Citations

Abstract BACKGROUND Despite advances in cancer biology, surgery and its extent of resection remains the most important prognostic modulator in gliomas, with the distinction between infiltrated and non-infiltrated tissues being an issue at the penumbra. Isocitrate dehydrogenase (IDH) mutation is unique to IDH-mutant glioma cells and can be used as a potential surrogate for tumor infiltration. Here we evaluate desorption electrospray ionization-mass spectrometry (DESI-MS) as a near real-time method for detecting infiltration at the margin of IDH-mutant gliomas. METHODS We conducted a prospective study in which DESI-MS analysis was performed on freshly obtained tissue biopsies to detect the presence of 2-Hydroxyglutarate (2HG), with subsequent review of each sample by a board-certified neuropathologist. RESULTS A total of 247 biopsies from a 49-patient study were previously collected and analyzed at Indiana University. Assessment of IDH mutation status of 71 core biopsies yielded sensitivity, specificity, and accuracy values of 89, 100, and 94%, respectively. Subsequent improvement of the method, including the measurement of 2HG relative to an internal control (Glutamate, Glu), allowed for detection of IDH-mutant infiltration at surgical margins. We have collected 183 biopsies (116 core and 67 margin) from 23 patients. Preliminary results of IDH mutation assessments of core biopsies indicate 93% sensitivity, 100% specificity, and 98% accuracy and margin biopsies indicate 88% sensitivity, 100% specificity, and 94% accuracy. Tumor infiltration at the margin of the surgical resection by detection of IDH-mut infiltration was detected in 88% of margin biopsies from IDH mutant tumors (30 of 34 biopsies). CONCLUSION We present a novel method to allow intraoperative evaluation of IDH mutation presence in glioma margin biopsies. Our method and its ongoing validation identified tumor infiltration beyond surgical margins and serves as a foundation for molecularly guided surgery in IDH-mut gliomas via DESI-MS.

Abstract Exposure of biological systems to acute or chronic insults triggers a host of molecular and physiological responses to either tolerate, adapt, or fully restore homeostasis; these responses constitute the hallmarks of resilience. Given the many facets, dimensions, and discipline‐specific focus, gaining a shared understanding of “resilience” has been identified as a priority for supporting advances in cardiovascular health. This report is based on the working definition: “ Resilience is the ability of living systems to successfully maintain or return to homeostasis in response to physical, molecular, individual, social, societal, or environmental stressors or challenges ,” developed after considering many factors contributing to cardiovascular resilience through deliberations of multidisciplinary experts convened by the National Heart, Lung, and Blood Institute during a workshop entitled: “ Enhancing Resilience for Cardiovascular Health and Wellness.” Some of the main emerging themes that support the possibility of enhancing resilience for cardiovascular health include optimal energy management and substrate diversity, a robust immune system that safeguards tissue homeostasis, and social and community support. The report also highlights existing research challenges, along with immediate and long‐term opportunities for resilience research. Certain immediate opportunities identified are based on leveraging existing high‐dimensional data from longitudinal clinical studies to identify vascular resilience measures, create a ‘resilience index,’ and adopt a life‐course approach. Long‐term opportunities include developing quantitative cell/organ/system/community models to identify resilience factors and mechanisms at these various levels, designing experimental and clinical interventions that specifically assess resilience, adopting global sharing of resilience‐related data, and cross‐domain training of next‐generation researchers in this field.

Background. Danlou tablet (DLT), the traditional Chinese medicine has been commonly used for dyslipidemia, atherosclerosis, and coronary heart disease. Whether it was effective against vascular injury caused by CIH has remained unknown. The aim of the current study was to observe the effects of DLT on chronic intermittent hypoxia (CIH)-induced vascular injury via regulation of blood lipids and to explore potential mechanisms. Methods. Sixteen 12-week-old male ApoE−/− mice were randomly divided into four groups. The sham group was exposed to normal room air, whereas the other three groups were exposed to CIH. Mice in the CIH + normal saline (NS) group were gavaged with NS. Mice in the CIH + Angptl4-ab group were intraperitoneally injected with Angptl4-antibody. Mice in the CIH + DLT group were gavaged with DLT. After four weeks of intervention, serum lipid concentrations, and serum lipoprotein lipase (LPL) activity were detected. The changes in atherosclerosis in vascular tissue were detected by hematoxylin and eosin (H&E) staining. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot analysis were applied to detect the expression levels of hypoxia-induciblefactor-1 (HIF-1), factor-inhibiting HIF-1 (FIH-1), angiopoietin-like 4 (Angptl4), and LPL in different tissues. Results. CIH exposure increases serum lipid levels, decreases serum LPL activity, and exacerbates atherosclerosis. Both Angptl4-ab and DLT treatment reversed the changes in lipid concentration, LPL activity, and atherosclerosis caused by CIH. In the epididymal fat pad, CIH exposure decreased the expression of FIH-1 and increased the expression of HIF-1, whereas DLT treatment increased the expression of FIH-1 and LPL and inhibited the expression of HIF-1 and Angptl4. In heart tissue, the expression levels of LPL and Angptl4 were not affected by modeling or treatment. Conclusions. DLT improved vascular damage by improving the increase in blood lipids induced by CIH, potentially by upregulating FIH-1 and downregulating HIF-1 and Angptl4 in adipose tissue. Therefore, DLT may be a promising agent for the prevention and treatment of CIH-induced vascular injury.

