The ability to measure neutralizing antibodies on large scale can be important for understanding features of the natural history and epidemiology of infection, as well as an aid in determining the ...efficacy of interventions, particularly in outbreaks such as the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Because of the assay's rapid scalability and high efficiency, serology measurements that quantify the presence rather than function of serum antibodies often serve as proxies of immune protection. Here, we report the development of a high-throughput, automated fluorescence-based neutralization assay using SARS-CoV-2 virus to quantify neutralizing antibody activity in patient specimens. We performed large-scale testing of over 19,000 COVID-19 convalescent plasma (CCP) samples from patients who had been infected with SARS-CoV-2 between March and August 2020 across the United States. The neutralization capacity of the samples was moderately correlated with serological measurements of anti-receptor-binding domain (RBD) IgG levels. The neutralizing antibody levels within these convalescent-phase serum samples were highly variable against the original USA-WA1/2020 strain with almost 10% of individuals who had had PCR-confirmed SARS-CoV-2 infection having no detectable antibodies either by serology or neutralization, and ~1/3 having no or low neutralizing activity. Discordance between neutralization and serology measurements was mainly due to the presence of non-IgG RBD isotypes. Meanwhile, natural infection with the earliest SARS-CoV-2 strain USA-WA1/2020 resulted in weaker neutralization of subsequent B.1.1.7 (alpha) and the B.1.351 (beta) variants, with 88% of samples having no activity against the BA.1 (omicron) variant.
The ability to directly measure neutralizing antibodies on live SARS-CoV-2 virus in individuals can play an important role in understanding the efficacy of therapeutic interventions or vaccines. In contrast to functional neutralization assays, serological assays only quantify the presence of antibodies as a proxy of immune protection. Here, we have developed a high-throughput, automated neutralization assay for SARS-CoV-2 and measured the neutralizing activity of ~19,000 COVID-19 convalescent plasma (CCP) samples collected across the United States between March and August of 2020. These data were used to support the FDA's interpretation of CCP efficacy in patients with SARS-CoV-2 infection and their issuance of emergency use authorization of CCP in 2020.
Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate ...with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides.
This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing.
Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area.
This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.
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Abstract
Gliomas are vastly heterogenous tumors with wide-ranging pathological signatures; as such, there is a pressing need to understand how individualized factors affect tumor growth patterns to ...develop treatment plans and improve prognosis. This study used autopsy-based radio-pathomic maps of tumor probability to test the hypothesis that differences in patient sex and age impact the presence of non-enhancing tumor. This study used pre-surgical imaging and demographic data from 501 glioma patients from the UCSF-PDGM dataset. Specifically, T1, T1C, FLAIR, and ADC images were used to generate maps of tumor probability using a previously validated model. This model used autopsy tissue aligned to imaging as ground truth to develop models sensitive to identifying areas of non-enhancing tumor outside of traditional imaging signatures. Thresholded maps were used to delineate areas of tumor presence, and the non-enhancing tumor volume was computed for each patient as the predicted high probability tumor volume within non-enhancing FLAIR hyperintensity. A general linear model was then fit to assess the effect of age and sex on non-enhancing tumor volumes, using tumor grade and contrast-enhancing volume as covariates. Male patients trended towards greater non-enhancing tumor volumes compared to female patients (B=5676.6, p=0.053). Older patients were also found to have less non-enhancing tumor volume compared to younger patients (B=-261.1, p=0.015). These results suggest that demographic characteristics influence the progression of tumor beyond the angiogenic tumor mass and may therefore influence treatment efficacy and prognoses. Future research is essential to understanding how these sex differences relate to the worse prognosis historically observed for males with glioma, and how the reduced presence of non-enhancing tumor in older patients relates to a reduction in plasticity-induced tumor propagation.
