Prostate cancer brain metastases: Monitoring response to treatment with PSMA PET/CT

Prostate cancer brain metastases are rare but increasingly recognized with prostate-specific membrane antigen (PSMA) PET/CT. Distinguishing tumor response from postradiation changes are challenging on MRI. PSMA PET/CT may clarify equivocal brain lesions after radiotherapy. A 71-year-old man with metastatic prostate cancer developed 2 new brain lesions on PSMA PET/CT. Lesions were high PSMA-avid and MRI follow up showed enhancing masses with edema, consistent with metastases. He underwent whole-brain radiation. Follow-up PSMA PET/CT after radiotherapy demonstrated significantly decreased lesion size and activity, with activity lower than blood pool, indicating a treatment response. MRI also showed near-resolution of the lesions. This case highlights the potential utility of PSMA PET/CT for detecting prostate cancer brain metastases and monitoring treatment response. PSMA PET/CT provides valuable complementary information to MRI for managing irradiated prostate cancer brain metastases.

Gradual normalization of superscan in prostate cancer: A case report and literature review

This report presents the imaging findings in a patient with advanced prostate cancer and bone metastases. A superscan pattern on the initial whole-body bone scan suggested extensive disease. The patient responded well to definitive treatment, exhibiting clinical improvement based on decreased PSA levels and CT findings in 6-month follow-up. However, serial follow-up bone scans showed normalization in about 18 months. This paper aims to discuss the limitations of bone scintigraphy in evaluating treatment responses in patients with prostate cancer.

Impact of Workflow and Educational Interventions on MR Safety in Radiation Oncology

PURPOSE/OBJECTIVE(S)

Magnetic resonance imaging (MRI) is becoming increasingly integrated into radiation oncology (RO) departments with the use of MRI-Linacs and MRI simulation. Due to the number of implants in patients with cancer, adoption of comprehensive patient screening and MR safety workflows in RO is critical. Identifying MR unsafe implants only at the time of MRI simulation leads to same-day cancellations, potentially delaying treatment, and can risk MR safety events (SEs). This quality improvement study evaluated the impact of workflow and educational interventions on MR safety in RO at a single institution.

MATERIALS/METHODS

In an effort to decrease same-day cancellations and improve safety surrounding use of a 3 Tesla MRI simulator at an academic center, three plan-see-do-act (PDSA) cycles were implemented from 4/18/22 - 1/19/23. MR safety oversight for the simulator was provided by a multidisciplinary team, with input from both radiology and RO. PDSA cycle 1 implemented a two-screen functional workflow, adapted from radiology at the same institution. The first screen is completed by the practice coordinator (PC) at the time of scheduling to triage high-risk patients into a work queue (WQ) for further evaluation by the MR safety team. The second screen is performed by the MR technologist (MRT) at the point of care. PDSA cycle 2 involved education for PCs. PDSA cycle 3 was a second PC educational intervention including a visual aide to assist with WQ use. Efficacy was determined by the number of same-day cancellations, patients in the WQ (a measure of the number of patients identified at the initial screen as having an implant), and SEs in each PDSA cycle.

RESULTS

PDSA cycle 1 spanned 56 workdays during which 91 MR simulations were scheduled with 6 cancellations (6.5%). PDSA cycle 2 spanned 84 days during which 173 MR simulations were scheduled with 18 cancellations (10.4%). PDSA cycle 3 spanned 39 workdays and had 94 MR simulations, with 7 cancellations (7.4%). The cancellation rate during each PDSA cycle was 0.11, 0.21, and 0.17 cancellations/day, respectively. The number of patients in the WQ during each PDSA cycle, representing successfully screened high-risk patients, was 0, 0, and 3, respectively. There were no SEs during the study.

CONCLUSION

In this study, an MR safety workflow from radiology was successfully implemented in RO. There were no SEs during the study, but the number of patients successfully screened as high-risk and placed in the WQ increased after repeat PC education. Further increases in WQ use would decrease the demand for implant assessment at point of care, which could decrease burden on the MRT, same day cancellations, and potentially SEs. This will be especially important if case load increases. Future work could expand educational efforts to additional staff.

DeepSWI: Using Deep Learning to Enhance Susceptibility Contrast on T2*-Weighted MRI

BACKGROUND

Although susceptibility-weighted imaging (SWI) is the gold standard for visualizing cerebral microbleeds (CMBs) in the brain, the required phase data are not always available clinically. Having a postprocessing tool for generating SWI contrast from T2*-weighted magnitude images is therefore advantageous.

PURPOSE

To create synthetic SWI images from clinical T2*-weighted magnitude images using deep learning and evaluate the resulting images in terms of similarity to conventional SWI images and ability to detect radiation-associated CMBs.

STUDY TYPE

Retrospective.

POPULATION

A total of 145 adults (87 males/58 females; 43.9 years old) with radiation-associated CMBs were used to train (16,093 patches/121 patients), validate (484 patches/4 patients), and test (2420 patches/20 patients) our networks.

FIELD STRENGTH/SEQUENCE

3D T2*-weighted, gradient-echo acquired at 3 T.

ASSESSMENT

Structural similarity index (SSIM), peak signal-to-noise-ratio (PSNR), normalized mean-squared-error (nMSE), CMB counts, and line profiles were compared among magnitude, original SWI, and synthetic SWI images. Three blinded raters (J.E.V.M., M.A.M., B.B. with 8-, 6-, and 4-years of experience, respectively) independently rated and classified test-set images.

STATISTICAL TESTS

Kruskall-Wallis and Wilcoxon signed-rank tests were used to compare SSIM, PSNR, nMSE, and CMB counts among magnitude, original SWI, and predicted synthetic SWI images. Intraclass correlation assessed interrater variability. P values <0.005 were considered statistically significant.

RESULTS

SSIM values of the predicted vs. original SWI (0.972, 0.995, 0.9864) were statistically significantly higher than that of the magnitude vs. original SWI (0.970, 0.994, 0.9861) for whole brain, vascular structures, and brain tissue regions, respectively; 67% (19/28) CMBs detected on original SWI images were also detected on the predicted SWI, whereas only 10 (36%) were detected on magnitude images. Overall image quality was similar between the synthetic and original SWI images, with less artifacts on the former.

CONCLUSIONS

This study demonstrated that deep learning can increase the susceptibility contrast present in neurovasculature and CMBs on T2*-weighted magnitude images, without residual susceptibility-induced artifacts. This may be useful for more accurately estimating CMB burden from magnitude images alone.

EVIDENCE LEVEL

3.

TECHNICAL EFFICACY

Stage 2.

Adjuvant Chemoradiotherapy within One Year of Resection for Molecularly Defined Astrocytoma

PURPOSE/OBJECTIVE(S)

Treatments for diffuse low-grade gliomas (LGG) are controversial. Level I evidence supports the use of adjuvant radiotherapy (RT) and PCV chemotherapy for histologic LGG, but integration of molecular biomarkers in recent WHO classification and the emergence of temozolomide chemotherapy for gliomas necessitates additional investigation of the optimal treatment and timing of postoperative interventions. We hypothesized molecularly-defined LGG (IDH-mutant astrocytoma (astro) and IDH-mutant, 1p/19q-codeleted oligodendroglioma (oligo)) may have different clinical outcomes following adjuvant RT (aRT) with chemotherapy (aRT+chemo) vs observation or chemo alone.

MATERIALS/METHODS

A retrospective analysis of consecutive adult patients diagnosed with WHO Grade 2 astrocytoma or oligodendroglioma who underwent initial resection at a single institution from January 1998 to November 2017 was performed. Wilcoxon rank sum and Chi-squared tests were used to compare continuous and categorical variables, respectively. Survival analyses were performed using the Kaplan-Meier method and Cox proportional hazards models. Patients without clinical progression or death were censored at the date of last follow-up. Pre-operative and post-operative T2 FLAIR hyperintense tumor volumes were quantified using 3D Slicer to calculate extent of resection (EOR).

RESULTS

A total of 342 patients with molecularly-defined LGG (178 astro, 164 oligo) were identified with a median follow up of 9.1 yr. 171 (50%) patients received RT during their treatment course, of which 31 (18%) were treated with aRT within 1 year of diagnosis. The median aRT dose was 54 Gy (range: 40-60 Gy). aRT was more likely for astro (58%) vs oligo (41%, p = 0.001) and for patients who had resections with lower median EOR (88% vs 95%, p = 0.014). 53 patients (15%) were treated with chemo alone, and 136 patients (40%) were treated with aRT+chemo. Temozolomide was used for 161 patients (85%). For astro, aRT+chemo was associated with longer PFS (median 14.9 yr) compared to observation (4.8 yr, p = 0.05), aRT without chemo (5.2 yr, p = 0.01), or chemo alone (4.7 yr, p = 0.02). For oligo, aRT+chemo was associated with longer PFS (median not reached) compared to aRT without chemo (1.6 yr, p = 0.03), but not when compared to observation (median not reached, p = 0.47), or chemo alone (7.9 yr, p = 0.45). Multivariate analysis showed preoperative tumor volume, EOR, and aRT+chemo (but not aRT or chemo alone) were independently associated with astro PFS compared to observation. Propensity matching based on pre-operative tumor volume, EOR, and age demonstrated longer astro PFS after aRT+chemo (14.9 yr) compared to observation or chemo alone (4.5 yr, p = 0.015), without significant difference in OS (18.2 vs. 11.5 yr, p = 0.40).

CONCLUSION

Retrospective data from a single institution support the use of adjuvant radiotherapy with chemotherapy for patients with molecular astrocytomas, while the role of this approach for oligodendrogliomas is unclear in this cohort.

Association of partial T2-FLAIR mismatch sign and isocitrate dehydrogenase mutation in WHO grade 4 gliomas: results from the ReSPOND consortium

PURPOSE

While the T2-FLAIR mismatch sign is highly specific for isocitrate dehydrogenase (IDH)-mutant, 1p/19q-noncodeleted astrocytomas among lower-grade gliomas, its utility in WHO grade 4 gliomas is not well-studied. We derived the partial T2-FLAIR mismatch sign as an imaging biomarker for IDH mutation in WHO grade 4 gliomas.

METHODS

Preoperative MRI scans of adult WHO grade 4 glioma patients (n = 2165) from the multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium were analyzed. Diagnostic performance of the partial T2-FLAIR mismatch sign was evaluated. Subset analyses were performed to assess associations of imaging markers with overall survival (OS).