AbstractBackgroundCone‐beam computed tomography (CBCT) allows for patient setup and positioning, and potentially dose verification or adaptive replanning prior to each treatment delivery. Poor CBCT image quality due to scatter artifacts and patient motion has been a major limiting factor. A new image reconstruction algorithm was recently clinically implemented for improving image quality through iterative reconstruction (iCBCT).PurposeThis study aims to characterize iCBCT image quality, establish image value (HU)‐to‐relative electron density (RED) calibration curves for dose calculation, and assess the dosimetric accuracy for different anatomical sites.Material and methodsBoth conventional CBCT and iCBCT scans were acquired from a Varian TrueBeam On‐Board Imager system. A Catphan 604 phantom was scanned to compare image quality between the traditional Feldkamp–Davis–Kress (FDK) and novel iterative reconstruction techniques. Computerized Imaging Reference Systems (CIRS) electron density phantom was used to construct site‐specific HU‐RED curves corresponding to various scan settings. The CIRS Dynamic Thorax phantom, Rando pelvis phantom, and BrainLab head phantom were used for assessing dosimetric accuracy calculated on iCBCT images, compared to that on traditional FDK‐based CBCT images. All phantoms were scanned on a computed tomography (CT) to obtain baseline HU values for comparison.ResultsTest results obtained from Catphan showed statistically significant improvement with iCBCT, compared to FDK CBCT. Average HU differences from the baseline CT values were improved to within ±30 HU for iCBCT, compared to FDK CBCT for phantom studies. Dose calculated on iCBCT for both phantoms and patient cases directly using baseline HU‐RED calibration from CT showed 0.5%–2.0% accuracy from the baseline dose calculated on CT, which is comparable to doses calculated using site‐specific HU‐RED calibration curves.ConclusioniCBCT provides improved image quality, improved HU accuracy compared to CT baseline, and has potential to provide online dose verification as part of the adaptive radiotherapy workflow directly using the baseline HU‐RED calibration curve from CT.

AbstractPurposeTo investigate the dependency of dose‐volume histogram (DVH) behavior and precision on underlying discretization using shapes and dose distributions with known analytical DVHs for five commercial DVH calculators.MethodsDVHs and summary metrics were extracted from all five systems using synthetic cone and cylinder objects for which the true volume and DVH curves were known. Trends in the curves and metrics were explored by varying the underlying voxelization of the CT image, structure set, and dose grid as well by varying the geometry of the structure and direction of a linear dose gradient. Using synthetic structures allowed for comparison with ground truth DVH curves to assess their accuracy while an algorithm was additionally developed to assess the precision of each system. The precision was calculated with a novel algorithm that treats any “stair step” behavior in a DVH curve as an uncertainty band and calculates the width, characterized as a percent difference, of the band for various DVH metrics. The underlying voxelization was additionally changed and DVHs were extracted for two clinical examples. The details of how each system calculated DVHs were also investigated and tendencies in the calculated curves, metrics, and precision were related to choices made in the calculation methodology.ResultsCalculation methodology differences that had a noticeable impact on the DVH curves and summary metrics include supersampling beyond the input grids and interpretation of the superior and inferior ends of the structures. Among the systems studied, the median precision ranged from 0.902% to 3.22%, and interquartile ranges varied from 1.09% to 3.91%.ConclusionsCommercial dose‐evaluation solutions can calculate different DVH curves, structure volume measures, and dose statistics for the same input data due to differences in their calculation methodologies. This study highlights the importance of understanding and investigating the DVH calculation when considering a new clinical system and when using more than one system for data transfer.

Abstract Objectives Patients with clonal cytopenia of undetermined significance (CCUS) are at increased risk of developing myeloid neoplasia (MN). We evaluated whether a simple flow cytometry immunophenotyping (FCIP) assay could differentiate the risk of development of MN in patients with CCUS. Methods Bone marrow aspirates were assessed by FCIP panel in a cohort of 80 patients identified as having CCUS based on next-generation sequencing or cytogenetics from March 2015 to May 2020, with available samples. Flow cytometric assay included CD13/HLA-DR expression pattern on CD34-positive myeloblasts; CD13/CD16 pattern on maturing granulocytic precursors; and aberrant expression of CD2, CD7, or CD56 on CD34-positive myeloblasts. Relevant demographic, comorbidity, and clinical and laboratory data, including the type and extent of genetic abnormalities, were extracted from the electronic health record. Results In total, 17 (21%) patients with CCUS developed MN over the follow-up period (median survival follow-up, 28 months [95% confidence interval, 19-31]). Flow cytometry immunophenotyping abnormalities, including the aberrant pattern of CD13/HLA-DR expression, as detected at the time of the diagnosis of CCUS, were significantly associated with risk of developing MN (hazard ratio, 2.97; P = .006). Additional FCIP parameters associated with the development of MN included abnormal expression of CD7 on myeloblasts and the presence vs absence of any FCIP abnormality. Conclusions A simple FCIP approach that includes assessment of CD13/HLA-DR pattern on CD34-positive myeloblasts can be useful in identifying patients with CCUS at higher risk of developing MN.

Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose–volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.