Tissue Regions Of Interest Research Articles

Liver tissue changes during and post 6-month spaceflight as measured by ultrasound radio frequency signal processing.

Analysis of ultrasound radio frequency (RF) signals allows for the determination of the index of reflectivity (IR), which is a new measure that is dependent on tissue properties. Previous work has shown differences in the IR of the carotid artery wall with long-duration spaceflight; therefore, it was hypothesized that liver tissue would also show differences in this measure with spaceflight. The RF signal of a liver tissue region of interest (ROI) was displayed and processed along six different lines covering a surface of approximately 2cm × 2cm. The IR was calculated as the energy backscattered by the liver ROI divided by the total energy returned to the ultrasound probe. Seven astronauts were investigated preflight, inflight on day 150, and postflight 4days and 6months after rerunning to Earth. Compared to preflight (63% ± 18%), the liver tissue ROI IR was significantly lower on flight day 150 (46% ± 14%; p = 0.027) and 4days postflight (46% ± 19%; p = 0.025). At 6months postflight, the IR returned to preflight values (59% ± 13%; p = 0.919). The significant decrease in the coefficient of reflectivity inflight and 4days postflight indicates an alteration in the liver tissue that reduces the reflection of ultrasound waves. This change in tissue properties could either be due to the addition of particles that do not reflect ultrasound waves or structural or cellular changes that alter the reflectivity of the tissue.

Artificial intelligence (AI) –powered H&E whole-slide image (WSI) analysis to predict recurrence in hormone receptor positive (HR+) early breast cancer (EBC).

571 Background: The recurrence risk (RS) based on transcriptomic profiles or Ki-67 level of HR+ EBC has been used for adjuvant treatment decisions and may also aid selecting patients who could benefit from novel treatments including CDK4/6 inhibitor or oral selective estrogen receptor degraders (SERDs). Artificial intelligence-powered H&E whole slide image (WSI) models can be a practical approach for this biomarker without the need of additional tissue samples. We developed ML-based models of histopathologic features from an H&E WSI to approximate RS of 21-gene assay (OncotypeDx) using various algorithms of different complexities. Methods: The development dataset was composed of 1,009 cases from TCGA-BRCA with gene expression profiles and 483 EBC cases from Samsung Medical Center with RS results. The prediction model was developed in two-stages. First, Lunit SCOPE, an AI-powered H&E WSI analyzer, detects various cell types and segments tissue areas resulting in various cell densities in tissue regions of interest. Next, four traditional ML models were applied on top of the first stage results. In addition, a deep learning model based on multiple instance learning (DL-MIL) was developed using three features: a supervised convolutional neural network, a supervised cell detection model, and an AI-based pathology profiling analyzer, for extracting semantic contents. The model performance was validated using two independent EBC test sets: 248 cases for comparison with the RS results (set 1), and 708 cases with DFS (set 2). Results: The cross-validation performance of the four traditional ML algorithms had area under the curve of receiver operating characteristics curve (AUROC) ranging from 0.728 to 0.779, where mitotic cell density, tumor cell density, and fibroblast density in cancer area were the variables with the highest importance for all four models. The DL-MIL model had cross validation performance of AUROC 0.831. In test set 1, DL-MIL model showed the highest discrimination with AUROC of 0.828 compared to traditional ML models where logistic regression showed the highest discrimination with AUROC of 0.786 (Table). The DL-MIL model showed the highest performance among the models to predict DFS with hazard ratio (HR) of 2.48 (1.47-4.18). Conclusions: Histopathomic models can accurately predict the RS of high risk as well as poor DFS from only H&E WSIs. These AI models can be a practical tool for treatment selection including emerging drugs such as SERDs. [Table: see text]

Abstract 7391: Beyond traditional staining: A virtual-IHC digital assay for HER2 score prediction from routine H&E images