RESULTS

One hundred twenty-one (5.6%) of 2165 grade 4 gliomas were IDH-mutant. Partial T2-FLAIR mismatch was present in 40 (1.8%) cases, 32 of which were IDH-mutant, yielding 26.4% sensitivity, 99.6% specificity, 80.0% positive predictive value, and 95.8% negative predictive value. Multivariate logistic regression demonstrated IDH mutation was significantly associated with partial T2-FLAIR mismatch (odds ratio [OR] 5.715, 95% CI [1.896, 17.221], p = 0.002), younger age (OR 0.911 [0.895, 0.927], p < 0.001), tumor centered in frontal lobe (OR 3.842, [2.361, 6.251], p < 0.001), absence of multicentricity (OR 0.173, [0.049, 0.612], p = 0.007), and presence of cystic (OR 6.596, [3.023, 14.391], p < 0.001) or non-enhancing solid components (OR 6.069, [3.371, 10.928], p < 0.001). Multivariate Cox analysis demonstrated cystic components (p = 0.024) and non-enhancing solid components (p = 0.003) were associated with longer OS, while older age (p < 0.001), frontal lobe center (p = 0.008), multifocality (p < 0.001), and multicentricity (p < 0.001) were associated with shorter OS.

CONCLUSION

Partial T2-FLAIR mismatch sign is highly specific for IDH mutation in WHO grade 4 gliomas.

The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)

Automated brain tumor segmentation methods have become well-established and reached performance levels offering clear clinical utility. These methods typically rely on four input magnetic resonance imaging (MRI) modalities: T1-weighted images with and without contrast enhancement, T2-weighted images, and FLAIR images. However, some sequences are often missing in clinical practice due to time constraints or image artifacts, such as patient motion. Consequently, the ability to substitute missing modalities and gain segmentation performance is highly desirable and necessary for the broader adoption of these algorithms in the clinical routine. In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023. The primary objective of this challenge is to evaluate image synthesis methods that can realistically generate missing MRI modalities when multiple available images are provided. The ultimate aim is to facilitate automated brain tumor segmentation pipelines. The image dataset used in the benchmark is diverse and multi-modal, created through collaboration with various hospitals and research institutions.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Interactive Effects of Molecular, Therapeutic, and Patient Factors on Outcome of Diffuse Low-Grade Glioma

PURPOSE

In patients with diffuse low-grade glioma (LGG), the extent of surgical tumor resection (EOR) has a controversial role, in part because a randomized clinical trial with different levels of EOR is not feasible.

METHODS

In a 20-year retrospective cohort of 392 patients with IDH-mutant grade 2 glioma, we analyzed the combined effects of volumetric EOR and molecular and clinical factors on overall survival (OS) and progression-free survival by recursive partitioning analysis. The OS results were validated in two external cohorts (n = 365). Propensity score analysis of the combined cohorts (n = 757) was used to mimic a randomized clinical trial with varying levels of EOR.

RESULTS

Recursive partitioning analysis identified three survival risk groups. Median OS was shortest in two subsets of patients with astrocytoma: those with postoperative tumor volume (TV) > 4.6 mL and those with preoperative TV > 43.1 mL and postoperative TV ≤ 4.6 mL. Intermediate OS was seen in patients with astrocytoma who had chemotherapy with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL in addition to oligodendroglioma patients with either preoperative TV > 43.1 mL and residual TV ≤ 4.6 mL or postoperative residual volume > 4.6 mL. Longest OS was seen in astrocytoma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL who received no chemotherapy and oligodendroglioma patients with preoperative TV ≤ 43.1 mL and postoperative TV ≤ 4.6 mL. EOR ≥ 75% improved survival outcomes, as shown by propensity score analysis.

CONCLUSION

Across both subtypes of LGG, EOR beginning at 75% improves OS while beginning at 80% improves progression-free survival. Nonetheless, maximal resection with preservation of neurological function remains the treatment goal. Our findings have implications for surgical strategies for LGGs, particularly oligodendroglioma.

Targeted gene expression profiling predicts meningioma outcomes and radiotherapy responses

Background

Surgery is the mainstay of treatment for meningioma, the most common primary intracranial tumor, but improvements in meningioma risk stratification are needed and current indications for postoperative radiotherapy are controversial. Recent studies have proposed prognostic meningioma classification systems using DNA methylation profiling, copy number variants, DNA sequencing, RNA sequencing, histology, or integrated models based on multiple combined features. Targeted gene expression profiling has generated robust biomarkers integrating multiple molecular features for other cancers, but is understudied for meningiomas.

Methods

Targeted gene expression profiling was performed on 173 meningiomas and an optimized gene expression biomarker (34 genes) and risk score (0 to 1) was developed to predict clinical outcomes. Clinical and analytical validation was performed on independent meningiomas from 12 institutions across 3 continents (N = 1856), including 103 meningiomas from a prospective clinical trial. Gene expression biomarker performance was compared to 9 other classification systems.

Results

The gene expression biomarker improved discrimination of postoperative meningioma outcomes compared to all other classification systems tested in the independent clinical validation cohort for local recurrence (5-year area under the curve [AUC] 0.81) and overall survival (5-year AUC 0.80). The increase in area under the curve compared to the current standard of care, World Health Organization 2021 grade, was 0.11 for local recurrence (95% confidence interval [CI] 0.07-0.17, P < 0.001). The gene expression biomarker identified meningiomas benefiting from postoperative radiotherapy (hazard ratio 0.54, 95% CI 0.37-0.78, P = 0.0001) and re-classified up to 52.0% meningiomas compared to conventional clinical criteria, suggesting postoperative management could be refined for 29.8% of patients.

Conclusions

A targeted gene expression biomarker improves discrimination of meningioma outcomes compared to recent classification systems and predicts postoperative radiotherapy responses.

MerlinS13 phosphorylation controls meningioma Wnt signaling and magnetic resonance imaging features

Meningiomas are the most common primary intracranial tumors and are associated with inactivation of the tumor suppressor NF2/Merlin, but one-third of meningiomas retain Merlin expression and typically have favorable clinical outcomes. Biochemical mechanisms underlying Merlin-intact meningioma growth are incompletely understood, and non-invasive biomarkers that predict meningioma outcomes and could be used to guide treatment de-escalation or imaging surveillance of Merlin-intact meningiomas are lacking. Here we integrate single-cell RNA sequencing, proximity-labeling proteomic mass spectrometry, mechanistic and functional approaches, and magnetic resonance imaging (MRI) across meningioma cells, xenografts, and human patients to define biochemical mechanisms and an imaging biomarker that distinguish Merlin-intact meningiomas with favorable clinical outcomes from meningiomas with unfavorable clinical outcomes. We find Merlin drives meningioma Wnt signaling and tumor growth through a feed-forward mechanism that requires Merlin dephosphorylation on serine 13 (S13) to attenuate inhibitory interactions with β-catenin and activate the Wnt pathway. Meningioma MRI analyses of xenografts and human patients show Merlin-intact meningiomas with S13 phosphorylation and favorable clinical outcomes are associated with high apparent diffusion coefficient (ADC) on diffusion-weighted imaging. In sum, our results shed light on Merlin posttranslational modifications that regulate meningioma Wnt signaling and tumor growth in tumors without NF2/Merlin inactivation. To translate these findings to clinical practice, we establish a non-invasive imaging biomarker that could be used to guide treatment de-escalation or imaging surveillance for patients with favorable meningiomas.

Detection of Malpositioned VP Shunt Catheter by Radionuclide CSF Cisternography

ABSTRACT

A 37-year-old man presented with a 2-week history of abdominal pain, headaches, nausea, vomiting, and leukocytosis. Medical history includes congenital hydrocephalus, with a ventriculoperitoneal shunt placed several years ago. Radionuclide cerebrospinal fluid cisternography shows curvilinear activity in the abdomen, in the pattern of small and large bowel loops, suggesting that the tip of the catheter is inside a small bowel loop. No activity is seen in the intraperitoneal compartment. CT of the abdomen and pelvis followed by laparoscopic surgery confirmed the findings.

Variant Sciatic Nerve Anatomy in Relation to the Piriformis Muscle on Magnetic Resonance Neurography: A Potential Etiology for Extraspinal Sciatica

OBJECTIVE

To assess the prevalence and clinical implications of variant sciatic nerve anatomy in relation to the piriformis muscle on magnetic resonance neurography (MRN), in patients with lumbosacral neuropathic symptoms.

MATERIALS AND METHODS

In this retrospective single-center study, 254 sciatic nerves, from 127 patients with clinical and imaging findings compatible with extra-spinal sciatica on MRN between 2003 and 2013, were evaluated for the presence and type of variant sciatic nerves, split sciatic nerve, abnormal T2-signal hyperintensity, asymmetric piriformis size and increased nerve caliber, and summarized using descriptive statistics. Two-tailed chi-square tests were performed to compare the anatomical variant type and clinical symptoms between imaging and clinical characteristics.

RESULTS

Sixty-four variant sciatic nerves were identified with an equal number of right and left variants. Bilateral variants were noted in 15 cases. Abnormal T2-signal hyperintensity was seen significantly more often in variant compared to conventional anatomy (40/64 vs. 82/190; p = 0.01). A sciatic nerve split was seen significantly more often in variant compared to conventional anatomy (56/64 vs. 20/190; p < 0.0001). Increased nerve caliber, abnormal T2-signal hyperintensity, and asymmetric piriformis size were significantly associated with the clinically symptomatic side compared to the asymptomatic side (98:2, 98:2, and 97:3, respectively; p < 0.0001 for all). Clinical symptoms were correlated with variant compared to conventional sciatic nerve anatomy (64% vs. 46%; p = 0.01).

CONCLUSION

Variant sciatic nerve anatomy, in relation to the piriformis muscle, is frequently identified with MRN and is more likely to be associated with nerve signal changes and symptomatology.

Spectrum of Clinical Presentations, Imaging Findings, and HLA Types in Immune Checkpoint Inhibitor-Induced Hypophysitis

Context

Hypophysitis is a known immune-related adverse event (irAE) of immune checkpoint inhibitors (CPIs), commonly associated with CTLA-4 inhibitors and less often with PD-1/PD-L1 inhibitors.

Objective

We aimed to determine clinical, imaging, and HLA characteristics of CPI-induced hypophysitis (CPI-hypophysitis).

Methods

We examined the clinical and biochemical characteristics, magnetic resonance imaging (MRI) of the pituitary, and association with HLA type in patients with CPI-hypophysitis.

