MicroRNAs associated with postoperative outcomes in patients with limited stage neuroendocrine carcinoma of the esophagus

Esophageal neuroendocrine carcinoma (E-NEC) is an aggressive disease with a poor prognosis. The present study aimed to assess the role of surgery in the treatment of patients with resectable E-NEC, and identify a microRNA (miRNA/miR) signature in association with positive postoperative outcomes. Between February 2017 and August 2019, 36 patients with E-NEC who underwent curative surgery at the Japan Neuroendocrine Tumor Society partner hospitals were enrolled in the study. A total of 16 (44.4%) patients achieved disease-free survival (non-relapse group), whereas 20 (55.6%) patients developed tumor relapse (relapse group) during the median follow-up time of 36.5 months (range, 1-242) after surgery with a 5-year overall survival rate of 100 and 10.8%, respectively (P<0.01). No clinicopathological parameters, such as histological type or TNM staging, were associated with tumor relapse. Microarray analysis of 2,630 miRNAs in 11 patients with sufficient quality RNA revealed 12 miRNAs (miR-1260a, -1260b, -1246, -4284, -612, -1249-3p, -296-5p, -575, -6805-3p, -12136, -6822-5p and -4454) that were differentially expressed between the relapse (n=6) and non-relapse (n=5) groups. Furthermore, the top three miRNAs (miR-1246, -1260a and -1260b) were associated with overall survival (P<0.01). These results demonstrated that surgery-based multidisciplinary treatment is effective in a distinct subpopulation of limited stage E-NEC. A specific miRNA gene set is suggested to be associated with treatment outcome.

The use of an artificial intelligence algorithm for circulating tumor cell detection in patients with esophageal cancer

Despite recent advances in multidisciplinary treatments of esophageal squamous cell carcinoma (ESCC), patients frequently suffer from distant metastasis after surgery. For numerous types of cancer, circulating tumor cells (CTCs) are considered predictors of distant metastasis, therapeutic response and prognosis. However, as more markers of cytopathological heterogeneity are discovered, the overall detection process for the expression of these markers in CTCs becomes increasingly complex and time consuming. In the present study, the use of a convolutional neural network (CNN)-based artificial intelligence (AI) for CTC detection was assessed using KYSE ESCC cell lines and blood samples from patients with ESCC. The AI algorithm distinguished KYSE cells from peripheral blood-derived mononuclear cells (PBMCs) from healthy volunteers, accompanied with epithelial cell adhesion molecule (EpCAM) and nuclear DAPI staining, with an accuracy of >99.8% when the AI was trained on the same KYSE cell line. In addition, AI trained on KYSE520 distinguished KYSE30 from PBMCs with an accuracy of 99.8%, despite the marked differences in EpCAM expression between the two KYSE cell lines. The average accuracy of distinguishing KYSE cells from PBMCs for the AI and four researchers was 100 and 91.8%, respectively (P=0.011). The average time to complete cell classification for 100 images by the AI and researchers was 0.74 and 630.4 sec, respectively (P=0.012). The average number of EpCAM-positive/DAPI-positive cells detected in blood samples by the AI was 44.5 over 10 patients with ESCC and 2.4 over 5 healthy volunteers (P=0.019). These results indicated that the CNN-based image processing algorithm for CTC detection provides a higher accuracy and shorter analysis time compared to humans, suggesting its applicability for clinical use in patients with ESCC. Moreover, the finding that AI accurately identified even EpCAM-negative KYSEs suggested that the AI algorithm may distinguish CTCs based on as yet unknown features, independent of known marker expression.

Position and Posture Measurement Method of the Omnidirectional Camera Using Identification Markers

Recently, many studies on unmanned aerial vehicle (UAVs) that perform position control using camera images have been conducted. The measurements of the surrounding environment and position of the mobile robot are important in controlling the UAV. The distance and direction of the optical ray to the object can be obtained from the diameter and coordinates in the image. In these studies, various camera systems using plane cameras, fisheye cameras, or omnidirectional cameras are used. Because these camera systems have different geometrical optics, one simple image position measurement method cannot yield the position and posture. Therefore, we propose a new method that measures the position from the size of three-dimensional landmarks using omnidirectional cameras. Three-dimensional measurements are performed by these omnidirectional cameras using the distance and direction to the object. This method can measure three-dimensional positions from the direction and distance of the ray; therefore, if the optical path such as the reflection or refraction is known, it can perform measurements using a different optical system’s camera. In this study, we construct a method to obtain the relative position and relative posture necessary for the self-position estimation based on an object with an omnidirectional camera; further, we verify this method by experiment.