Abstract Background: In breast cancer diagnostics, the accurate assessment of HER2 status is essential for treatment stratification. Conventionally, HER2 immunohistochemistry (IHC) scoring necessitates specialized staining protocols, which are resource- and time-intensive. This study presents a virtual IHC (vIHC) digital assay to predict HER2 scores directly from hematoxylin and eosin (H&E) stained images, which are routinely acquired for all specimens. This methodology aims to accelerate standard practices by reducing the need for additional IHC staining, thereby decreasing laboratory workload, costs, and turnaround time. It also presents an opportunity to screen a wider array of samples promptly, potentially identifying candidates for HER2-targeted therapy that might otherwise be delayed or overlooked. Methods: Serially sectioned breast core needle biopsy samples were stained with H&E and HER2-IHC following Leica-Bond staining protocols. Stained slides were imaged using the Hologic GeniusTM Digital Diagnostics System with Research Use Only software for Whole Slide Imaging. From this initial image dataset, 240 regions of interest (ROIs) were manually selected in order to create a dataset that uniformly covers: (i) all HER2-IHC scores (0-3), (ii) a wide range of HER2 stain intensities, and (iii) expression patterns. The IHC scores were assigned to each selected ROI by a pathology expert, following the scoring recommendations of ASCO/CAP American Society of Clinical Oncology and College of American Pathologists, with the caveat that ductal carcinoma in situ was also given a score for the purpose of training the model. These ROIs were then extracted from the images of the serially sectioned H&E-stained slides using Reveal Bio’s proprietary co-registration digital assay. A predictive digital assay was developed at Reveal Biosciences to classify the 240 H&E-stained breast tissue ROIs into four HER2 IHC score categories: 0, 1, 2, and 3. For model evaluation, cross-validation was performed 10 times by randomly splitting the dataset of H&E ROIs into 80% for training MIL and 20% for testing the model, followed by averaging the performance metrics across the 10 dataset splits. The performance metrics included classification accuracy, precision, recall, and F1 scores for each HER2 score category. Results: The vIHC digital assay for predicting the HER2 scores directly from H&E stained breast tissue images achieved the average classification accuracy of 93% across the four HER2 score categories on the test set of ROIs. This performance demonstrates that the model is capable of successfully distinguishing between the HER2 score categories directly from H&E images without the need for IHC staining. The results suggest that the vIHC digital assay could be a reliable and cost-effective tool for preliminary breast cancer screening, providing a valuable adjunct to traditional histopathological analysis. Citation Format: Misagh Naderi, Nikola Mitic, Nikola Spasic, Djordje Cikic, Kristen Ruf, Nikolaus Wagner, Igor Mihajlovic, Sinisa Todorovic, Ray Jenoski, Sid Mayer, Michael Quick, Claire Weston. Beyond traditional staining: A virtual-IHC digital assay for HER2 score prediction from routine H&E images [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7391.

Repeatability quantification of brain diffusion-weighted imaging for future clinical implementation at a low-field MR-linac

BackgroundLongitudinal assessments of apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging (DWI) during intracranial radiotherapy at magnetic resonance imaging-guided linear accelerators (MR-linacs) could enable early response assessment by tracking tumor diffusivity changes. However, DWI pulse sequences are currently unavailable in clinical practice at low-field MR-linacs. Quantifying the in vivo repeatability of ADC measurements is a crucial step towards clinical implementation of DWI sequences but has not yet been reported on for low-field MR-linacs. This study assessed ADC measurement repeatability in a phantom and in vivo at a 0.35 T MR-linac.MethodsEleven volunteers and a diffusion phantom were imaged on a 0.35 T MR-linac. Two echo-planar imaging DWI sequence variants, emphasizing high spatial resolution (“highRes”) and signal-to-noise ratio (“highSNR”), were investigated. A test–retest study with an intermediate outside-scanner-break was performed to assess repeatability in the phantom and volunteers’ brains. Mean ADCs within phantom vials, cerebrospinal fluid (CSF), and four brain tissue regions were compared to literature values. Absolute relative differences of mean ADCs in pre- and post-break scans were calculated for the diffusion phantom, and repeatability coefficients (RC) and relative RC (relRC) with 95% confidence intervals were determined for each region-of-interest (ROI) in volunteers.ResultsBoth DWI sequence variants demonstrated high repeatability, with absolute relative deviations below 1% for water, dimethyl sulfoxide, and polyethylene glycol in the diffusion phantom. RelRCs were 7% [5%, 12%] (CSF; highRes), 12% [9%, 22%] (CSF; highSNR), 9% [8%, 12%] (brain tissue ROIs; highRes), and 6% [5%, 7%] (brain tissue ROIs; highSNR), respectively. ADCs measured with the highSNR variant were consistent with literature values for volunteers, while smaller mean values were measured for the diffusion phantom. Conversely, the highRes variant underestimated ADCs compared to literature values, indicating systematic deviations.ConclusionsHigh repeatability of ADC measurements in a diffusion phantom and volunteers’ brains were measured at a low-field MR-linac. The highSNR variant outperformed the highRes variant in accuracy and repeatability, at the expense of an approximately doubled voxel volume. The observed high in vivo repeatability confirms the potential utility of DWI at low-field MR-linacs for early treatment response assessment.