Results

Forty-nine patients were identified. Mean age was 61.3 years, 61.2% were men, 81.6% were Caucasian, 38.8% had melanoma, and 44.5% received PD-1/PD-L1 inhibitor monotherapy while the remainder received CTLA-4 inhibitor monotherapy or CTLA-4/PD-1 inhibitor combination therapy. A comparison of CTLA-4 inhibitor exposure vs PD-1/PD-L1 inhibitor monotherapy revealed faster time to CPI-hypophysitis (median 84 vs 185 days, P < .01) and abnormal pituitary appearance on MRI (odds ratio 7.00, P = .03). We observed effect modification by sex in the association between CPI type and time to CPI-hypophysitis. In particular, anti-CTLA-4 exposed men had a shorter time to onset than women. MRI changes of the pituitary were most common at the time of hypophysitis diagnosis (55.6% enlarged, 37.0% normal, 7.4% empty or partially empty) but persisted in follow-up (23.8% enlarged, 57.1% normal, 19.1% empty or partially empty). HLA typing was done on 55 subjects; HLA type DQ0602 was over-represented in CPI-hypophysitis relative to the Caucasian American population (39.4% vs 21.5%, P = 0.01) and CPI population.

Conclusion

The association of CPI-hypophysitis with HLA DQ0602 suggests a genetic risk for its development. The clinical phenotype of hypophysitis appears heterogenous, with differences in timing of onset, changes in thyroid function tests, MRI changes, and possibly sex related to CPI type. These factors may play an important role in our mechanistic understanding of CPI-hypophysitis.

Federated learning enables big data for rare cancer boundary detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

NIMG-46. TOWARDS PREDICTING TUMOR AGGRESSIVENESS WITH RADIOPATHOMIC ANALYSIS OF MULTI-PARAMETRIC ANATOMICAL, DIFFUSION-WEIGHTED, AND METABOLIC MRI IN PATIENTS WITH NEWLY-DIAGNOSED GLIOMAS

INTRODUCTION

Pathologically aggressive tumor biology can extend beyond the contrast-enhancing or non-enhancing anatomical lesions in patients with glioma. Identification of malignant regions can help guide diagnosis and subsequent treatment planning. This study leverages a unique multi-parametric MRI dataset with tissue samples of known spatial coordinates to noninvasively predict cellular proliferation (KI-67) and a novel index of tumor aggressiveness (TAI), that combines proliferation, cellularity, and tumor-score.

METHODS

420 tissue samples were collected from 162 patients with newly-diagnosed glioma (47% IDH-wildtype). Clinical imaging consisted of T2-weighted, T2-FLAIR, T1-weighted pre- and post-contrast images, and apparent diffusion coefficient (ADC) and fractional anisotropy (FA) from diffusion-weighted imaging. Mean normalized imaging metrics were quantified from 5mm spheres centered at the location of the tissue sample. A single spectrum was reconstructed at the location of each tissue sample from 3D 1H-MR Spectroscopic Imaging (MRSI) before quantifying normalized metabolite peak-heights for choline, creatine, NAA, lactate/lipid, and relative indices. Univariate mixed-effects linear regression models were employed and features with p&lt; 0.2 were selected for subsequent model building. Support vector machine (SVM), random forest, and gradient boosting machine-learning algorithms were trained and tested on a ⅔-⅓ train-test split with 4-fold cross-validation in training to predict a high/low KI-67 and TAI.

RESULTS

Although none of the individual imaging metrics were significantly associated with KI-67 in the univariate analysis, all diffusion and several MRSI metrics (ncholine, nNAA, CNI, excess choline and creatine) were significantly associated with cellularity. Preliminary multivariate analyses to date suggest that the best radiopathomic model performance is achieved when an SVM was used along with T1-precontrast, nADC, and all metabolite levels (mean cross-validation AUC=0.73 and accuracy=.77).

CONCLUSION

Our results suggest that multi-parametric physiologic and metabolic MRI are useful for radiopathomic-mapping of tumor aggressiveness and are currently being optimized in a larger cohort.

NIMG-67. MULTI-PARAMETRIC MRI-BASED MACHINE LEARNING ANALYSIS FOR PREDICTION OF NEOPLASTIC INFILTRATION AND RECURRENCE IN PATIENTS WITH GLIOBLASTOMA: UPDATES FROM THE MULTI-INSTITUTIONAL RESPOND CONSORTIUM

PURPOSE

Glioblastoma is extremely infiltrative with malignant cells extending beyond the enhancing rim where recurrence inevitably occurs, despite aggressive multimodal therapy. We hypothesize that important characteristics of peritumoral tissue heterogeneity captured and analyzed by multi-parametric MRI and artificial intelligence (AI) methods are generalizable in the updated multi-institutional ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium and predictive of neoplastic infiltration and future recurrence.

METHODS

We used the most recent update of the ReSPOND consortium to evaluate and further refine generalizability of our methods with different scanners and acquisition settings. 179 de novo glioblastoma patients with available T1, T1Gd, T2, T2-FLAIR, and ADC sequences at pre-resection baseline and after complete resection with subsequent pathology-confirmed recurrence were included. To establish generalizability of the predictive models, training and testing of the refined AI model was performed through Leave-One-Institution-Out-Cross-Validation schema. The multi-institutional cohort consisted of the Hospital of the University of Pennsylvania (UPenn, 124), Case Western Reserve University/University Hospitals (CWRU/UH, 27), New York University (NYU, 13), Ohio State University (OSU, 13), and University Hospital Río Hortega (RH, 2). Features extracted from pre-resection MRI were used to build the model predicting the spatial pattern of subsequent tumor recurrence. These predictions were evaluated against regions of pathology-confirmed post-resection recurrence.

RESULTS

Our model predicted the locations that later harbored tumor recurrence with overall odds ratio (99% CI)/AUC (99% CI), 12.0(11.8-12.2)/0.80(0.76-0.85), and per institute, CWRU/UH, 11.0(10.7-11.3)/0.80 (0.64-0.97); NYU, 7.0(6.7-7.3)/0.78(0.56-1.00); OSU, 18.3(17.5-19.1)/0.83(0.54-1.00); RH, 40.0(35.3-45.5)/0.93(0.00-1.00); UPenn, 8.00(7.7-8.3)/0.80(0.75-0.84).

CONCLUSION

This study provides extensive multi-institutional validated evidence that machine learning tools can identify peritumoral neoplastic infiltration and predict location of future recurrence, by decrypting the MRI signal heterogeneity in peritumoral tissue. Our analyses leveraged the unique dataset of the ReSPOND consortium, which aims to develop and validate AI-based biomarkers for individualized prediction and prognostication and establish generalizability in a multi-institutional setting.

NIMG-61. IMPROVED GENERALIZABILITY OF RADIOPATHOMIC PROBABILISTIC MAPPING OF TREATMENT-INDUCED EFFECTS WITH PHYSIOLOGIC MR IMAGING AND DEEP LEARNING IN PATIENTS WITH RECURRENT GLIOBLASTOMA

Although physiologic (diffusion-weighted and perfusion-weighted) MRI has shown promise in identifying regions of recurrent tumor (rTumor) in patients with glioblastoma suspected of progression, distinguishing treatment-induced effects (TxE) from rTumor on anatomical MRI remains a challenge. Whereas prior larger-scale machine learning (ML)-based studies mostly utilize anatomical imaging alone and/or perform lesion-level predictions, this study aimed to develop a non-invasive, radiopathomic tool for regional probabilistic mapping of TxE using 208 tissue-samples (55 pathologically-confirmed TxE, 153 recurrent glioblastoma) acquired from 107 patients with known spatial coordinates on pre-surgical MRI. We tested the hypothesis that applying a deep-learning (DL) model that included physiological MRI can: 1) more accurately identify areas of TxE that mimic rTumor on anatomical MRI and 2) better generalize to an independent test set than ML-models or a DL-model that uses anatomical MRI alone. An 80/20 split for training/validation was used after 1/3 of the patients were withheld for testing. Oversampling of TxE samples was employed to address class imbalance and an equal proportion of TxE samples was maintained across all datasets. Three ML-models, their ensemble, and a deep 4D-convolutional-neural-network were trained based on normalized anatomical (post-contrast T1, T2-FLAIR), diffusion-weighted (ADC, FA), and DSC perfusion-weighted (PeakHeight, %recovery) images cropped to 10mm-cubic patches centered on the coordinates from where tissue was obtained. Although Random Forest and voting-ensembled ML-models using all imaging and the anatomical DL-model had the best validation performance (AUC=0.81-0.82), these models did not generalize (test AUC=0.58-0.59). The DL-model including physiologic images had slightly lower validation AUC (0.78) but the best overall test AUC (0.795), indicating superior generalizability. Elevated blood volume (nPeakHeight) was the most important feature. Our DL-model’s interpretability was also demonstrated by disrupting class separation after shuffling voxels within each input patch. These results suggest that using deep-learning with physiologic MRI can improve intratumoral classification of TxE from rTumor.

TMET-04. DETECTING DYNAMIC PYRUVATE TUMOR METABOLISM IN PATIENTS WITH GLIOMA USING HYPERPOLARIZED CARBON-13 METABOLIC IMAGING

INTRODUCTION

Hyperpolarized carbon-13 (HP-13C) MR enables rapid dynamic imaging of metabolic pathways in the human brain using non-toxic, non-radioactive metabolites as tracers. This study presents our unique experience on the benefit of using [1-13C]pyruvate and [2-13C]pyruvate MR for evaluating patients with glioma.

METHODS

132 scans (71 using an integrated 13C /1H coil) including steady-state 1H-MRSI (~10 min) and dynamic HP-13C imaging (60 sec) following injection of HP [1-13C]pyruvate (N=125) or [2-13C]pyruvate (N=7) were acquired from 46 patients with glioma (18F/28M; 15 IDH-mutant, 27 IDH-wildtype, 4 IDH-status-unknown). Maps of temporally-summed 13C-metabolite signals, ratios, and kinetic rate constants were calculated for contrast-enhancing, nonenhancing, and normal-appearing-white-matter (NAWM) regions and compared to steady-state metabolic metrics.

