n = 31 volumes, total of 930 MRIs). The average tumor lesion and resection cavity volumes were 94.56 ± 64.68 cc and 72.44 ± 35.08 cc, respectively. The average Dice similarity coefficient between manual and auto-segmentation for tumor lesion and resection cavity across all patients was 0.67 and 0.84, respectively. This is the first brain lesion segmentation network developed for MRI-linac. The network performed comparably to the only other published network for auto-segmentation of post-operative glioblastoma lesions. Segmented volumes can be utilized for adaptive radiotherapy and propagated across multiple MRI contrasts to create a prognostic model for glioblastoma based on multiparametric MRI.

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.

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.

Neoadjuvant chemotherapy and stereotactic body radiation therapy for borderline resectable pancreas adenocarcinoma: influence of vascular margin status and type of chemotherapy.

BACKGROUND: The influence of chemotherapy type and vascular margin status after sequential chemotherapy and stereotactic body radiation therapy (SBRT) for borderline resectable pancreatic cancer (BRPC) is unknown. METHODS: A retrospective review was performed on BRPC patients treated with chemotherapy and 5-fraction SBRT from 2009 to 2021. Surgical outcomes and SBRT-related toxicity were reported. Clinical outcomes were estimated by Kaplan-Meier with log rank comparisons. RESULTS: A total of 303 patients received neoadjuvant chemotherapy and SBRT to a median dose of 40 Gy prescribed to the tumor-vessel interface and median dose of 32.4 Gyto 95% of the gross tumor volume. One hundred and sixty-nine patients (56%) were resected and benefited from improved median OS (41.1 vs 15.5 months, P < 0.001). Close/positive vascular margins were not associated with worse OS or FFLRF. Type of neoadjuvant chemotherapy did not influence OS for resected patients, but FOLFIRINOX was associated with improved median OS in unresected patients (18.2 vs 13.1 months, P = 0.001). CONCLUSION: For BRPC, the effect of a positive or close vascular margin may be mitigated by neoadjuvant therapy. Shorter duration neoadjuvant chemotherapy as well as the optimal biological effective dose of radiotherapy should be prospectively explored.

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.

Implementation and evaluation of a dynamic contrast enhanced MR perfusion protocol for glioblastoma using a 0.35T MRI-Linac system.

Purpose: MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3DT1w) and dynamic contrast enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35T MRI-Linac. Methods and materials: The protocol implemented was used to acquire 3DT1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35T-MRI-Linac. The detection of post-contrast enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35T-MRI-Linac to images obtained using a 3T-standalone scanner. The DCE data were tested temporally and spatially using data from the flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes. Results: The 3D-T1 contrast enhancement volumes were visually and volumetrically similar (±0.6-3.6%) between 0.35T MRI-Linac and 3T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54% decrease and 8.6% increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. Conclusion: Our findings support the feasibility of obtaining post-contrast 3DT1w and DCE data from patients with glioblastoma using a 0.35T MRI-Linac system.

Simulated Adaptive Radiotherapy for Shrinking Glioblastoma Resection Cavities on a Hybrid MRI-Linear Accelerator.

During radiation therapy (RT) of glioblastoma, daily MRI with combination MRI-linear accelerator (MRI-Linac) systems has demonstrated significant anatomic changes, including evolving post-surgical cavity shrinkage. Cognitive function RT for brain tumors is correlated with radiation doses to healthy brain structures, especially the hippocampi. Therefore, this study investigates whether adaptive planning to the shrinking target could reduce normal brain RT dose with the goal of improving post-RT function. We evaluated 10 glioblastoma patients previously treated on a 0.35T MRI-Linac with a prescription of 60 Gy delivered in 30 fractions over six weeks without adaptation ("static plan") with concurrent temozolomide chemotherapy. Six weekly plans were created per patient. Reductions in the radiation dose to uninvolved hippocampi (maximum and mean) and brain (mean) were observed for weekly adaptive plans. The dose (Gy) to the hippocampi for static vs. weekly adaptive plans were, respectively: max 21 ± 13.7 vs. 15.2 ± 8.2 (p = 0.003) and mean 12.5 ± 6.7 vs. 8.4 ± 4.0 (p = 0.036). The mean brain dose was 20.6 ± 6.0 for static planning vs. 18.7 ± 6.8 for weekly adaptive planning (p = 0.005). Weekly adaptive re-planning has the potential to spare the brain and hippocampi from high-dose radiation, possibly reducing the neurocognitive side effects of RT for eligible patients.

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).

Delineation of recurrent glioblastoma by whole brain spectroscopic magnetic resonance imaging.

BACKGROUND: Glioblastoma (GBM) cellularity correlates with whole brain spectroscopic MRI (sMRI) generated relative choline to N-Acetyl-Aspartate ratio (rChoNAA) mapping. In recurrent GBM (rGBM), tumor volume (TV) delineation is challenging and rChoNAA maps may assist with re-RT targeting. METHODS: Fourteen rGBM patients underwent sMRI in a prospective study. Whole brain sMRI was performed to generate rChoNAA maps. TVs were delineated by the union of rChoNAA ratio over 2 (rChoNAA > 2) on sMRI and T1PC. rChoNAA > 2 volumes were compared with multiparametric MRI sequences including T1PC, T2/FLAIR, diffusion-restriction on apparent diffusion coefficient (ADC) maps, and perfusion relative cerebral blood volume (rCBV). RESULTS: rChoNAA > 2 (mean 27.6 cc, range 6.6-79.1 cc) was different from other imaging modalities (P ≤ 0.05). Mean T1PC volumes were 10.7 cc (range 1.2-31.4 cc). The mean non-overlapping volume of rChoNAA > 2 and T1PC was 29.2 cm3. rChoNAA > 2 was 287% larger (range 23% smaller-873% larger) than T1PC. T2/FLAIR volumes (mean 111.7 cc, range 19.0-232.7 cc) were much larger than other modalities. rCBV volumes (mean 6.2 cc, range 0.2-19.1 cc) and ADC volumes were tiny (mean 0.8 cc, range 0-3.7 cc). Eight in-field failures were observed. Three patients failed outside T1PC but within rChoNAA > 2. No grade 3 toxicities attributable to re-RT were observed. Median progression-free and overall survival for re-RT patients were 6.5 and 7.1 months, respectively. CONCLUSIONS: Treatment of rGBM may be optimized by sMRI, and failure patterns suggest benefit for dose-escalation within sMRI-delineated volumes. Dose-escalation and radiologic-pathologic studies are underway to confirm the utility of sMRI in rGBM.