Abstract WP106: Hemispheric Comparison Of Magnetic Susceptibility 24-72 Hours After Reperfusion

Introduction: Magnetic susceptibility is a radiological contrast mechanism that may provide a measure of oxygenation and contribute to an understanding the pathophysiology of stroke. Currently, there are few studies that have explored magnetic susceptibility in ischemic tissue as a marker of tissue injury. Our study assesses day one ischemic tissue post-reperfusion for changes in magnetic susceptibility and evaluates its relationship with radiological markers of ischemic injury - apparent diffusion coefficient (ADC). Methods: In a prospective observational study, thirty-five ischemic stroke patients who underwent endovascular thrombectomy were scanned with MRI (24 – 72 hours after reperfusion) to obtain multi-echo Gradient Echo and DWI images. An experienced neuroradiologist manually delineated ischemic tissue ROIs (region of interest) on DWI images. Contralateral ROIs were obtained using an automated method. The cerebrospinal fluid and regions of haemorrhagic transformation were excluded from the ROIs. A Morphology Enabled Dipole Inversion (MEDI) pipeline was employed to generate Quantitative Susceptibility Mapping (QSM) images, and these were used to quantify magnetic susceptibility. Mean magnetic susceptibility and mean ADC values were measured in these ROIs, and these values were then correlated. Results: Mean magnetic susceptibility of the ischemic ROI was significantly higher than that of the contralateral ROI (0.71±11.72 ppb, -5.59±7.30 ppb; p = 0.003). Mean ADC values of the ROI in the ischemic hemisphere were significantly lower than those in the contralateral hemisphere (0.65±0.09 х 10 -3 mm 2 /s, 0.81±0.05 х 10 -3 mm 2 /s; p = 1.31 х 10 -11 ). No significant correlation between susceptibility and ADC was found (ρ = -0.278, p = 0.106). Conclusion: Magnetic susceptibility in the ischemic tissue is measurable and is elevated when compared to the normal tissue. This may be attributed to the increased oxygen extraction fraction in the ischemic tissue. Further voxel wise analysis and larger longitudinal multi-parametric analysis correlating magnetic susceptibility with other imaging measures of tissue injury over time will help characterise the significance of lesional magnetic susceptibility changes in ischemic tissue.

A multi-scale, multi-region and attention mechanism-based deep learning framework for prediction of grading in hepatocellular carcinoma.

Histopathological grading is a significant risk factor for postsurgical recurrence in hepatocellular carcinoma (HCC). Preoperative knowledge of histopathological grading could provide instructive guidance for individualized treatment decision-making in HCC management. This study aims to develop and validate a newly proposed deep learning model to predict histopathological grading in HCC with improved accuracy. In this dual-centre study, we retrospectively enrolled 384 HCC patients with complete clinical, pathological and radiological data. Aiming to synthesize radiological information derived from both tumour parenchyma and peritumoral microenvironment regions, a modelling strategy based on a multi-scale and multi-region dense connected convolutional neural network (MSMR-DenseCNNs) was proposed to predict histopathological grading using preoperative contrast enhanced computed tomography (CT) images. Multi-scale inputs were defined as three-scale enlargement of an original minimum bounding box in width and height by given pixels, which correspondingly contained more peritumoral analysis areas with the enlargement. Multi-region inputs were defined as three regions of interest (ROIs) including a squared ROI, a precisely delineated tumour ROI, and a peritumoral tissue ROI. The DenseCNN structure was designed to consist of a shallow feature extraction layer, dense block module, and transition and attention module. The proposed MSMR-DenseCNN was pretrained by the ImageNet dataset to capture basic graphic characteristics from the images and was retrained by the collected retrospective CT images. The predictive ability of the MSMR-DenseCNN models on triphasic images was compared with a conventional radiomics model, radiological model and clinical model. MSMR-DenseCNN applied to the delayed phase (DP) achieved the highest area under the curve (AUC) of 0.867 in the validation cohort for grading prediction, outperforming those on the arterial phase (AP) and portal venous phase (PVP). Fusion of the results on triphasic images did not increase the predictive ability, which underscored the role of DP for grading prediction. Compared with a single-scale and single-region network, the DP-phase based MSMR-DenseCNN model remarkably raised sensitivity from 67.4% to 75.5% with comparable specificity of 78.6%. MSMR-DenseCNN on DP defeated conventional radiomics, radiological and clinical models, where the AUCs were correspondingly 0.765, 0.695 and 0.612 in the validation cohort. The MSMR-DenseCNN modelling strategy increased the accuracy for preoperative prediction of grading in HCC, and enlightens similar radiological analysis pipelines in a variety of clinical scenarios in HCC management.

Assessment of bone densitometry using radiography with a step-wedge phantom: a pilot study of the forearm.