RESULTS

The only adverse event, in a single patient, was a burning sensation after the injection that resolved after saline flush. The mean time-to-injection of HP probes was 58.6±14.0 sec. Signal-to-noise ratios of [1-13C]lactate and [13C]bicarbonate within the NAWM from the HP [1-13C]pyruvate data were 53±39 and 13±6, respectively. The SD/mean of repeated injections (N=3) for lactate/pyruvate and pyruvate-to-lactate conversion rates were 3.8±3.1% and 6.5±3.1% in the NAWM, respectively. Patients with progressive GBM had significantly higher lactate/pyruvate and lower bicarbonate/lactate (p&lt; 0.05) in contrast- and non-enhancing lesions compared to NAWM. Significantly elevated lactate/pyruvate and reduced bicarbonate/lactate (p&lt; 0.01) were found in contrast-enhancing compared to nonenhancing regions, whereas choline/NAA and steady-state 1H-lactate levels were similar. HP [2-13C]pyruvate data showed reduced glutamate/pyruvate and pyruvate-to-glutamate conversion rates in T2 lesions compared to contralateral normal-appearing brain in IDH-mutant gliomas, consistent with known metabolic reprogramming.

CONCLUSION

This study demonstrates the potential benefit of dynamic HP-13C MRI for evaluating patients with glioma, which provides unique and spatially distinct contrast compared to steady-state metabolic imaging. Ongoing studies will further characterize dynamic metabolism in specific glioma subtypes and provide biomarkers for evaluating responses to treatment.

SURG-05. SUPERVISED MACHINE LEARNING IDENTIFIES RISK FACTORS ASSOCIATED WITH LEPTOMENINGEAL DISEASE AFTER SURGICAL RESECTION OF BRAIN METASTASES

INTRODUCTION

Predictors of postoperative leptomeningeal disease (LMD) after resection of brain metastases (BMs) are not well defined.

OBJECTIVE

This study examined rates and predictors of LMD, including subtypes, in patients who underwent resection of a BM followed by postoperative radiation.Method: A retrospective, single-center study was conducted examining overall LMD, classical LMD (cLMD), and nodular LMD (nLMD) risk. Logistic regression and a Cox proportional hazards analyses were performed to identify risk factors associated with LMD. Random forest models were constructed to predict LMD and differentiate cLMD versus nLMD. Accuracy and the area under the receiver operating characteristic curve (AUROC) were calculated to evaluate the models.Result: Of the 217 patients in the cohort, 47 (21.7%) developed postoperative LMD with 19(8.8%) cLMD cases and 28(12.9%) nLMD cases . Six-, 12-, and 24-month LMD-free survival rates were 92.3%, 85.6%, and 71.4%, respectively. Patients with cLMD had worse survival outcomes from LMD diagnosis compared to nLMD (2.4 vs 6.9 mo, Log-rank p=0.02), and treatment of LMD was associated with improved survival for both cLMD and nLMD subtypes. Multivariate Cox hazard analysis identified cerebellar/insular/occipital location (HR 3.25, 95% CI 1.73-6.11, p=0.0003), absence of extracranial disease (HR 2.49, 95% CI 1.27-4.88, p=0.008), and ventricle contact (HR 2.82, 95% CI 1.5-5.3, p=0.001) to be associated with postoperative LMD. A predictive model using random forest analysis with an AUROC of 0.87 in a test cohort identified tumor location, systemic disease status, and tumor volume as the most important factors associated with LMD. Both regression analysis and random forest analysis identified postoperative systemic therapy exposure as the main factor differentiating cLMD from nLMD development.

CONCLUSION

Tumor location, absence of extracranial disease at the time of surgery, contact with a ventricle, and increased tumor volume are associated with LMD. Classical LMD is associated with worse prognosis compared to nLMD.

QOL-10. NOVEL MULTIMODAL STUDY OF THREE COGNITIVE REHABILITATION INTERVENTIONS IN LOWER GRADE GLIOMA

BACKGROUND

Grade 2 and 3 glioma survivors (LrGG) are living longer, yet experience cognitive impairments with diminished quality of life (QOL). We present a novel multimodal study of three cognitive rehabilitation interventions in stable LrGG survivors.

METHODS

Participants were radiologically stable adult LrGG patients who were off medical treatment for ≥ 6 months with subjective and objective cognitive impairments ( &gt;1SD in 2 or more domains). Patients were offered either In-person cognitive rehabilitation (strategy training including telehealth), or randomized to App-based cognitive rehabilitation (retraining and strategy training) versus Text messaging (strategy training). Intervention duration was 3 months. Neuropsychological testing (with parallel forms) and QOL assessments were conducted at baseline (T1), immediate post intervention (T2), and 6-month follow-up (T3), and analyzed with repeated measures regression or Wilcoxon signed rank tests.

RESULTS

Of the 33 analyzed (enrollment ongoing); 15/17 In-person, 5/8 App-based, and 8/8 Texting completed ≥ 80% or greater of interventions. Demographic and clinical characteristics were similar between cohorts. Median age was 48 years (range 27-63), 58% astrocytoma, 30% oligodendroglioma, 15% other (1 pilocytic astrocytoma, 4 diffuse glioma NOS), and 76% had prior radiotherapy. Rehabilitation interventions showed improvements in auditory working memory (T1-T2 In-person p= 0.02, eta2= 0.32-medium effect), verbal learning (T1-T3 App-based p= .06, eta2= 0.54-large effect; T1-T3 Texting p= .01, eta2= 0.75-large effect), and verbal memory (T1-T3 App-based p= .06, rho=0.31-medium effect).

CONCLUSION

Significant improvements in cognitive impairments were found with medium to large treatment effects within each cohort. Cognitive rehabilitation via In-person and Texting showed strongest feasibility and acceptability. In-person cognitive rehab showed earlier posttreatment improvements whereas treatment effects for App-based and Texting were noted, but took longer to realize gains. These interventions may show promise for addressing cognitive impairments in LrGG survivors and warrant further investigation.

EPCO-01. EPIGENETIC REPROGRAMMING SHAPES THE MOLECULAR AND CELLULAR LANDSCAPE OF SCHWANNOMA

DNA methylation profiling provides robust classification of nervous system tumors, but mechanisms driving epigenetic identity of individual tumor types are incompletely understood. Integrating DNA methylation profiling (n=76), RNA sequencing (n=24), single-cell RNA-sequencing (n=9), and mass cytometry (n=9), we discovered vestibular schwannomas are comprised of two epigenetic groups distinguished by neural crest development pathways or repair and regeneration pathways driving immune infiltration. Analyses of preoperative magnetic resonance imaging studies (n=66) or paired primary and recurrent schwannomas (n=13) suggested radiotherapy was sufficient but not necessary for epigenetic reprogramming of neural crest enriched schwannomas into immune enriched schwannomas. In support of this hypothesis, DNA methylation profiling, RNA sequencing, single-cell RNA sequencing, proteomic mass spectrometry, and lymphocyte migration assays demonstrated radiotherapy epigenetically reprogramed viable schwannoma cells to secrete immunomodulatory signals and recruit lymphocytes in vitro. Genome-wide CRISPRi screens identified histone acetyltransferases or DNA methyltransferases driving schwannoma radiotherapy responses, including the epigenetic regulators KDM5C or KDM1A. CRISPRi and lymphocyte migration assays ± radiotherapy confirmed KDM5C drives schwannoma immune infiltration whereas KDM1A inhibits schwannoma immune infiltration. To define genomic mechanisms underlying epigenetic group identity, we performed pooled CRISPRi screening coupled with single-cell RNA sequencing (Perturb-seq) of 44 schwannoma markers. In parallel, we developed single nuclei profiling of chromatin accessibility through paired ATAC sequencing and RNA sequencing coupled with pooled CRISPRi screening (snARC-seq) of 54 epigenetic regulators identified by our genome-wide CRISPRi screen. Functional genomic approaches revealed the tyrosine phosphatase PTPRG as a regulator of survival, and KDM5C and KDM1A as regulators of inflammation. In summary, we report two epigenetic groups of schwannomas and mechanisms underlying epigenetic group identity using a new functional genomic technique allowing for simultaneous interrogation of single-cell epigenetic and gene expression changes in the context of genetic and therapeutic perturbations. These data elucidate a novel epigenetic mechanism of action of radiotherapy.

NIMG-40. RAPID EARLY PROGRESSION (REP) OF GLIOBLASTOMA IS AN INDEPENDENT NEGATIVE PROGNOSTIC FACTOR: RESULTS FROM A SYSTEMATIC REVIEW AND META-ANALYSIS

INTRODUCTION

In patients with newly-diagnosed glioblastoma, rapid early progression (REP) refers to tumour regrowth between surgery and postoperative chemoradiotherapy. This systematic review and meta-analysis appraised published data on REP to better characterise and understand it.

METHODS

Systematic searches of MEDLINE, EMBASE and the Cochrane database from inception to 21/10/21. Studies describing the incidence of REP – tumour growth between the postoperative MRI scan and pre-radiotherapy MRI scan in newly-diagnosed glioblastoma, were included. The primary outcome was REP incidence.

RESULTS

From 1590 search results, 9 studies were included with 716 patients. The median age was 56.9 years (IQR 54.0-58.8 years). There was a male predominance with a median male-to-female ratio of 1.4 (IQR 1.1-1.5). The median number of days between MRI scans was 34 days (IQR 18-45 days). The mean incidence rate of REP was 45.9% (range 19.3%-72.0%) and significantly lower in studies employing functional imaging to define REP (p&lt; 0.001). REP/non-REP groups were comparable with respect to age (p=0.99), gender (p=0.33) and time between scans (p=0.81). REP was associated with shortened overall survival (HR 1.78, 95% CI 1.30-2.43, p&lt; 0.001), shortened progression-free survival (HR 1.78, 95% CI 1.30-2.43, p&lt; 0.001), subtotal resection (OR 6.96, 95% CI 4-51-10.73, p&lt; 0.001) and IDH wildtype versus mutant tumours (OR 0.20, 95% CI 0.02-0.38, p=0.03). MGMT promoter methylation was not associated with REP (OR 1.29, 95% CI 0.72-2.28, p=0.39).

CONCLUSIONS

REP occurs in almost half of patients with newly-diagnosed glioblastoma and has a strongly negative prognostic effect. Future studies should investigate its biology and effective treatment strategies.

NIMG-24. LONGITUDINAL MR SPECTROSCOPY TO DETECT PROGRESSION IN PATIENTS WITH LOWER-GRADE GLIOMA IN THE SURVEILLANCE PHASE

BACKGROUND

Monitoring lower-grade gliomas (LrGGs) for disease progression is made difficult by the limits of anatomical MRI to distinguish treatment-related tissue changes from tumor progression. MR spectroscopic imaging (MRSI) offers additional metabolic information that can help address these challenges. The goal of this study was to compare longitudinal changes in multiparametric MRI, including diffusion-weighted imaging, perfusion imaging, and 3D MRSI, for LrGG patients who progressed at the final time point and those who remained clinically stable.

