Features Of Spatial Arrangement Research Articles

Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings.

Purpose: We used computerized image analysis and machine learning approaches to characterize spatial arrangement features of the immune response from digitized autopsied H&E tissue images of the lung in coronavirus disease 2019 (COVID-19) patients. Additionally, we applied our approach to tease out potential morphometric differences from autopsies of patients who succumbed to COVID-19 versus H1N1. Approach: H&E lung whole slide images from autopsy specimens of nine COVID-19 and two H1N1 patients were computationally interrogated. 606 image patches ( per patient) of were extracted from the 11 autopsied patient studies. A watershed-based segmentation approach in conjunction with a machine learning classifier was employed to identify two types of nuclei families: lymphocytes and non-lymphocytes (i.e., other nucleated cells such as pneumocytes, macrophages, and neutrophils). Based off the proximity of the individual nuclei, clusters for each nuclei family were constructed. For each of the resulting clusters, a series of quantitative measurements relating to architecture and density of nuclei clusters were calculated. A receiver operating characteristics-based feature selection method, violin plots, and the t-distributed stochastic neighbor embedding algorithm were employed to study differences in immune patterns. Results: In COVID-19, the immune response consistently showed multiple small-size lymphocyte clusters, suggesting that lymphocyte response is rather modest, possibly due to lymphocytopenia. In H1N1, we found larger lymphocyte clusters that were proximal to large clusters of non-lymphocytes, a possible reflection of increased prevalence of macrophages and other immune cells. Conclusion: Our study shows the potential of computational pathology to uncover immune response features that may not be obvious by routine histopathology visual inspection.

Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

Spatial information regarding the arrangement of land cover objects plays an important role in distinguishing the land use types at land parcel or local neighborhood levels. This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images. We examine three kinds of graph-based methods, i.e., feature engineering, graph kernels, and GCNs. Based upon the extracted arrangement features and features regarding the spatial composition of land cover objects, we formulated ten land use classifications. We tested those on two different remote sensing images, which were acquired from GaoFen-2 (with a spatial resolution of 0.8 m) and ZiYuan-3 (of 2.5 m) satellites in 2020 on Fuzhou City, China. Our results showed that land use classifications that are based on the arrangement features derived from GCNs achieved the highest classification accuracy than using graph kernels and handcrafted graph features for both images. We also found that the contribution to separating land use types by arrangement features varies between GaoFen-2 and ZiYuan-3 images, due to the difference in the spatial resolution. This study offers a set of approaches for effectively mapping land use types from (very) high resolution satellite images.

Computerized features of spatial arrangement of tumor-infiltrating lymphocytes from H&E images predicts survival and response to checkpoint inhibitors in gynecologic cancers.

6074 Background: Immune checkpoint inhibitors (ICI) have demonstrated success in solid tumors. In gynecologic cancers (GC), the response rate is still low (~10-15%) except in MSI-H endometrial cancer (~ 50%). Current biomarkers (e.g. PDL1 expression) have limited utility in identifying benefit from ICI in GC. In this work we evaluated the ability of computational measurements of spatial arrangement of tumor infiltrating lymphocytes (TIL) from H&E slide images in predicting overall survival (OS) and response to ICI in ovarian, cervical and endometrial cancers. Methods: The study included 151 patients, including 102 ovarian carcinomas treated with surgery and chemotherapy (D1) and another set (D2) of n=49 patients (n=14 ovarian, n=27 endometrial and n=8 cervical), treated with different ICI agents (Pembrolizumab, Nivolumab, Ipilimumab, Avelumab) in the second line setting. Progressors and non-progressors in D2 were classified according to clinical improvement and radiologic assessment by RECIST. A machine learning approach was employed to identify tumor regions on the diagnostic slides from D1 and D2 and then used to automatically identify TILs within the tumor regions. Subsequently machine learning was used to define TIL clusters based on TIL proximity, and graph network theory was used to capture measurements relating to spatial arrangement of TIL clusters. The multivariable Cox regression model (MCRM) was trained on n=51 patients from D1 to predict OS and then independently evaluated in predicting (1) OS on the hold-out n=51 patients in D1 and (2) response and progression-free survival (PFS) in D2. Results: Statistical analysis identified 7 prognostic features relating to interaction of TIL clusters with cancer nuclei. MCRM was prognostic of OS on the n=51 hold out patients in D1 (hazard ratio (HR)=2.06, 95% confidence interval [1.04- 4.07], p=0.008) and predictive of PFS in D2 (HR=2.24, CI=[1.13-4.44], p=0.03). The AUC for MCRM in predicting progression in D2 was 82%. Conclusions: Computerized features of spatial arrangement of TILs on H&E images were prognostic of OS and PFS and predicted response to ICI in three gynecological cancers. These findings need to be validated in larger, multi-site validation sets. [Table: see text]

A Study on Nuclei Segmentation, Feature Extraction and Disease Stage Classification for Human Brain Histopathological Images

Computer aided diagnosis (CAD) systems are important in obtaining precision medicine and patient driven solutions for various diseases. One of the main brain tumor is the Glioblastoma multiforme (GBM) and histopathological tissue images can provide unique insights into identifying and grading disease stages. In this study, we consider nuclei segmentation method, feature extraction and disease stage classification for brain tumor histopathological images using automatic image analysis methods. In particular we utilized automatic nuclei segmentation and labeling for histopathology image data obtained from The Cancer Genome Atlas (TCGA) and check for significance of feature descriptors using K-S test and classification accuracy using support vector machine (SVM) and Random Forests (RF). Our results indicate that we obtain classification accuracy 98.6% and 99.8% in the case of Object-Level features and 82.1% and 86.1% in the case of Spatial Arrangement features, respectively.

A general model of the opiate pharmacophore 1. Regions of the opiate pharmacophore responsible for nonselective affinity for the opiate receptor

By superposition of the molecules of opiate receptor ligands of various structural classes, three regions responsible for the nonselective ligand affinity were distinguished in the opiate pharmacophore. Spatial arrangement features, electronic properties, the capability of H-bonding and hydrophobic and electrostatic interactions of these regions were determined. The set of geometric parameters found can be used as a criterion for estimation of the opiate activity in simulation of new types of ligands.

Pulmonary artery and vein classification method using spatial arrangement features from X-ray CT image

Pulmonary artery and vein classification method using spatial arrangement features from X-ray CT image