Radiographic absorptiometry (RA) is one of the earliest methods of bone densitometry and has been used to measure the phalanges and metacarpals where soft tissue attenuation is minimal. The aim of this study was to determine whether the technique can be adapted to correct for soft tissue attenuation and measure areal bone mineral density (aBMD) in the forearm. A total of 51 patients referred for a clinical spine and hip dual-energy X-ray absorptiometry (DXA) examination and 8 young and middle-aged volunteers were recruited to this study. The first 29 participants (20 women, 9 men, aged 61±14 years) served as the training cohort, and the remaining 30 (20 women, 10 men, aged 55±16 years) comprised the validation cohort. All participants underwent a DXA scan of their non-dominant forearm, and a digital X-ray image of the same arm was acquired with a step phantom. Identical regions of interest (ROIs) in the radius and ulna at the one-third radius site were measured on the X-ray and DXA images, and a soft tissue ROI was measured on X-ray images between the radius and ulna. The X-ray measurements in the training cohort were expressed as equivalent step phantom thickness (Eq. SPT) and used to estimate forearm aBMD using a linear equation calibrated against the DXA scans. Estimates of forearm aBMD made from the digital X-ray images acquired in the validation cohort were compared with the results of the DXA scans. Digital X-ray estimates of radius and ulna aBMD at the one-third radius site in the validation cohort showed a good correlation with GE-Lunar iDXA scanner measurements (r=0.795; P<0.001). The Bland-Altman plot had a mean bias of -0.002 g/cm2 and 95% limits of agreement of -0.185 to +0.181 g/cm2. Digital X-ray estimates of proximal forearm aBMD corrected for soft tissue attenuation correlated with DXA measurements with correlation coefficients comparable to those seen for other peripheral bone densitometry technologies.

Soft Tissue Standardization Improves Humerus Tissue Thickness Precision and Lowers BMD in Total Shoulder Arthroplasty Patients

DXA technology requires soft tissue attenuation measurement. This sampling is confined within a standard soft tissue region of interest (ROI) defined by the blue squares on Figure 1, which is automatically placed and used to compare with radiation absorption over bone. Variable placement of the soft tissue ROI likely alters BMD measurement and reproducibility. The aim of this study was to measure humeral BMD in total shoulder arthroplasty (TSA) patients. As no shoulder feature is available in GE Lunar enCORE v18, hip atypical femur fracture (AFF) software was utilized. In preliminary evaluation, we observed that soft tissue ROI placement and amount of tissue sampled were inconsistent (Figure 2). We hypothesized that use of a consistent soft tissue ROI would change BMD and improve precision. The purpose of this analysis was to compare humerus BMD and precision prior to and following soft tissue ROI standardization. After manufacturer consultation, all scans were reanalyzed using a soft tissue ROI of a fixed size (100 pixels tall, 50 pixels wide) manually placed on every scan distal and lateral to the humeral head (Figure 3). This study of TSA patients, 1-5 years post-surgery, included full humerus scans obtained on a GE Lunar iDXA with v17 or v18 software and analyzed with v18. Each participant had bilateral shoulder scans performed twice to assess precision and humerus BMD was measured at custom ROIs. BMD and tissue thickness obtained with automated and manual approaches were compared using T-test. Precision was determined using the ISCD calculator and compared between methods with F-test. Thirty subjects were included (20M/10F) with mean age and years’ post-surgery of 70.6 and 2.7 respectively. Average tissue thickness was reduced 13.5% (p < 0.01) with manual tissue sampling. Mean BMD among the nine custom ROIs was 2.2-8.5% lower (p < 0.01) with manual tissue sampling (Table 1). Additionally, tissue thickness %CV improved (p < 0.01) from 47.6 to 14.8% and 25.9 to 9.6%. Similarly, BMD precision improved (p < 0.01) at the 1/3 humerus and distal implant ROIs on the TSA side (Table 2). When measuring skeletal sites without optimized software available, it is necessary to critically evaluate software performance. In this study, soft tissue ROI standardization markedly improved tissue thickness sampling and reduced BMD at custom humerus ROIs. Optimization and automation of humerus DXA software would likely further improve BMD precision and measurement.

Spatial protein heterogeneity analysis in frozen tissues to evaluate tumor heterogeneity.

A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer patients. Comprehending tumor heterogeneity, especially at the protein level, is critical for tracking tumor evolution, and showing the presence of different phenotypical variants and their location with respect to tissue architecture. Although a variety of techniques is available for quantifying protein expression, the heterogeneity observed in the tissue is rarely addressed. The proposed method is validated in breast cancer fresh-frozen tissues derived from five patients. Protein expression is quantified on the tissue regions of interest (ROI) with a resolution of up to 100 μm in diameter. High heterogeneity values across the analyzed patients in proteins such as cytokeratin 7, β-actin and epidermal growth factor receptor (EGFR) using a Shannon entropy analysis are observed. Additionally, ROIs are clustered according to their expression levels, showing their location in the tissue section, and highlighting that similar phenotypical variants are not always located in neighboring regions. Interestingly, a patient with a phenotype related to increased aggressiveness of the tumor presents a unique protein expression pattern. In summary, a workflow for the localized extraction and protein analysis of regions of interest from frozen tissues, enabling the evaluation of tumor heterogeneity at the protein level is presented.

Exploring the diagnostic potential of adding T2 dependence in diffusion-weighted MR imaging of the prostate.