METHODS

Forty-one patients with LrGG who were clinically stable were longitudinally assessed for progression. Changes in anatomical, diffusion, perfusion, and MRSI data were acquired and compared between patients who remained clinically stable and those who progressed.

RESULTS

Thirty-one patients remained stable, and 10 patients progressed. Over the study period, progressed patients had a significantly greater increase in normalized choline, choline-to-N-acetylaspartic acid index (CNI), normalized creatine, and creatine-to-N-acetylaspartic acid index (CRNI), than stable patients. CRNI was significantly associated with progression status and WHO type. Progressed astrocytoma patients had greater increases in CRNI than stable astrocytoma patients.

CONCLUSIONS

LrGG patients in surveillance with tumors that progressed had significantly increasing choline and creatine metabolite signals on MRSI, with a trend of increasing T2 FLAIR volumes, compared to LrGG patients who remained stable. These data show that MRSI can be used in conjunction with anatomical imaging studies to gain a clearer picture of LrGG progression, especially in the setting of clinical ambiguity.

NIMG-65. IMPROVED SPATIAL MAPPING OF TUMOR AGGRESSIVENESS WITH 1H MAGNETIC RESONANCE SPECTROSCOPY AND DEEP LEARNING IN PATIENTS WITH NEWLY-DIAGNOSED GLIOMA

INTRODUCTION

Noninvasive, radiopathomic mapping of tumor aggressiveness can benefit patients with glioma by guiding the selection of tissue samples for diagnosis, increasing extent of resection, and non-invasively characterizing residual tumor burden for subsequent treatment. Although prior studies have demonstrated the utility of metabolic metrics quantified from 1H-MR Spectroscopy (MRS) in probing tumor pathology, this study evaluated the benefit of using the entire 1D-spectrum and deep learning for radiopathomic mapping of intratumoral cellularity, proliferation (ki-67), and a new tumor aggressiveness index (TAI) defined as log((n(ki−67)+n(cellularity))*tumor-score).

METHODS

Multi-voxel 1H-MRS was acquired on 281 patients newly diagnosed with a glioma (47% IDH-wildtype) immediately before surgical resection. After reconstructing individual spectra at the locations where tissue samples were obtained during surgery and normalizing by NAA in contralateral normal-appearing-white-matter, 607 spectra with corresponding histopathology were deemed of sufficient quality for analysis. A 1D convolutional-neural-network with bidirectional long- and short-term memory deep-learning model using the entire spectrum (0.6-3.6ppm) was compared to mixed-effects regression (with choline-to-NAA index[CNI]) and Random Forest (with CNI+normalized peak heights) models for predicting ki-67, cellularity, and TAI. Results &

DISCUSSION

Using deep-learning on the entire spectrum resulted in 10.3%-22.1% lower mean absolute error (MAE) and 0.32-0.37 higher R2 values compared to using CNI alone or a random forest model with multiple metabolic metrics. MAE values for all 3 deep-learning models were 26-44% &lt; 1 standard deviation of the ground truth, demonstrating reasonable prediction accuracy within the test data set. Although the lowest MAE (0.16) and highest R2 (0.41) was attained when predicting TAI with deep-learning, the prediction of cellularity resulted in the lowest %MAE. Colormaps of predicted pathology identified regions of heightened aggressiveness surrounding tissue samples with most abnormal pathological features that sometimes extended beyond the non-enhancing lesion. Current work is evaluating the clinical utility of our deep-learning model and predicted maps of aggressiveness.

NIMG-33. PROGNOSTIC STRATIFICATION OF DE NOVO GLIOBLASTOMA PATIENTS ACROSS 22 GEOGRAPHICALLY DISTINCT INSTITUTIONS: UPDATES FROM THE RESPOND CONSORTIUM

PURPOSE

Glioblastoma, IDH-wildtype, is the most common primary malignant adult brain tumor with median overall survival (OS) of ~14 months, with little improvement over the last 20 years. We hypothesize that AI-based integration of quantitative tumor characteristics, independent of acquisition protocol and equipment, can reveal accurate generalizable prognostic stratification. We seek an AI-based OS predictor using routine clinically acquired MRI sequences, quantitatively evaluated across institutions of the ReSPOND (Radiomics Signatures for PrecisiON Diagnostics) consortium.

METHODS

We identified a retrospective cohort of 2,293 diffuse glioma (IDH-wildtype/-NOS/-NEC) patients from 22 geographically distinct institutions across 3 continents, with preoperative structural MRI scans. The entire tumor burden was automatically segmented into 3 sub-compartments, i.e., enhancing, necrotic, peritumoral T2-FLAIR abnormality. We developed our AI predictor by multivariate integration of i)patient age, ii)tumor sub-compartment volume normalized to brain volume, iii)spatial distribution characteristics (tumor location, distance to the ventricles, and laterality), and iv)morphologic descriptors (major axes’ length, axes’ ratio, extent, and number of tumors). The AI predictor returns a continuous value between 0-1, defining short-, intermediate-, and long-survivors based on thresholds on the 25th and 75th percentiles. Leave-One-Site-Out-Cross-Validation was used to assess the generalizability of our stratification. Kaplan-Meier survival curves were computed for OS analysis and evaluated by a Cox proportional hazards model for statistical significance and hazard ratios.

RESULTS

Survival analysis yielded a hazard ratio of 2.07 (95%CI, 2.06-2.08, p-value= 4.8e-102) for patient stratification into short-, intermediate-, and long-survivors. Pearson correlation between the predicted and actual OS yielded an R= 0.49.

CONCLUSION

Multivariate integration of visually quantified tumor characteristics, agnostic to acquisition protocol/equipment, yields an accurate OS surrogate index. Validation of our AI model in the largest centralized glioblastoma imaging dataset, from the ReSPOND consortium, supports its generalizability across diverse patient populations and acquisition settings, potentially contributing to equitable improvements of personalized patient care.

FDG PET/CT in abdominal aortic graft infection: A case report and literature review

This case report follows a 47-year-old man who had multiple grafts undergoing FDG PET/CT (positron emission tomography/computed tomography) scan to evaluate for graft infection. Initial CT showed enhancing soft tissue and fluid collection around the graft, and the subsequent FDG PET/CT showed findings concerning for graft infection. This case exemplifies that FDG PET/CT is a synergistic tool in diagnosing aortic graft infections, a rare and often fatal complication of aortic grafts.

Upfront Biology-Guided Therapy in Diffuse Intrinsic Pontine Glioma: Therapeutic, Molecular, and Biomarker Outcomes from PNOC003

PURPOSE

PNOC003 is a multicenter precision medicine trial for children and young adults with newly diagnosed diffuse intrinsic pontine glioma (DIPG).

PATIENTS AND METHODS

Patients (3-25 years) were enrolled on the basis of imaging consistent with DIPG. Biopsy tissue was collected for whole-exome and mRNA sequencing. After radiotherapy (RT), patients were assigned up to four FDA-approved drugs based on molecular tumor board recommendations. H3K27M-mutant circulating tumor DNA (ctDNA) was longitudinally measured. Tumor tissue and matched primary cell lines were characterized using whole-genome sequencing and DNA methylation profiling. When applicable, results were verified in an independent cohort from the Children's Brain Tumor Network (CBTN).

RESULTS

Of 38 patients enrolled, 28 patients (median 6 years, 10 females) were reviewed by the molecular tumor board. Of those, 19 followed treatment recommendations. Median overall survival (OS) was 13.1 months [95% confidence interval (CI), 11.2-18.4] with no difference between patients who followed recommendations and those who did not. H3K27M-mutant ctDNA was detected at baseline in 60% of cases tested and associated with response to RT and survival. Eleven cell lines were established, showing 100% fidelity of key somatic driver gene alterations in the primary tumor. In H3K27-altered DIPGs, TP53 mutations were associated with worse OS (TP53mut 11.1 mo; 95% CI, 8.7-14; TP53wt 13.3 mo; 95% CI, 11.8-NA; P = 3.4e-2), genome instability (P = 3.1e-3), and RT resistance (P = 6.4e-4). The CBTN cohort confirmed an association between TP53 mutation status, genome instability, and clinical outcome.

CONCLUSIONS

Upfront treatment-naïve biopsy provides insight into clinically relevant molecular alterations and prognostic biomarkers for H3K27-altered DIPGs.

LOCL-06 SUPERVISED MACHINE LEARNING IDENTIFIES RISK FACTORS ASSOCIATED WITH LEPTOMENINGEAL DISEASE AFTER SURGICAL RESECTION OF BRAIN METASTASES

BACKGROUND

Resection of brain metastases (BMs) can help with local disease control, yet predictors of leptomeningeal disease (LMD) after surgery are not well defined. This study examined rates and predictors of LMD in patients who underwent resection of a BM.

METHODS

A retrospective, single-center study was conducted examining LMD risk for adult patients with a BM that underwent resection with postoperative adjuvant radiation. Logistic regression analyses and a supervised machine learning algorithm (Random forest) were implemented to identify factors within the cohort that were associated with LMD.

RESULTS

Of the 182 patients in the cohort, 43 patients (23.6%) developed LMD in the postoperative setting with 18 cases of classical LMD (9.9%) and 25 cases of nodular LMD (13.7%). Median censored time to LMD was not reached, and 6-, 12-, and 24-month LMD-free rates from surgery were 93%, 86.3%, and 71.8%, respectively. Median time from surgery to classical and nodular LMD were 13.1 and 9.5 months, respectively (Log-rank p=0.71). Patients diagnosed with classical LMD had worse survival outcomes from LMD diagnosis compared to nodular LMD (2.6 vs 9.7 mo, Log-rank p=0.02), and LMD-subtype was significantly associated with overall survival from the date of surgery (classical vs nodular vs none: 16.1 vs 20 vs 36.7 mo, p <.0001). Random forest analysis identified primary cancer type, absence of extracranial disease, and tumor volume as the top 3 factors associated with LMD. On multivariate regression analysis, absence of extracranial disease at index surgery was associated with any LMD (OR 2.65, 95% CI 1.15-6.10, p=0.02). Treatment with postoperative checkpoint inhibitors, type of radiation, and performing additional craniotomies were not associated with risk of LMD.

CONCLUSIONS

Classical-type LMD is associated with worse prognosis compared to nodular-type LMD. Absence of extracranial disease at the time of surgery was the most consistent factor associated with LMD on follow-up.

