Citations

AbstractPurpose has often been proposed as a quantitative imaging biomarker for diagnosis, prognosis, and treatment response assessment for various tumors. None of the many software tools for quantification are standardized. The ISMRM Open Science Initiative for Perfusion Imaging–Dynamic Contrast‐Enhanced (OSIPI‐DCE) challenge was designed to benchmark methods to better help the efforts to standardize measurement.MethodsA framework was created to evaluate values produced by DCE‐MRI analysis pipelines to enable benchmarking. The perfusion MRI community was invited to apply their pipelines for quantification in glioblastoma from clinical and synthetic patients. Submissions were required to include the entrants' values, the applied software, and a standard operating procedure. These were evaluated using the proposed score defined with accuracy, repeatability, and reproducibility components.ResultsAcross the 10 received submissions, the score ranged from 28% to 78% with a 59% median. The accuracy, repeatability, and reproducibility scores ranged from 0.54 to 0.92, 0.64 to 0.86, and 0.65 to 1.00, respectively (0–1 = lowest–highest). Manual arterial input function selection markedly affected the reproducibility and showed greater variability in analysis than automated methods. Furthermore, provision of a detailed standard operating procedure was critical for higher reproducibility.ConclusionsThis study reports results from the OSIPI‐DCE challenge and highlights the high inter‐software variability within estimation, providing a framework for ongoing benchmarking against the scores presented. Through this challenge, the participating teams were ranked based on the performance of their software tools in the particular setting of this challenge. In a real‐world clinical setting, many of these tools may perform differently with different benchmarking methodology.

Abstract It has been widely acknowledged that limited access to the human brain, along with the importance of its structures, presents significant challenges in obtaining brain tumor tissue. This poses difficulties for clinical decision-making, as healthcare professionals often have to rely solely on imaging to address complex problems. There is currently no definitive method to make these decisions other than relying on informed yet subjective human judgment. That said, imaging abnormalities are a result of a presence of particular tumor-related biology. Because of this, and the heterogeneity frequently observed in brain tumors, we have developed an image-localized biopsy protocol that has been employed for research purposes at Mayo Clinic Arizona for over five years. This multidisciplinary approach involves the collaborative efforts from neurosurgeons, neuro-oncologists, scrub-techs, nurses, radiologists, as well as multiple research labs, all working within a well-established pipeline. The implementation of this protocol has allowed us to obtain image-localized biopsies during surgical procedures, enabling the correlation of local imaging findings with corresponding tissue features through the utilization of machine learning models. The primary objective of this endeavor is to enhance our understanding of the underlying biological mechanisms of brain tumors based on patients' imaging data. This protocol has been successful for our research purposes, and with minor adjustments, it can be readily adopted by any hospital in order to collect more data about these incredibly heterogeneous diseases. While the use of neuronavigation during surgery has become commonplace in many medical institutions, localization of tissue samples is not. Utilizing pre-existing infrastructure in the operating room is a small burden with big potential positive outcomes. Sample locations can be useful to clinicians during tumor board discussions, to better assess disease heterogeneity. These minor refinements in the surgical workflow have the capacity to provide clinicians with additional clinically-relevant data without disrupting patient care.

Abstract PURPOSE 18F-DOPA is sensitive/specific for identifying areas of biologically aggressive disease for glioblastoma. We report the impact of 18F-DOPA on radiation treatment planning from a phase II study. METHODS Gross target volume (GTV) was defined by the boolean of high-risk PET (tumor/normal brain SUV > 2.0) and MRI T1 contrast enhancement (MRI-CE) including surgical cavity. The clinical target volume (CTV) included the GTV with 1cm margin. Using proton beam therapy, GTV ≤ 65 cc was treated with 25 (CTV), 30 (MRI-CE), and 35 (PET) GyE over 5 fractions with a simultaneous integrated boost. GTV > 65 cc received 30 (CTV), 35 (MRI-CE), and 40 (PET) GyE over 10 fractions. Radiation field design was compared to traditional 1.5cm CTV margin on MRI-CE. RESULTS Between May 2019 and October 2021, 39 patients were treated. The median MRI-CE tumor diameter was 5.2cm. The median PET volume was 7.9cc (range 0–64.9cc). The median MRI-CE volume was 35.1cc (range 8.6–114cc). High-risk PET was contained within: MRI-CE 18/39 (46%), MRI-CE + T2W FLAIR 13/39 (33%), MRI-CE + T2W FLAIR + outside GTV 4/39 (10%), outside GTV 2/39 (5%) and no uptake 2/39 (5%). Utilizing this planning technique, there was a 26% volume reduction in CTV to traditional 1.5 cm margin on MRI-CE. PET imaging in treatment planning allowed inclusion of high-risk disease that would have been excluded on traditional CTV in 23% of cases (range 1.6-33.8cc). PET was more predictive [HR 1.03 (95% CI 1.01, 1.05) p = 0.002] than MRI [HR 1.01 (95% CI 1.00, 1.03) p = 0.02] for death with each cc increasing risk of death by 3.1%. CONCLUSIONS The incorporation of 18F-DOPA in treatment planning yields a more targeted approach with reductions in both normal brain treated and risk of geographic miss compared to traditional CTV margins.