Magnetic resonance imaging (MRI) is essential in the detection and staging of prostate cancer. However, improved tools to distinguish between low-risk and high-risk cancer are needed in order to select the appropriate treatment. To investigate the diagnostic potential of signal fractions estimated from a two-component model using combined T2- and diffusion-weighted imaging (T2-DWI). 62 patients with prostate cancer and 14 patients with benign prostatic hyperplasia (BPH) underwent combined T2-DWI (TE = 55 and 73 ms, b-values = 50 and 700 s/mm2) following clinical suspicion of cancer, providing a set of 4 measurements per voxel. Cancer was confirmed in post-MRI biopsy, and regions of interest (ROIs) were delineated based on radiology reporting. Signal fractions of the slow component (SFslow) of the proposed two-component model were calculated from a model fit with 2 free parameters, and compared to conventional bi- and mono-exponential apparent diffusion coefficient (ADC) models. All three models showed a significant difference (p<0.0001) between peripheral zone (PZ) tumor and normal tissue ROIs, but not between non-PZ tumor and BPH ROIs. The area under the receiver operating characteristics curve distinguishing tumor from prostate voxels was 0.956, 0.949 and 0.949 for the two-component, bi-exponential and mono-exponential models, respectively. The corresponding Spearman correlation coefficients between tumor values and Gleason Grade Group were fair (0.370, 0.499 and -0.490), but not significant. Signal fraction estimates from a two-component model based on combined T2-DWI can differentiate between tumor and normal prostate tissue and show potential for prostate cancer diagnosis. The model performed similarly to conventional diffusion models.

Advanced Molecular Characterization Using Digital Spatial Profiling Technology on Immunooncology Targets in Methylated Compared with Unmethylated IDH-Wildtype Glioblastoma

Introduction Glioblastoma (GBM) is the most common primary adult brain tumour with a median overall survival (OS) of 12–15 months. Molecular characterization of multiple immunooncology targets in GBM may help target novel immunotherapeutic strategies. We used NanoString GeoMx® Digital Spatial Profiling (DSP) to assess multiple immunooncology protein targets in methylated versus unmethylated IDH-wild-type glioblastoma. Methods NanoString GeoMx® DSP technology uses multiple primary antibodies conjugated to indexing DNA oligos with a UV photocleavable linker. Tissue regions of interest (ROIs) are selected with bound fluorescent antibodies; oligos are released via a UV-mediated linker and quantitated. We used DSP multiplex analysis of 31 immunooncology proteins and controls (CD4, CD14, CD68, CD8A, B7-H3, PD-L1, CD19, FOXP3, CD44, STAT3 (phospho Y705), CD45, Pan Cytokeratin, MS4A1/CD20, CD45RO, PD1, CD3, beta-2 microglobulin, VISTA, Bcl2, GZMB, PTEN, beta-catenin, CD56, Ki-67, STAT3, AKT, p-Akt, S6, Histone H3, IgG Rabbit control, and Mouse IgG control) from ROIs in a cohort of 10 IDH-wild-type glioblastomas (5 methylated and 5 unmethylated). An nCounter platform allowed quantitative comparisons of antibodies between ROIs in MGMT methylated and unmethylated tumours. Mean protein expression counts between methylated and unmethylated GBM were compared using technical and biological replicates. Results The analysis showed 10/27 immunooncology target proteins were significantly increased in methylated versus unmethylated IDH-wild-type glioblastoma tumour core (false discovery rate (FDR) <0.1 by Benjamini–Hochberg procedure). Conclusions NanoString GeoMx® DSP was used to analyse multiple immunooncology protein target expression in methylated versus unmethylated IDH-wild-type glioblastoma. In this small study, there was a statistical increase in CD4, CD14, CD68, CD8A, B7-H3, PDL-1, CD19, FOXP3, CD44, and STAT3 protein expression in methylated versus unmethylated GBM tumour core; however, this requires larger cohort validation. Advanced multiplex immunooncological biomarker analysis may be useful in identifying biomarkers for novel immunotherapeutic agents in GBMs.

Furosemide administration onehour before bone scintigraphy examination in horses does not improve the image quality or reduce the radiation dose rate.