Spectroscopic imaging of D-2-hydroxyglutarate and other metabolites in pre-surgical patients with IDH-mutant lower-grade gliomas

Purpose

Prognostically favorable IDH-mutant gliomas are known to produce oncometabolite D-2-hydroxyglutarate (2HG). In this study, we investigated metabolite-based features of patients with grade 2 and 3 glioma using 2HG-specific in vivo MR spectroscopy, to determine their relationship with image-guided tissue pathology and predictive role in progression-free survival (PFS).

Methods

Forty-five patients received pre-operative MRIs that included 3-D spectroscopy optimized for 2HG detection. Spectral data were reconstructed and quantified to compare metabolite levels according to molecular pathology (IDH1R132H, 1p/19q, and p53); glioma grade; histological subtype; and T2 lesion versus normal-appearing white matter (NAWM) ROIs. Levels of 2HG were correlated with other metabolites and pathological parameters (cellularity, MIB-1) from image-guided tissue samples using Pearson’s correlation test. Metabolites predictive of PFS were evaluated with Cox proportional hazards models.

Results

Quantifiable levels of 2HG in 39/42 (93%) IDH+ and 1/3 (33%) IDH– patients indicated a 91.1% apparent detection accuracy. Myo-inositol/total choline (tCho) showed reduced values in astrocytic (1p/19q-wildtype), p53-mutant, and grade 3 (vs. 2) IDH-mutant gliomas (p < 0.05), all of which exhibited higher proportions of astrocytomas. Compared to NAWM, T2 lesions displayed elevated 2HG+ γ-aminobutyric acid (GABA)/total creatine (tCr) (p < 0.001); reduced glutamate/tCr (p < 0.001); increased myo-inositol/tCr (p < 0.001); and higher tCho/tCr (p < 0.001). Levels of 2HG at sampled tissue locations were significantly associated with tCho (R = 0.62; p = 0.002), total NAA (R = − 0.61; p = 0.002) and cellularity (R = 0.37; p = 0.04) but not MIB-1. Increasing levels of 2HG/tCr (p = 0.0007, HR 5.594) and thresholding (≥ 0.905, median value; p = 0.02) predicted adverse PFS.

Conclusion

In vivo 2HG detection can reasonably be achieved on clinical scanners and increased levels may signal adverse PFS.

DIPG-31. Prognostic and predictive biomarkers of response in children and young adults with H3K27M-altered diffuse intrinsic pontine glioma: results from a multi-center, interventional clinical trial (PNOC003)

BACKGROUND: Diffuse intrinsic pontine glioma (DIPG) is a fatal brain tumor. Herein, we report on novel prognostic and predictive genomic biomarkers identified in PNOC003, a multi-center precision medicine trial for children and young adults diagnosed with DIPG. METHODS: Patients aged 3-25 years were enrolled on PNOC003 based on radiographic diagnosis of DIPG. Pre-treatment tumor biopsies were analyzed using tumor-normal whole-exome sequencing and mRNA-tumor sequencing to determine biology-informed, multi-agent therapy following radiation therapy (RT). Whole-genome sequencing was performed as an exploratory study aim. Genomic biomarkers were investigated to identify predictors of RT response and overall survival (OS) in patients with confirmed H3K27M-altered DIPG. Prognostic biomarkers were verified in a retrospective, H3K27M-altered diffuse midline glioma cohort (n=22) from the Children’s Brain Tumor Network (CBTN). RESULTS: Thirty patients enrolled on PNOC003 met molecular criteria for H3K27M-altered DIPG. TP53 was the most frequently altered driver gene (73%). Somatic alterations in PTEN&gt;TP53&gt;PDGFRA were independently associated with OS (P&lt;0.05, in order of negative impact on survival). TP53 mutations associated with worse OS (TP53mut 11.1 mo [95% CI 8.7, 14]; TP53wt 13.3 mo [95% CI 11.8, NA]; P=3e-2), chromosomal instability (P=3e-3), and resistance to RT (P=6e-4). Moreover, loss of chromosome 10q, encoding tumor suppressor PTEN, was associated with worse OS, co-occurred with PTEN alterations, biallelic PTEN inactivation and loss of gene expression. The combination of TP53 alterations and loss of 10q/PTEN in H3K27M-altered DIPG was associated with the worst OS in a combined PNOC003 and CBTN cohort (TP53mut/10qdel, n=14, OS 8.4 mo [95% CI 7.4, 15.8]; TP53mut/10qwt, n=20, OS 13.1 mo [95% CI 10.1, 17.2]; TP53wt/10qwt, n=14, OS 15.5 mo [11.8, 29.4]; P=2e-3). CONCLUSION: PNOC003, a tissue-driven clinical trial, provided insights into prognostic and predictive genomic biomarkers and informed a novel molecular tumor classification system for H3K27M-altered DIPGs.

A targeted gene expression biomarker and association with meningioma outcomes and radiotherapy

2007

Background: Improvements in risk stratification of meningioma are needed to guide post-operative management and application of adjuvant therapy. Although profiling of DNA methylation, copy number variants (CNVs), RNA sequencing, and exome sequencing have better elucidated meningioma biology, these approaches have not revealed clinically tractable biomarkers for radiotherapy responses. In this study, we develop and validate a targeted gene expression biomarker to predict meningioma outcomes and benefit from radiotherapy. Methods: Targeted gene expression profiling was performed on a development set of 173 meningiomas (median follow-up 8.1 years) and a validation set of 331 consecutive meningiomas (median follow-up 6.1 years) treated at independent institutions (70% WHO grade 1, 24% WHO grade 2, 6% WHO grade 3). All patients underwent surgery (n = 504) with or without postoperative radiotherapy (n = 73 with radiation). Regularized Cox regression within the development set resulted in a continuous gene expression risk score for local freedom from recurrence (LFFR). The model (34 genes and 7 housekeeping genes) and thresholds for low, intermediate, and high-risk scores were locked and applied to the validation set. Results: The gene expression risk score outperformed WHO grade (validation 5-year LFFR delta-AUC 0.15, 95% CI 0.076-0.229, p = 0.001) and DNA methylation grouping (delta-AUC 0.075, 95% CI 0.006-0.130, p = 0.01) for LFFR, disease-specific survival, and OS, achieving a negative predictive value for recurrence at 5-years of 93.2%. The biomarker reclassified 35.8% of WHO grade 1 tumors as intermediate or high risk (5-year LFFR/OS 62%/79%), and 18.3% of WHO grade 2-3 tumors as low risk (5-year LFFR/OS 78%/100%). The biomarker was independently prognostic after accounting for WHO grade, extent of resection, primary versus recurrent presentation, CNV status, DNA methylation group, and Ki67 labeling index, and was predictive for LFFR after postoperative radiotherapy, with a hazard ratio of 0.41 for intermediate to high risk propensity-matched meningiomas (95% CI 0.2-0.9, p = 0.0002) versus 0.79 for low risk meningiomas (95% CI 0.1-8.8, p = 0.5182). Conclusions: Targeted gene expression profiling of 504 meningiomas resulted in a biomarker which improved discrimination of meningioma local recurrence, disease-specific survival, and overall survival, and also predicted benefit from radiotherapy.

Rapid early progression (REP) of glioblastoma is an independent negative prognostic factor: Results from a systematic review and meta-analysis

Background

In patients with newly diagnosed glioblastoma, rapid early progression (REP) refers to tumor regrowth between surgery and postoperative chemoradiotherapy. This systematic review and meta-analysis appraised previously published data on REP to better characterize and understand it.

Methods

Systematic searches of MEDLINE, EMBASE and the Cochrane database from inception to October 21, 2021. Studies describing the incidence of REP-tumor growth between the postoperative MRI scan and pre-radiotherapy MRI scan in newly diagnosed glioblastoma were included. The primary outcome was REP incidence.

Results

From 1590 search results, 9 studies were included with 716 patients. The median age was 56.9 years (IQR 54.0-58.8 y). There was a male predominance with a median male-to-female ratio of 1.4 (IQR 1.1-1.5). The median number of days between MRI scans was 34 days (IQR 18-45 days). The mean incidence rate of REP was 45.9% (range 19.3%-72.0%) and significantly lower in studies employing functional imaging to define REP (P < .001). REP/non-REP groups were comparable with respect to age (P = .99), gender (P = .33) and time between scans (P = .81). REP was associated with shortened overall survival (HR 1.78, 95% CI 1.30-2.43, P < .001), shortened progression-free survival (HR 1.78, 95% CI 1.30-2.43, P < .001), subtotal resection (OR 6.96, 95% CI 4.51-10.73, P < .001) and IDH wild-type versus mutant tumors (OR 0.20, 95% CI 0.02-0.38, P = .03). MGMT promoter methylation was not associated with REP (OR 1.29, 95% CI 0.72-2.28, P = .39).

Conclusions

REP occurs in almost half of patients with newly diagnosed glioblastoma and has a strongly negative prognostic effect. Future studies should investigate its biology and effective treatment strategies.

NIMG-26. IMPROVING THE GENERALIZABILITY OF DEEP LEARNING FOR T2-LESION SEGMENTATION OF GLIOMAS IN THE POST-TREATMENT SETTING

Although current advances for automated glioma lesion segmentation and volumetric measurements using deep learning have yielded high performance on newly-diagnosed patients, response assessment in neuro-oncology still relies on manually-drawn, cross-sectional areas of the tumor because these models do not generalize to patients in the post-treatment setting, where they are most needed in the clinic. Surgical resections, adjuvant treatment, or disease progression can alter the characteristics of these lesions on T2-weighted imaging, causing measures of segmentation accuracy, typically measured by Dice coefficients of overlap (DCs), to drop by ~15%. To improve the generalizability of T2-lesion segmentation to patients with glioma post-treatment, we evaluated the effects of: 1) training with different proportions of newly-diagnosed and treated gliomas, 2) applying transfer learning from pre- to post-treatment domains, and 3) incorporating a loss term that spatially weights the lesion boundaries with greater emphasis in training. Using 425 patients (208 newly-diagnosed, 217 post-Tx, with 25 treated patients withheld as a test set) and a top-performing model previously trained on newly-diagnosed gliomas, we found that DCs increased by 10% (to 0.84) then plateaued after including ~25% of post-treatment patients in training. Transfer learning (pre-training on newly-diagnosed and finetuning with post-treatment data) significantly improved Hausdorf distances (HDs), a measure more sensitive to changes at the lesion boundaries, by 17% after including 26% post-treatment images in training, while DCs remained similar. Although modifying our loss functions with boundary-weighted penalizations resulted in comparable DCs to using standard DC loss, HD measures were further reduced by 26%, suggesting that HDs may be a more sensitive metric to subtle changes in segmentation accuracy than DCs. Current work is evaluating their utility in providing accurate volumes for real-time response assessment in the clinic using workflows that have recently been deployed on our clinical PACs system.