Acquisition of Disaster Emergency Information Using a Terrain Database by Flying Robots

Because of the frequent occurrence of large-scale disasters, such as earthquakes, tsunamis, volcanic eruptions, and river floods, there is an increased demand for emergency response, restoration, and disaster prevention using robotic technology. One such technology involves assessment of the damage status using flying robots, which have undergone rapid development in recent years. In this study, using images of the disaster site obtained from a flying robot and a terrain database consisting of predisaster 3-D data, we aim to detect efficiently the collapse of electric utility poles, which are man-made objects, and the water level difference before and after river flooding, which is part of the natural landscape. By detecting these disaster-related events, we show the validity of the proposed method to assess the damage situation using the terrain database.

3D Measurement Using a Fish-Eye Camera Based on EPI Analysis

In car driving support systems and mobile robots, it is important to understand three-dimensional environment widely at once. In this paper, we use a fish-eye camera as a sensor to measure three-dimensional (3D) environments. This camera can take a wide-range and distortional image and can be easily mounted on cars. We propose a method for reconstructing 3D environment using fish-eye images based on Epipolar-Plane Image (EPI) analysis. This method enables easy and stable matching of feature points. The effectiveness of the proposed method is verified by experiments.

Mouth Movement Recognition Using Template Matching and its Implementation in an Intelligent Room

This paper proposes a method of recognizing movements of the mouth from images and implements the method in an intelligent room. The proposed method uses template matching and recognizes mouth movements for the purpose of indicating a target object in an intelligent room. First, the operator’s face is detected. Then, the mouth region is extracted from the facial region using the result of template matching with a template image of the lips. Dynamic Programming (DP) matching is applied to a similarity measure that is obtained by template matching. The effectiveness of the proposed method is evaluated through experiments to recognize several names of common home appliances and operations.

Role of Pre-Operation in Experiencing Differently Sized Hands

In this paper, we investigate pre-operations intended to adapt one to hands of controlled sizes when experiencing differently sized hands. Pre-operations are categorized into four types based on the relationship between a hand and an object. To quantify the degree of adaptation to differently sized hands, an index, called “Degree Of Immersion (DOI),” is defined. The index indicates the appropriateness of the observed behavior to the presented hand size. The DOI was measured to compare pre-operations when changing hands in size variously. The experimental comparisons led to two main points: (i) no pre-operation is required with decreasing hand size due to easy adaptation, (ii) a pre-operation, touching and controlling an object in position, is sufficiently effective for adapting larger hands. These points are important for design aid applications to assess usability of designing products by various users.

Skin Color Registration Using Recognition of Waving Hands

This paper proposes skin color registration using the recognition of waving hands. In order to recognize hand gestures from images, skin colors are useful information. The proposed method can register skin colors simply and quickly because it uses just a few waves of the hand. The method consists of 2 steps. First, the regions of the waving hands are extracted from low-resolution images without using color information. Second, the color values of the extracted regions are classified into background colors and hand colors depending on time series of color images. The color information classified as hand colors is registered as skin colors. The proposed method is robust against lighting conditions and individual differences in skin color, because the skin color is registered as an adapted skin color in each case. Several experiments are conducted to demonstrate the effectiveness of the proposed method.

Measurement of Three-Dimensional Environment with a Fish-Eye Camera Based on Structure from Motion - Error Analysis

This paper proposes a method for measuring 3-dimensional (3D) environment and estimating camera movement with two fish-eye images. This method deals with large distortion of images from a fish-eye camera to calibrate internal and external camera parameters precisely by simultaneous estimation. In this paper, we analyze 3D measurement accuracy based on a theoretical model and evaluate it in practical analysis in experimental and real environments. These analyses show that the theoretical measurement error model works over a wide range of fish-eye views.