This prospective, cross-sectional, pilot study aimed to investigate the effects of furosemide as a diureticon the image quality of bone scintigraphy performed using 99m Tc-HDP and to investigate the impact of furosemide on the radiation dose rate. Thirty-one horses undergoing bone scintigraphy were included. The horses were divided into the control (n=14) and furosemide group (n=17), which received 1mg/kg furosemide intravenously 1 h post 99m Tc-HDP administration. The image quality was assessed subjectively and semi-quantitatively. The bone-to-soft tissue (B:S) ratio was calculated from the counts per pixel of regions of interest (ROI) positioned over the left radial diaphysis (bone ROI) and its caudal soft tissue area (soft tissue ROI). The radiation rate dose (μSv/h) of both groups was measured at 0, 3, 6, 12, 18, and 24h post 99m Tc-HDP administration at a distance of 0, 30, and 100cm from the head, kidney, and pelvis. The results showed no significant differences in the B:S ratio or the radiation dose rate observed between the groups. However, the radiation dose rate decreased by 56% at 3h post 99m Tc-HDP administration and keeping a distance of 30cm reduced the radiation dose rate by 65%. Administering furosemide does not improve the image quality or reduce the radiation dose rate. The authors recommend commencing with bone scintigraphy 3h post 99m Tc-HDP administration and keeping at least a distance of 30cm from the horse to reduce the staff radiation dose.

Light sheet fluorescence microscopy guided MALDI-imaging mass spectrometry of cleared tissue samples

Light sheet fluorescence microscopy (LSFM) of optically cleared biological samples represents a powerful tool to analyze the 3-dimensional morphology of tissues and organs. Multimodal combinations of LSFM with additional analyses of the identical sample help to limit the consumption of restricted specimen and reduce inter-sample variation. Here, we demonstrate the proof-of-concept that LSFM of cleared brain tissue samples can be combined with Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry Imaging (MALDI-MSI) for detection and quantification of proteins. Samples of freshly dissected murine brain and of archived formalin-fixed paraffin-embedded (FFPE) human brain tissue were cleared (3DISCO). Tissue regions of interest were defined by LSFM and excised, (re)-embedded in paraffin, and sectioned. Mouse sections were coated with sinapinic acid matrix. Human brain sections were pre-digested with trypsin and coated with α-cyano-4-hydroxycinnamic acid matrix. Subsequently, sections were subjected to MALDI-time-of-flight (TOF)-MSI in mass ranges between 0.8 to 4 kDa (human tissue sections), or 2.5–25 kDa (mouse tissue sections) with a lateral resolution of 50 µm. Protein- and peptide-identities corresponding to acquired MALDI-MSI spectra were confirmed by parallel liquid chromatography tandem mass spectrometry (LC–MS/MS) analysis. The spatial abundance- and intensity-patterns of established marker proteins detected by MALDI-MSI were also confirmed by immunohistochemistry.

An accelerated procedure for approaching and imaging of optically branded region of interest in tissue.

Many areas of biology have benefited from advances in light microscopy (LM). However, one limitation of the LM approach is that numerous critically important aspects of subcellular machineries are well beyond the resolution of conventional LM. For studying these, electron microscopy (EM) remains the technique of choice to visualize and identify macromolecules at the ultrastructural level. The most powerful approach is combining both techniques, LM and EM (i.e., to apply correlative light/electron microscopy, CLEM) to image exactly the same region of interest. This combination allows, for example, to immuno-localize proteins by LM and then to visualize the ultrastructural context of the same region of the sample. However, the identification and correlation of the regions of interest (ROIs) at the levels of LM and EM remains a major challenge, mostly due to the difficulties with correlation along the Z-axis for both modalities. In this chapter, we address this difficulty and describe an approach for performing CLEM in tissue samples using marks from near-infrared branding as indicators of a ROI, and then using serial block face-scanning electron microscopy (SBF-SEM) to identify and approach this ROI. Once a ROI has been approached, serial sections are collected on grids for high-resolution imaging by transmission EM, and subsequent correlation with LM images showing labeled proteins.

Advanced molecular characterization using Digital Spatial Profiling Technology on immuno-oncology targets in methylated compared with unmethylated IDH-wildtype glioblastoma

Abstract Introduction Glioblastoma (GBM) is the most common primary adult brain tumour with 12–15 months median overall survival. Molecular characterization of multiple immuno-onology targets in GBM is required to optimise immunotherapeutic strategies. Advanced molecular characterisation using Digital Spatial Profiling Technology allows assessment of multiple immuno-oncology protein targets in methylated compared with unmethylated Glioblastoma IDH-wildtype. Methods Nanostring DSP uses multiple primary antibodies conjugated to indexing DNA oligos with a UV photocleavable linker. Tissue Regions of interest (ROI) are selected with bound fluorescent antibodies, oligos are released via a UV-mediated linker and quantitated. We used DSP multiplex analysis of 31 immuno-oncology proteins and controls (PD-1, PD-L1, B7-H3, VISTA, CD45, CD45RO, CD3, MS4A1 (CD20), CD4, CD8A, CD68, STAT3, STAT3 (Phopshory705), GZMB, Beta-2-microglobulin, CD56, Beta-catenin, FOXP3, CD14, CD19, AKT, P-AKT, PTEN, Bcl-2, CD44, Histone H3, S6, IgG Rabbit Control, Mouse IgG Control, Pan-Cytokeratin, Ki67) from ROIs in a cohort of 10 Glioblastomas IDH-wildtype (5 methylated, 5 unmethylated). An nCounter platform allowed quantitative comparisons of antibodies between ROIs in MGMT methylated and unmethylated tumours. Mean protein expression counts between methylated and unmethylated GBM were compared using a linear mixed effect model for technical and biological replicates. Results GeoMx DSP showed 10/27 immuno-oncology target proteins were significantly increased in methylated versus unmethylated Glioblastoma IDH-wildtype (false discovery rate FDR &lt;0.1 by Benjamini-Hocheberg Procedure). Conclusions Increased immuno-oncology protein target expression in methylated versus unmethylated glioblastoma IDH-wildtype using DSP platform has been identified. Advanced multiplex immuno-oncological biomarker analysis is required to identify predictive biomarkers for novel immunotherapeutic agents in GBMs.