BIOM-40. TARGETED GENE EXPRESSION PROFILING PREDICTS MENINGIOMA OUTCOMES AND RADIOTHERAPY RESPONSES

BACKGROUND

Surgery is the mainstay of meningioma treatment, but improvements in meningioma risk stratification are needed and indications for postoperative radiotherapy are controversial. DNA methylation profiling, copy number variants (CNVs), exome sequencing, and RNA sequencing have improved understanding of meningioma biology, but have not superseded histologic grading, or revealed biomarkers for radiotherapy responses. To address these unmet needs, we optimized and validated a targeted gene expression biomarker predicting meningioma outcomes and responses to radiotherapy.

METHODS

Targeted gene expression profiling was performed on a discovery cohort of 173 meningiomas (median follow-up 8.1 years) and a validation cohort of 331 meningiomas (median follow-up 6.1 years) treated with surgery (n=504) and postoperative radiotherapy (n=73) at independent, international institutions (70% WHO grade 1, 24% WHO grade 2, 6% WHO grade 3). Optimized targeted gene expression models predicting clinical outcomes (34 genes) or radiotherapy responses (12 genes) were developed from the discovery cohort, and compared to histologic and molecular classification systems by performing DNA methylation profiling, CNV analysis, exome sequencing, and RNA sequencing on the same meningiomas.

RESULTS

Targeted gene expression profiling achieved a concordance-index of 0.75 ± 0.03 (SEM) for local freedom from recurrence (LFFR) and 0.72 ± 0.03 for overall survival (OS) in the validation cohort, outperforming WHO grade (5-year LFFR delta-AUC 0.15, 95% CI 0.076-0.229, p=0.001) and DNA methylation grouping (delta-AUC 0.075, 95% CI 0.006-0.130, p=0.01) for LFFR, disease-specific survival, and OS. The biomarker was independently prognostic after accounting for WHO grade, extent of resection, primary versus recurrent presentation, CNV status, DNA methylation group, and Ki67 labeling index, and identified meningiomas benefiting from radiotherapy (interaction p-value=0.0008), suggesting postoperative radiotherapy could be refined in 30.2% of cases.

CONCLUSIONS

Targeted gene expression profiling of 504 meningiomas improves discrimination of meningioma local recurrence, disease-specific survival, and overall survival, and predicts radiotherapy responses.

TWMP-01. RESECTION OF SUPPLEMENTARY MOTOR AREA GLIOMAS: REVISITING SUPPLEMENTARY MOTOR SYNDROME AND THE ROLE OF THE FRONTAL ASLANT TRACT

INTRODUCTION

The supplemental motor area (SMA) is an eloquent region that is frequently a site for gliomas or the region is included in the resection trajectory to deeper lesions.

METHODS

Patient, tumor and outcome data were collected retrospectively from the UCSF tumor registry for patients who underwent surgical resection for newly diagnosed supratentorial diffuse glioma (WHO Grade II - IV) between 2010 and 2019 in the SMA region and the extent of SMA resection was determined by volumetric assessment. Tumors were registered to a standard brain atlas to create a frequency heat map of tumor volumes and resection cavities.

RESULTS

Although the volume of tumor within the SMA region did not correlate with the development of SMA syndrome, patients with SMA syndrome had larger resection cavities in the SMA region (25.4% SMA resection vs. 14.2% SMA resection, p = 0.039). The size of the resection cavity in the SMA region did not correlate with the severity of the SMA syndrome. Patients who developed SMA syndrome had cavities that were located more posteriorly in the SMA region and in the cingulate. When the frontal aslant tract (FAT) was preserved, 50% of patients developed SMA syndrome post-operatively; whereas 100% of patients who had disruption of the FAT during surgery developed SMA syndrome (p = 0.06). There was no difference in the overall survival for newly diagnosed glioblastoma patients with SMA syndrome compared to those without SMA syndrome (1.6 years vs. 3.0 years, p = 0.33).

CONCLUSION

For patients with SMA gliomas, larger resections and resections involving the posterior SMA region and posterior cingulate gyrus increased the likelihood of a post-operative SMA syndrome. Although SMA syndrome occurred in all cases where the FAT was resected, FAT preservation does not reliably avoid SMA syndrome post-operatively.

NIMG-43. ADVANCED MULTI-PARAMETRIC HYPERPOLARIZED 13C/1H IMAGING OF GBM

INTRODUCTION

The goal of this study was to characterize progressive and pseudoprogressive GBM using multi-parametric hyperpolarized (HP)-13C / 1H MRI.

METHODS

Dynamic HP-13C MRI was acquired from 13 patients with progressive GBM [patients (scans): 2(3) IDH-mutant; 11(13) IDH-wildtype] and 2 IDH-wildtype patients (3 scans) demonstrating pseudo-progression following intravenous injection of HP [1-13C]pyruvate. Frequency-selective echo-planar imaging (3s temporal resolution, 3.38 cm3 spatial resolution) captured [1-13C]pyruvate metabolism to [1-13C]lactate and 13C-bicarbonate in the brain. Dynamic 13C data were kinetically modeled to obtain the pyruvate-to-lactate conversion rate constant k PL and temporally summed to calculate 13C-metabolite percentiles and ratios (linearly interpolated 2x in-plane). 1H imaging included T2, post-Gd T1, perfusion (nCBV, %recovery), diffusion (ADC), and lactate-edited spectroscopy (CNI, choline-to-NAA index; 1H-lactate). The normal-appearing white matter (NAWM), non-enhancing lesion (NEL), and contrast-enhancing lesion (CEL) were segmented from 1H images. 13C-resolution masks were iteratively applied on a voxel-wise basis to evaluate 1H imaging parameters within each ROI and multi-parametric data were collectively evaluated using a mixed effects model in R.

RESULTS

Progressive IDH-mutant GBM compared to wildtype counterparts displayed increased perfusion %recovery (p &lt; 0.001) and k PL (p &lt; 0.01), together with reduced 1H-lactate (p &lt; 0.001) and pyruvate percentile (p &lt; 0.01), in the T2 lesion. Among IDH-wildtype progressive GBM, the CEL was distinguished from NEL/NAWM by increased nCBV (p &lt; 0.05/0.001), 1H-lactate (p &lt; 0.05/0.001); and decreased bicarbonate / lactate (p &lt; 0.05/0.001). The CEL and NEL were collectively distinguished from NAWM by elevated CNI (p &lt; 0.001/0.001), ADC (p &lt; 0.05/0.001), pyruvate percentile (p &lt; 0.001/0.001), lactate percentile (p &lt; 0.001/0.001), and relative lactate / pyruvate (p &lt; 0.001/0.05). Psuedo-progressive IDH-wildtype GBM displayed lower k PL (T2 Lesion; p &lt; 0.01) and nCBV (CEL; p &lt; 0.01) compared to progressive GBM.

CONCLUSION

HP-13C parameters can potentially augment proton imaging and demonstrated Warburg-associated metabolic alterations.

NIMG-25. IMPROVING THE NONINVASIVE CLASSIFICATION OF GLIOMA GENETIC SUBTYPE WITH DEEP LEARNING AND DIFFUSION-WEIGHTED IMAGING

In contrast to the WHO 2016 guidelines that use genetic alterations to further stratify patients within a designated grade, new recommendations suggest that IDH mutation status, followed by 1p19q-codeletion, should be used before grade when differentiating gliomas. Although most gliomas will be resected and their tissue evaluated with genetic profiling, non-invasive characterization of genetic subgroup can benefit patients where surgery is not otherwise advised or a fast turn-around is required for clinical trial eligibility. Prior studies have demonstrated the utility of using anatomical images and deep learning to distinguish either IDH-mutant from IDH-wildtype tumors or 1p19q-codeleted from non-codeleted lesions separately, but not combined or using the most recent recommendations for stratification. The goal of this study was to evaluate the effects of training strategy and incorporation of Apparent Diffusion Coefficient (ADC) maps from diffusion-weighted imaging on predicting new genetic subgroups with deep learning. Using 414 patients with newly-diagnosed glioma (split 285/50/49 training/validation/test) and optimized training hyperparameters, we found that a 3-class approach with T1-post-contrast, T2-FLAIR, and ADC maps as inputs achieved the best performance for molecular subgroup classification, with overall accuracies of 86.0%[CI:0.839,1.0], 80.0%[CI:0.720,1.0], and 85.7%[CI:0.771,1.0] on training, validation, and test sets, respectively, and final test class accuracies of 95.2%(IDH-wildtype), 88.9%(IDH-mutated,1p19qintact), and 60%(IDHmutated,1p19q-codeleted). Creating an RGB-color image from 3 MRI images and applying transfer learning with a residual network architecture pretrained on ImageNet resulted in an 8% averaged increase in overall accuracy. Although classifying both IDH and 1p19q mutations together was overall advantageous compared with a tiered structure that first classified IDH mutational status, the 2-tiered approach better generalized to an independent multi-site dataset when only anatomical images were used. Including biologically relevant ADC images improved model generalization to our test set regardless of modeling approach, highlighting the utility of incorporating diffusion-weighted imaging in future multi-site analyses of molecular subgroup.

NIMG-21. VARIABLE RESOLUTION HYPERPOLARIZED [2-13C]PYRUVATE MRI IN HEALTHY VOLUNTEERS AND PATIENTS WITH IDH-MUTANT GLIOMA

INTRODUCTION

Mutations in isocitrate dehydrogenase (IDH) have been investigated as a prognostic biomarker in glioma. The presence of the IDH mutation (IDHm) is associated with 2-hydroxyglutarate (2HG) production and inhibition of glutamate synthesis (McBrayer, Cell 2018). Hyperpolarized carbon-13 (HP-13C) MRI enables dynamic measurements of in-vivo metabolism using a [2-13C]pyruvate labeled probe that undergoes conversion to [2-13C]lactate and [5-13C]glutamate. Here, we present HP [2-13C]pyruvate data from healthy volunteers and patients with IDHm diffuse glioma. Due to its intrinsic low signal-to-noise ratio (SNR), we demonstrate the ability of post-processing denoising to improve its utility and aid in detection of metabolic changes associated with IDHm.