Abstract INTRODUCTION Differentiation of glioblastoma recurrence from post treatment radiation effects (PTRE) would improve patient management. Dynamic susceptibility contrast (DSC) MRI can be used to generate fractional tumor burden (FTB) maps, via application of regional cerebral blood volume (RCBV) thresholds, to spatially differentiate recurrence from PTRE. FTB maps derived from a DSC-MRI protocol that includes a contrast agent preload in addition to the bolus injection and a moderate-flip angle (MFA) been histologically validated. Recently, a single-dose, low-flip angle (LFA) protocol was shown to provide RCBV measurements consistent with the MFA protocol and has been recommended as a consensus protocol. This study aims to identify the RCBV thresholds for the single-dose, LFA protocol that generates FTB maps, concordant with those obtained from the reference double-dose, MFA protocol. METHODS Two sets of DSC-MRI data were collected sequentially from 52 patients using a single-dose, low-flip angle (30o) protocol and a double-dose, moderate flip angle (60o) protocol using TR = 1.5s and TE = 30ms. Standardized RCBV maps (sRCBV) were obtained and co-registered with the post-contrast T1-weighted images using the FDA-approved clinical software, IB Neuro™ and IB Delta Suite™, respectively. The ground truth MFA-based FTB maps were computed using multiple sRCBV thresholds (1.0 and 1.75). An ROC analysis was conducted to identify the optimal, voxel-wise LFA sRCBV thresholds to produce FTB maps that best match the reference maps. RESULTS/ CONCLUSIONS The ROC analysis identified a lower LFA threshold of 1.0 to accurately distinguish PTRE from tumor recurrence and an upper threshold of 1.55 to differentiate aggressive tumor regions. The single-dose, LFA protocol eliminates variations in the contrast agent incubation time between the preload and bolus injection. This study provides motivation for the use of the single-dose LFA protocol in the management of patients with glioblastoma and is a reliable method for distinguishing tumor from PTRE.

Abstract High-grade glioma continues to have dismal survival owing in part to its intra- and inter-patient heterogeneity. Standard clinical protocol collects tumor samples with the aim of providing or confirming a diagnosis and determining the status of a few key genes (e.g. IDH1, MGMT). However, this protocol is unable to capture the diversity within tumor regions, immune expression or normal cell abundances that play key roles in the development of the disease. To overcome this, during surgery we collect image-localized multi-regional biopsies to characterize this disease heterogeneity. Data collection is ongoing, and we currently have 202 samples from 58 patients with available bulk RNA-Seq. With a single-cell reference dataset from Columbia University, we used CIBERSORTx, a deconvolution method, to predict relative abundances of 7 normal, 6 glioma, and 5 immune cell subpopulations for each sample. We used Cox proportional hazard models with first-order statistics (mean, minimum, maximum) of abundances within patients to determine whether these are prognostic, then used TCGA RNA-Seq data to validate these findings. We found that one glioma proneural subtype was significantly beneficial for patient survival relative to other glioma subtypes across all statistics (and showed significance in TCGA). Proliferative and mesenchymal glioma subtypes also showed significance for one or two of the calculated statistics. Oligodendrocyte progenitor cell abundances were consistently significantly beneficial for patient survival in our cohort, while an increased abundance of abnormal (reactive) astrocytes is associated with poor prognosis. We also ran these analyses within invasive margin and core tumor regions to determine location-specific population abundances associated with patient survival. In conclusion, understanding the in vivo diversity of cellular subpopulations within high grade glioma is important for treatment stratification and patient benefit.

Abstract High-grade gliomas represent the most common type of primary adult malignant brain tumor historically diagnosed and graded from histologic criteria alone. Gliomas harboring isocitrate dehydrogenase (IDH) 1 or 2 mutations are associated with lower grade tumors and portend a favorable prognosis relative to IDH-wildtype glioma. However, molecular profiling has identified additional alterations in lower grade gliomas that confer an aggressive high-grade phenotype and a worse clinical prognosis. Of these, the loss of CDKN2A/B in IDH-mutant astrocytoma is the strongest implicated alteration and is sufficient to define a grade 4 tumor regardless of histologic grade. However, there remain no effective therapies targeted at molecular subgroups in aggressive gliomas to date. Here, we sought to elucidate the biologic pathways and functional outcomes associated with the acquisition of high-grade behavior under loss of CDKN2A/B in IDH-mutant astrocytoma to inform future therapeutic strategies. We analyzed a cohort of patient IDH-mutant astrocytomas with whole exome and RNA sequencing data and found several candidate genes/pathways regulated concurrent with CDKN2A/B loss, including regulators of embryonic development, extracellular matrix organization and regulation of vascular processes. Moreover, we identified 1 tumor that displayed intratumoral heterogeneity of CDKN2A/B expression across biopsies. Using grade 4 IDH-mutant astrocytoma patient-derived (PDX) and established cell-lines harboring CDKN2A/B homozygous deletion, we then stably re-expressed cell cycle regulators p14, p15, and p16 using a Tet-inducible lentiviral system and confirmed their decreased proliferation as compared to parental lines. IDH-mutant cell lines with re-expression of p14, p15 or p16 were characterized by differential transcription factor activation and distinct vascular formation patterns as compared to parental lines. These lines also displayed an increased cell migration and invasive potential and were associated with enhanced integrin signaling through integrin beta-3. Together, our work supports a hypothesis of regional tumor evolution with distinct biologic phenotypes across IDH-mutant gliomas.