Abstract No. 531 Novel radiopaque Yttrium-90 glass microspheres in a porcine model: clinical potential for real-time targeting and dosimetry

Conventional Yttrium-90 (Y-90) microspheres for Radioembolization are not directly visible on X-ray fluoroscopy or computed tomography (CT) precluding microsphere visualization at administration. We investigate the use of novel radiopaque microspheres for real-time X-ray and CT confirmation of tissue targeting. Intra-procedural CT region of interest (ROI) quantification of microsphere radiopacity may be translated to absorbed dose, allowing for personalized therapy. 30 μm (mean) diameter, multi-component, non-irradiated Y-89 glass microspheres were injected through a .021” microcatheter into the segmental arteries of the right and left kidneys of a hybrid farm pig. Fluoroscopy at time of injection and static X-ray and Cone-beam CT (CBCT) after embolization were obtained. After sacrifice, kidneys were explanted and imaged using fan beam CT (FBCT) and co-registered to CBCT. Average Hounsfield Units (HU) and voxel enhancement ratio (VER) data were obtained and analyzed (ANOVA, p<0.01) from spherical ROI’s placed on embolized (n=1) and non-embolized tissue (n=3) for CBCT and FBCT respectively. Real-time fluoroscopic and static x-ray visualization of microsphere delivery was confirmed. Three embolized parenchymal (microvasculature) ROIs with CBCT had mean voxel values from 191 to 229 HU. Non-embolized CBCT ROI had a mean value of 68 HU with VER of 2.8-3.4. Three embolized FBCT mean voxel values ranged from 80 to 107 HU, non-embolized FBCT ROI had a mean value of 34 HU with 2.4-3.2 VER. Embolized tissue ROIs had significantly greater voxel values than the non-embolized ROI for both CBCT (p<0.001) and FBCT (p<0.001). 3-dimensional HU threshold map data demonstrate the potential for CT based absorbed dose microsphere quantification. Novel radiopaque Y-90 Glass Microspheres demonstrate real-time fluoroscopic and static X-ray visualization and significant increase in tissue CBCT and FBCT radiopacity relative to non-embolized tissue with potential for tumor targeting, assessment of microsphere distribution and absorbed dose quantification.Tabled 1Parenchymal Tissue ROICBCT Mean (HU)CBCT Standard DeviationFBCT Mean (HU)FBCT Standard DeviationNon-embolized ROI68153411Embolized ROI 1217299624Embolized ROI 22294810742Embolized ROI 3191258019 Open table in a new tab

CEST, ASL, and magnetization transfer contrast: How similar pulse sequences detect different phenomena.

Chemical exchange saturation transfer (CEST), arterial spin labeling (ASL), and magnetization transfer contrast (MTC) methods generate different contrasts for MRI. However, they share many similarities in terms of pulse sequences and mechanistic principles. They all use RF pulse preparation schemes to label the longitudinal magnetization of certain proton pools and follow the delivery and transfer of this magnetic label to a water proton pool in a tissue region of interest, where it accumulates and can be detected using any imaging sequence. Due to the versatility of MRI, differences in spectral, spatial or motional selectivity of these schemes can be exploited to achieve pool specificity, such as for arterial water protons in ASL, protons on solute molecules in CEST, and protons on semi-solid cell structures in MTC. Timing of these sequences can be used to optimize for the rate of a particular delivery and/or exchange transfer process, for instance, between different tissue compartments (ASL) or between tissue molecules (CEST/MTC). In this review, magnetic labeling strategies for ASL and the corresponding CEST and MTC pulse sequences are compared, including continuous labeling, single-pulse labeling, and multi-pulse labeling. Insight into the similarities and differences among these techniques is important not only to comprehend the mechanisms and confounds of the contrasts they generate, but also to stimulate the development of new MRI techniques to improve these contrasts or to reduce their interference. This, in turn, should benefit many possible applications in the fields of physiological and molecular imaging and spectroscopy.