METHODS

Dynamic HP 13C data were acquired following intravenous injection of [2-13C]pyruvate from five healthy volunteers and one patient with IDHm grade III astrocytoma. A novel multi-resolution frequency specific multislice EPI sequence was used to obtain [2-13C]pyruvate, [5-13C]glutamate, and downfield and upfield [2-13C]lactate signals (3s temporal resolution, pyruvate/lactate/glutamate spatial resolutions = 0.75x0.75cm2/ 2.25x2.25cm2/ 2.25x2.25cm2, 5 slices 3cm thick). Following phase correction, patch-based tensor decomposition denoising was applied to metabolite images. Metabolite differences between normal-appearing white matter (NAWM) and T2 lesion were examined for the patient data.

RESULTS

HP [2-13C]pyruvate imaging is able to simultaneously probe glycolytic ([2-13C]lactate) and oxidative ([5-13C]glutamate) metabolism. Denoised pyruvate/lactate/glutamate signals achieved a 4-9/3-6/3-7 fold increase in SNR. T2 lesion exhibited decreased glutamate-to-pyruvate and glutamate-to-lactate AUC ratios versus contralateral NAWM (p&lt; 0.018, p &lt; 1.5e-5), consistent with IDH mutant status.

CONCLUSION

We successfully demonstrated the feasibility of applying variable resolution HP [2-13C]pyruvate metabolic imaging to detect IDHm specific metabolism. This technique addresses a major hurdle in HP 13C MRI by improving SNR while permitting robust metabolism quantification. Future studies will optimize methods for acquiring and processing data to evaluate further data acquired from IDHm glioma patients. Supported by NIH T32 CA151022, P01 CA118816, and NICO.

PATH-36. INTRATUMOR HETEROGENEITY AND BIOINFORMATIC DIFFERENCES INFLUENCE MENINGIOMA MOLECULAR CLASSIFICATION

Molecular alterations such as CDKN2A inactivation and TERT promoter mutation are new criteria for grade 3 meningiomas in the 5th edition of the WHO Classification of Tumors of the Central Nervous System. However, consensus approaches to identify copy number variants (CNVs) and short somatic variants in meningiomas are lacking. Here, we performed integrated DNA methylation profiling, RNA-sequencing, and targeted DNA mutational profiling on 10 stereotactically-collected, regionally-distinct samples from 4 meningiomas. Targeted DNA sequencing revealed numerous private short somatic variants from multiple sites within individual meningiomas, including a TERT promoter mutation in only 1 of 2 samples from the same tumor. DNA methylation profiling revealed differences in biologic groups and immune cell enrichment between regionally-distinct samples within individual meningiomas. CNV status was evaluated using DNA methylation profiling and RNA sequencing on 14 stereotactically-collected, regionally-distinct samples from 2 meningiomas. Phylogenetic architectures from DNA methylation profiling and targeted DNA sequencing were highly concordant and shared 99.12% of CNVs while RNA sequencing identified only 39% of the CNVs called from DNA based approaches. Finally, CNV analysis based on single-cell RNA sequencing revealed partially overlapping CNVs across meningioma cells within an individual tumor, suggesting subclonal populations may influence CNV-based meningioma molecular classification and underlie limitations in defining CNVs from bulk RNA-sequencing. In sum, these data highlight the relative strengths and weaknesses of various approaches for molecular analysis of meningiomas complicated by intratumor heterogeneity due to non-tumor cells and subclonal populations of meningioma cells. Future efforts to incorporate molecular analysis into the diagnostic paradigm for meningiomas may require orthogonal validation across multiple platforms or image-guided meningioma sampling to select the most aggressive regions for molecular profiling.

NIMG-14. RESTING STATE EXECUTIVE CONTROL AND SALIENCE NETWORK CONNECTIVITY IN CLINICALLY STABLE LOWER GRADE GLIOMA COVARIES WITH COGNITIVE PERFORMANCE

BACKGROUND

Survival outcomes for patients with lower grade gliomas (LrGG) are improving. However, injury from tumor growth and consequences of treatment often leads to impaired cognition, particularly in cognitive domains reliant on distributed functional networks and intact white-matter tracts. Resting state functional MRI (rsfMRI) is a method of investigating the integrity of these functional networks.

METHODS

This study investigated rsfMRI connectivity in 21 patients with clinically stable LrGG compared to age- and gender-matched healthy controls, and associated imaging measures with cognitive outcomes. Data were acquired for 12 cognitive tests administered within one week of imaging. RsfMRI and T1-weighted images for 21 research controls were acquired from OpenNeuro datasets. RsfMRI data were processed and analyzed using the CONN toolbox using CONN’s standard regions of interest (ROI) for the 8 canonical networks as seeds, and cognitive test scores as covariates, with a threshold for T tests of p&lt; .001 uncorrected.

RESULTS

Median age was 48 years old (range 27-67). There were 6 astrocytomas, IDHmut; 3 astrocytomas IDH-wt, 8 oligodendrogliomas, and 4 NOS. Thirteen had left hemisphere tumors (8 frontal, 3 parietal, 2 temporal), and 6 right (5 frontal, 1 temporal). Fourteen had previously recieved radiotherapy. There was significantly lower connectivity in frontoparietal executive control and the salience networks in LrGG patients versus controls. Within patients, lower executive control network connectivity covaried with worse performance on executive measures (FAS, Tower of London, Trails-A, Animal Naming, FrSBe), and attention and working memory measures (Digit Symbol, HVLT). Lower salience network connectivity covaried with poorer performance on executive measures (FrSBe, FAS) and attention and working memory measures (Digit Span, HVLT, WAIS-WM).

CONCLUSION

In clinically stable LrGG, rsfMRI measures of network connectivity are potentially useful markers to monitor and track, given the concordance with cognition, and could help guide cognitive assessment and rehabilitation.

Direct Spread of Primary Testicular Lymphoma along the Gonadal Vessels Detected on F-18 Fluorodeoxyglucose Positron-Emission Tomography/Computed Tomography Imaging

A 63-year-old male presented with left scrotal swelling and the ultrasound showed a large heterogeneous mass consistent with a testicular malignancy. The patient underwent left-sided orchiectomy which showed diffuse large B-cell lymphoma. The patient was then referred for whole-body F-18 fluorodeoxyglucose positron-emission tomography/computed tomography (FDG PET/CT) imaging which showed multiple hypermetabolic foci extending along the left inguinal canal to the retroperitoneum and the left perinephric space, suggesting direct contiguous spread of the tumor along the gonadal vessels, a form of metastasis unique to primary testicular lymphoma, and demonstrated for the first time on FDG PET/CT imaging.

Denoising of hyperpolarized 13 C MR images of the human brain using patch-based higher-order singular value decomposition

PURPOSE

To improve hyperpolarized ¹³ C (HP-¹³ C) MRI by image denoising with a new approach, patch-based higher-order singular value decomposition (HOSVD).

METHODS

The benefit of using a patch-based HOSVD method to denoise dynamic HP-¹³ C MR imaging data was investigated. Image quality and the accuracy of quantitative analyses following denoising were evaluated first using simulated data of [1-¹³ C]pyruvate and its metabolic product, [1-¹³ C]lactate, and compared the results to a global HOSVD method. The patch-based HOSVD method was then applied to healthy volunteer HP [1-¹³ C]pyruvate EPI studies. Voxel-wise kinetic modeling was performed on both non-denoised and denoised data to compare the number of voxels quantifiable based on SNR criteria and fitting error.

RESULTS

Simulation results demonstrated an 8-fold increase in the calculated SNR of [1-¹³ C]pyruvate and [1-¹³ C]lactate with the patch-based HOSVD denoising. The voxel-wise quantification of k_PL (pyruvate-to-lactate conversion rate) showed a 9-fold decrease in standard errors for the fitted k_PL after denoising. The patch-based denoising performed superior to the global denoising in recovering k_PL information. In volunteer data sets, [1-¹³ C]lactate and [¹³ C]bicarbonate signals became distinguishable from noise across captured time points with over a 5-fold apparent SNR gain. This resulted in >3-fold increase in the number of voxels quantifiable for mapping k_PB (pyruvate-to-bicarbonate conversion rate) and whole brain coverage for mapping k_PL .

CONCLUSIONS

Sensitivity enhancement provided by this denoising significantly improved quantification of metabolite dynamics and could benefit future studies by improving image quality, enabling higher spatial resolution, and facilitating the extraction of metabolic information for clinical research.

Pilot trial treating recurrent GBM patients with precision medicine regimens

2045

Background: Recurrence of GBM after initial treatment with surgery, radiation, and chemotherapy is nearly universal. Salvage therapies have limited efficacy with median overall survival (OS) of approximately 9 months and 6-month-progression-free survival (PFS-6) of 10-25% for both targeted and traditional therapies. Given GBM’s molecular heterogeneity, targeting a single molecular abnormality in isolation has consistently failed as a strategy, and precision combination approaches are needed. Methods: The primary objective was to demonstrate the feasibility of implementing a personalized drug regimen for patients (pts) with surgically resectable recurrent GBM within 35 days of surgery. Secondary objectives included safety and efficacy. Eligible pts signed consent before surgery, and tumor tissue was analyzed using the CLIA-approved “UCSF500” next-generation sequencing panel with paired tumor/germline sequencing. A specialized genomic tumor board made individualized treatment recommendations incorporating sequencing results of the recurrent tumor and clinical history for each pt, using up to 4 FDA-approved drugs in combination (all drugs provided by study). Correlative studies will be reported separately. Results: 19 pts signed consent and 16 pts had surgery on trial, 1 with pathology showing treatment effect only. The remaining 15 pts were all genetically profiled and successfully started their individualized treatment within 35 days of surgery, meeting the primary feasibility endpoint. Conclusions: Implementation of an individualized treatment regimen was feasible in a timely fashion in surgically resectable recurrent GBM pts, with encouraging preliminary efficacy results. Further investigation is warranted, both to validate efficacy and to streamline this approach in larger pt populations. Clinical trial information: NCT03681028. [Table: see text]

HRCT Diagnosis of Pleuroparenchymal fibroelastosis: Report of two cases

Pleuroparenchymal fibroelastosis (PPFE) is a rare idiopathic interstitial pneumonia that is often underdiagnosed on computed tomography scans. The disease process involves a combination of fibrosis involving the visceral pleura and fibroelastic changes within the subpleural lung parenchyma. Although definitive diagnosis is based on pathological evaluation, this is often not feasible and pattern recognition on CT as "definite PPFE" or "consistent with PPFE" is important given that sub group of patients will undergo rapid progression with clinical deterioration.