Abstract There is a longstanding ambiguity regarding the clinical diagnosis of dementia syndromes predominantly targeting executive functions versus behaviour and personality. This is due to an incomplete understanding of the macro-scale anatomy underlying these symptomatologies, a partial overlap in clinical features and the fact that both phenotypes can emerge from the same pathology and vice versa. We collected data from a patient cohort of which 52 had dysexecutive Alzheimer’s disease, 30 had behavioural variant frontotemporal dementia (bvFTD), seven met clinical criteria for bvFTD but had Alzheimer’s disease pathology (behavioural Alzheimer’s disease) and 28 had amnestic Alzheimer’s disease. We first assessed group-wise differences in clinical and cognitive features and patterns of fluorodeoxyglucose (FDG) PET hypometabolism. We then performed a spectral decomposition of covariance between FDG-PET images to yield latent patterns of relative hypometabolism unbiased by diagnostic classification, which are referred to as ‘eigenbrains’. These eigenbrains were subsequently linked to clinical and cognitive data and meta-analytic topics from a large external database of neuroimaging studies reflecting a wide range of mental functions. Finally, we performed a data-driven exploratory linear discriminant analysis to perform eigenbrain-based multiclass diagnostic predictions. Dysexecutive Alzheimer’s disease and bvFTD patients were the youngest at symptom onset, followed by behavioural Alzheimer’s disease, then amnestic Alzheimer’s disease. Dysexecutive Alzheimer’s disease patients had worse cognitive performance on nearly all cognitive domains compared with other groups, except verbal fluency which was equally impaired in dysexecutive Alzheimer’s disease and bvFTD. Hypometabolism was observed in heteromodal cortices in dysexecutive Alzheimer’s disease, temporo-parietal areas in amnestic Alzheimer’s disease and frontotemporal areas in bvFTD and behavioural Alzheimer’s disease. The unbiased spectral decomposition analysis revealed that relative hypometabolism in heteromodal cortices was associated with worse dysexecutive symptomatology and a lower likelihood of presenting with behaviour/personality problems, whereas relative hypometabolism in frontotemporal areas was associated with a higher likelihood of presenting with behaviour/personality problems but did not correlate with most cognitive measures. The linear discriminant analysis yielded an accuracy of 82.1% in predicting diagnostic category and did not misclassify any dysexecutive Alzheimer’s disease patient for behavioural Alzheimer’s disease and vice versa. Our results strongly suggest a double dissociation in that distinct macro-scale underpinnings underlie predominant dysexecutive versus personality/behavioural symptomatology in dementia syndromes. This has important implications for the implementation of criteria to diagnose and distinguish these diseases and supports the use of data-driven techniques to inform the classification of neurodegenerative diseases.

Abstract Given the prevalence of dementia and the development of pathology-specific disease-modifying therapies, high-value biomarker strategies to inform medical decision-making are critical. In vivo tau-PET is an ideal target as a biomarker for Alzheimer’s disease diagnosis and treatment outcome measure. However, tau-PET is not currently widely accessible to patients compared to other neuroimaging methods. In this study, we present a convolutional neural network (CNN) model that imputes tau-PET images from more widely available cross-modality imaging inputs. Participants (n = 1192) with brain T1-weighted MRI (T1w), fluorodeoxyglucose (FDG)-PET, amyloid-PET and tau-PET were included. We found that a CNN model can impute tau-PET images with high accuracy, the highest being for the FDG-based model followed by amyloid-PET and T1w. In testing implications of artificial intelligence-imputed tau-PET, only the FDG-based model showed a significant improvement of performance in classifying tau positivity and diagnostic groups compared to the original input data, suggesting that application of the model could enhance the utility of the metabolic images. The interpretability experiment revealed that the FDG- and T1w-based models utilized the non-local input from physically remote regions of interest to estimate the tau-PET, but this was not the case for the Pittsburgh compound B-based model. This implies that the model can learn the distinct biological relationship between FDG-PET, T1w and tau-PET from the relationship between amyloid-PET and tau-PET. Our study suggests that extending neuroimaging’s use with artificial intelligence to predict protein specific pathologies has great potential to inform emerging care models.