Abstract
Gliomas are the most common primary adult brain tumor in the United States and their biological heterogeneity makes it difficult to assess and treat tumor progression over time. This study ...used conventional MRI tumor segmentations to determine longitudinal growth differences between low grade (≤ G2), G3, and G4 gliomas. A total of 60 patients with at least three scans collected before death were organized into low-grade (n = 9), G3 (n = 13), and G4 (n = 38) gliomas at initial diagnosis. T1, T1C, FLAIR, and T2 images from every timepoint across all patients were used as input for tumor segmentation, using BraTS, to segment contrast enhancement (CE), FLAIR hyperintensity (FH), and the necrotic core (NC). Volumes within each segmentation and the time between each subsequent MRI was calculated, where the first post-surgical MRI was considered time 0. Linear mixed models were fitted for each segmentation volume, where Time, Group (i.e., G2-4) and their interaction (Group*Time) were fixed effects and subject was a random effect. We found that Time and Group*Time interaction were significant predictors of FH growth (p < 0.001 and 0.04, respectively); however, no group-level differences were observed in FH volumes over time. Group was the only significant predictor of CE growth (p = 0.006). Specifically, G4 growth was greater than G2 and had a trending increase than G3 (p = 0.003 and 0.08, respectively). Group, Time, and Group*Time were all significant predictors of NC growth (p = 0.04, 0.001, and 0.01, respectively). G4 NC growth was significantly greater than both G2 and G3 (both p < 0.05). No difference any of these tumor segmentations growth between G2 and G3 was detected. These results further show the heterogeneity in glioma growth over time and the necessity to assess how treatment effects longitudinal tumor composition progression and overall survival.
Abstract
AIM
The diffuse infiltration of glioblastoma (GBM) along the intricate network of white matter (WM) tracts poses a major challenge. Our aim is to investigate the potential of axons ...intersecting tumor enhancement (AXITE)-derived radiomic features in predicting one-year overall survival (OS), in comparison to conventional tumor-based radiomics.
METHODS
This study included data from the publicly available UCSF-PDGM-v2 (n=97). T1+C images and their corresponding tumor mask were rigidly registered to a common brain template (MNI152) using ANTs. WM tracts within the tumor-defined region of interest were generated using the HCP1065-1mm tractography atlas in DSI Studio Software, which mapped the AXITEs. Radiomic features were then extracted using PyRadiomics from subsequently masked AXITE maps as well as contrast-enhancing tumor masks for a comparative analysis. Four machine learning models, including a Random Forest, Support Vector Machine (SVM), Decision Tree, and Logistic Regression, were employed to predict OS using an 80/20 train/test split.
RESULTS
Among all models, SVM was the most effective in predicting OS. AXITE radiomics demonstrated remarkable precision (0.73 for OS < 1 yr and 0.78 for OS >1yr), accuracy (0.75), F1-Score (0.75), recall (0.75), and AUC of 0.84 in ROC analysis outclassing the performance of traditional tumor radiomics which had an accuracy (0.60), precision (0.66), F1-Score (0.56) and AUC of 0.60.
CONCLUSIONS
AXITE Radiomics holds promising potential in capturing tumoral heterogeneity and white matter tract infiltration of GBM, demonstrating superior prediction over traditional tumor radiomics. Our approach presents a more accurate representation of tumor invasiveness and tumor resistance associated with OS more accurately and promises a potential tool for improving patient prognosis and patient management.
Abstract
Current standard of care for glioblastoma includes surgery followed by chemotherapy and radiation, with extent of resection strongly correlated with survival. There is a need to improve the ...detection of tumor invasion beyond traditional imaging methods, as the full extent of tumor is known to be underestimated. Fluorescence-guided surgery with 5-Aminolevulinic acid (5-ALA) allows surgeons to visualize tumor cells beyond what is seen within traditional MR imaging margins. In this study, we compared tumor probability maps (TPMs) that detect tumor outside of contrast enhancement (CE) to the 5-ALA-guided resection cavity in glioblastoma patients. 3 pathologically confirmed glioblastoma patients that followed standard of care and underwent 5-ALA surgery were included. Patient 1 was a 56-year-old male with radiographic recurrence at 26 days, patient 2 was a 61-year-old male with radiographic recurrence at 130 days, and patient 3 was a 72-year-old male with cystic recurrence at 84 days, all post 5-ALA surgery. A previously published algorithm, using T1, T1C, FLAIR, and ADC as input and autopsy tissue as ground truth, was used to generate TPMs. Pre-surgical TPMs were qualitatively compared to pre-surgical CE, post-5-ALA surgical resection cavities, and radiographic recurrence seen as CE. In patients 1 and 2, TPMs identified areas suspicious of tumor that was not resected and later exhibited radiographic recurrence. Patient 3 had cystic recurrence only identified on post-resection T1C. In all patients, the extent of 5-ALA resection was greater than CE volume, but smaller than TPM identified tumor. These findings suggest that there was residual tumor that was unidentified via 5-ALA and traditional imaging but was identified by the TPMs. Future studies could utilize these maps in conjunction with 5-ALA to maximize surgical resections and prolong overall survival.