A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images

BackgroundQuality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers.MethodsIn total, 192, 88, 76 and 55 brain images are acquired using T2*, T1, T2 and contrast-enhanced T1 (T1C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed.ResultsTo the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, pw ≥ 0.11; paired-sample t test, pp ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, picc ≥ 0.74). Furthermore, Pearson correlation coefficient (rp) suggests that SNRwm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T2* (rp ≥ 0.78), BRISQUE and NIQE in T1 (rp ≥ 0.77), BLIINDS-II in T2 (rp ≥ 0.68) and BRISQUE and NIQE in T1C (rp ≥ 0.62) weighted MR images, while SNRcsf correlates strongly with BLIINDS-II in T2* (rp ≥ 0.63) and in T2 (rp ≥ 0.64) weighted MR images.ConclusionsThe consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images.

Versatile system for single cell acquisition and analysis

Abstract Single cell analysis (SCA) has become an essential part of basic and clinical research. A prerequisite to SCA studies is the isolation of individual cells from cell cultures. There are commercially available technologies including laser based systems and cell sorting instruments. However, they are typically complex and expensive making integration within standard lab/clinical sample processing workflows difficult. We have developed a universal platform for rapid acquisition of single cells from cultures and deposition into the single wells for further molecular analysis or clonal expansion. The instrument may also be used for tissue microdissection and measurements of individual cell adhesion. The main working principle is based on the capillary based vacuum pulse assisted technology (CTAS; UP8797644). Developed instrument collects individual cells from any adherent cultures grown in standard cell culture dishes in as small as 15 nl volume. It may be used with a wide range of inverted microscopes, so that the cells of interest can be identified based on morphology, location or fluorescent label. Automatic extraction and deposition algorithms were developed for acquisition of targeted cells and tissue regions of interest. Here, individual cells were collected from human neuroblastoma SH-SY5Y, CHO, and 3T3 cell lines. Collected cells were dispensed immediately into individual wells for clonal expansion. Clonal expansion and trypan blue assay revealed minimal effect on cellular viability (up to 99% when compared to controls). The benefits of the proposed technology include cost-efficiency, simple operation, complete workflow, and compatibility with a wide range of inverted microscopes and standard culture dishes.

Can Signal-to-Noise Ratio Perform as a Baseline Indicator for Medical Image Quality Assessment

Natural image quality assessment (NIQA) wins increasing attention, while NIQA models are rarely used in the medical community. A couple of studies employ the NIQA methodologies for medical image quality assessment (MIQA), but building the benchmark data sets necessitates considerable time and professional skills. In particular, the characteristics of synthesized distortions are different from those of clinical distortions, which make the results not so convincing. In clinic, signal-to-noise ratio (SNR) is widely used, which is defined as the quotient of the mean signal intensity measured in a tissue region of interest (ROI) and the standard deviation of the signal intensity in an air region outside the imaged object, and both regions are outlined by specialists. We take advantage of the knowledge that SNR is routinely used and concern whether SNR measure can perform as a baseline metric for the development of MIQA algorithms. To address the issue, the inter-observer reliability of SNR measure is investigated regarding to different tissue ROIs [white matter (WM); cerebral spinal fluid (CSF)] in magnetic resonance (MR) images. A total of 192 T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , 88 T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , 76 T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> and 55 contrast-enhanced T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> C) weighted images are analyzed. Statistical analysis indicates that SNR values show consistency between different observers to the same ROI in each modality (Wilcoxon rank sum test, p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">w</sub> ≥ 0.11; and paired sample t-test, pp 0.28). Moreover, whether off-the-shelf NIQA models can predict MR image quality is considered by using SNR values as the reference scores. Four NIQA models (BIQI, BLIINDS-II, BRISQUE, and NIQE) are evaluated, and the correlation between SNR values and NIQA results is evaluated. Pearson correlation coefficient (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ) shows that WM-based SNR values correlates well with BIQI, BLIINDS-II and BRISQUE in T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> images (rp 0.77), BRISQUE and NIQE in T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> images (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ≥ 0.75), BLIINDS-II in T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> images (rp 0.67), and BRISQUE and NIQE in T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> C images (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ≥ 0.58), while CSF-based SNR values correlates well with BLIINDS-II in T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> images (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ≥ 0.64) and T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> images (r <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> ≥ 0.60), and all p <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> <; 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> . The prediction performance analysis further proves the result from the correlation analysis. Conclusively, SNR measure is reliable to different observations and can perform as a baseline indicator for the development of MIQA algorithms. In general, BRISQUE and BLIINDS-II are full of potential to be conditionally used as objective MIQA models toward human brain MR images. This paper presents the first attempt of using SNR measure to bridge the gap between NIQA and MIQA, and large-scale experiments should be further conducted to confirm the conclusion in this paper.