Abstract
INTRODUCTION: Glioblastoma (GBM) is an aggressive primary brain cancer with significant resistance to the current therapeutic approach of chemotherapy and radiotherapy, jointly known as ...chemoradiation (CRT). Magnetic hyperthermia therapy (MHT) is a promising therapy for GBM that can be used to perform multiple sessions of non-invasive, localized hyperthermia by activating locally delivered magnetic iron oxide nanoparticles (MIONPs) with an external alternating magnetic field (AMF). In this study, MHT-mediated enhancement of CRT was evaluated in murine and human glioma cell lines both in cell culture and in rodents.
Abstract
Isocitrate dehydrogenase 1 (IDH1) mutation status is used as an important prognostic marker for gliomas, where IDH1-wildtype patients see shorter survival than patients with an IDH1 ...mutation. This study uses radio-pathomic maps of cell, extracellular fluid (ECF), and cytoplasm (Cyt) density developed using conventional MRI and aligned autopsy samples to test the hypothesis that IDH1-mutant tumors differ in contrast-enhancing and non-enhancing tissue composition from IDH1-wildtype tumors. A total of 426 patients from the UCSF-PDGM data set (380 IDH1-wildtype, 46 IDH1-mutant) were included in this study. T1, T1C, FLAIR and ADC images from each patient’s pre-surgical MRI were used to generate whole brain radio-pathomic maps of cell, ECF, and Cyt density using a previously reported algorithm. This tool was trained using aligned autopsy samples as ground truth and is particularly adept at highlighting areas of tumor beyond traditional imaging signatures. The mean value from each feature map was computed for each patient both within non-necrotic contrast enhancement and non-enhancing FLAIR hyperintensity. Higher ECF density (F=15.45, p< 0.001) and lower Cyt density (F=8.51, p=0.004) was observed within contrast-enhancement for IDH1 mutant patients, with no difference observed for cell density within contrast enhancement between mutation statuses (F=0.03, p=0.86). Higher cell density (F=4.16, p< 0.042), Higher ECF density (F=4.65, p=0.032), lower Cyt density (F=4.48, p=0.035) was also found in patients with IDH1 mutations in the non-enhancing FLAIR hyperintense region. These results suggest that the large-scale tissue composition of IDH1-mutant tumors may differ from IDH1-wildtype tumors, suggesting differences in growth patterns and pathological composition between tumor types. Future studies examining how tissue composition post-treatment over time may elucidate how pathological characteristics evolve for each tumor type.
Abstract
PURPOSE
Glioblastoma (GBM) is an aggressive primary brain tumor that is monitored through magnetic resonance imaging (MRI). Radiomic analysis offers valuable insight into better ...understanding the imaging features of these tumors. We tested the hypothesis that changes in radiomic features following radiation treatment predicts overall survival. Radiomic features we were interested in included 2D and 3D shape features, first order features evaluating patterns of voxel intensity within the mask regions, and higher order GLCM features.
METHODS
Data from 19 patients from our brain bank diagnosed with a GBM at autopsy and underwent radiation therapy were used for this study. T1C tumor masks were created by manually annotating contrast enhancing tumor for patients on pre- and post-treatment MRIs. Pyradiomics was used to extract radiomic features within both pre- and post-radiation tumor masks, including 14 shape features, 18 first order features, and 75 higher order features were compared across timepoints and the difference for each variable was correlated with overall survival.
RESULTS
Of the tested radiomic features, only the following shape features were found to be correlated with overall survival: Major Axis Length (R = -0.55, p =.02), Maximum 2D Diameter Row (R= -.56, p =.02), Maximum 2D Diameter Slice (R= -.52, p =.03), and Maximum 3D Diameter (R= -.47, p = .04).
CONCLUSIONS
These results show a decrease in the Major Axis Length, Maximum 2D Diameter, Maximum 2D Diameter, and Maximum 3D diameter of the T1C masks post-radiation correlate with an overall increase in survival. These inverse trending radiomic variables may be useful to assess tumor response to radiation therapy, specifically through assessment of shape features within the contrast-enhancing tumor region. Future research should determine how tumor changes in response to other treatment modalities and/or correlates with other prognostic variables.
