ABSTRACT
BACKGROUND: Endoscopic ultrasonography (EUS) is recommended as the best tool for evaluating gastric subepithelial lesions (SELs); nonetheless, it has difficulty distinguishing gastrointestinal stromal tumors (GISTs) from leiomyomas and schwannomas. GISTs have malignant potential, whereas leiomyomas and schwannomas are considered benign. PURPOSE: This study aimed to establish a combined radiomic model based on EUS images for distinguishing GISTs from leiomyomas and schwannomas in the stomach. METHODS: EUS images of pathologically confirmed GISTs, leiomyomas, and schwannomas were collected from five centers. Gastric SELs were divided into training and testing datasets based on random split-sample method (7:3). Radiomic features were extracted from the tumor and muscularis propria regions. Principal component analysis, least absolute shrinkage, and selection operator were used for feature selection. Support vector machine was used to construct radiomic models. Two radiomic models were built: the conventional radiomic model included tumor features alone, whereas the combined radiomic model incorporated features from the tumor and muscularis propria regions. RESULTS: A total of 3933 EUS images from 485 cases were included. For the differential diagnosis of GISTs from leiomyomas and schwannomas, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve were 74.5%, 72.2%, 78.7%, and 0.754, respectively, for the EUS experts; 76.8%, 74.4%, 81.0%, and 0.830, respectively, for the conventional radiomic model; and 90.9%, 91.0%, 90.6%, and 0.953, respectively, for the combined radiomic model. For gastric SELs <20 mm, the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the combined radiomic model were 91.4%, 91.6%, 91.1%, and 0.960, respectively. CONCLUSIONS: We developed and validated a combined radiomic model to distinguish gastric GISTs from leiomyomas and schwannomas. The combined radiomic model showed better diagnostic performance than the conventional radiomic model and could assist EUS experts in non-invasively diagnosing gastric SELs, particularly gastric SELs <20 mm.
Subject(s)
Gastrointestinal Stromal Tumors , Leiomyoma , Neurilemmoma , Stomach Neoplasms , Humans , Gastrointestinal Stromal Tumors/diagnostic imaging , Gastrointestinal Stromal Tumors/pathology , Endosonography , Stomach Neoplasms/diagnostic imaging , Leiomyoma/diagnostic imaging , Leiomyoma/pathology , Neurilemmoma/diagnostic imaging , Stomach/pathologyABSTRACT
Establishing the remote sensing yield estimation model of wheat-maize rotation cultivated land can timely and accurately estimate the comprehensive grain yield. Taking the winter wheat-summer maize rotation cultivated land in Caoxian County, Shandong Province, as test object, using the Sentinel-2 images from 2018 to 2019, we compared the time-series feature classification based on QGIS platform and support vector machine algorithm to select the best method and extract sowing area of wheat-maize rotation cultivated land. Based on the correlation between wheat and maize vegetation index and the statistical yield, we screened the sensitive vegetation indices and their growth period, and obtained the vegetation index integral value of the sensitive spectral period by using the Newton-trapezoid integration method. We constructed the multiple linear regression and three machine learning (random forest, RF; neural network model, BP; support vector machine model, SVM) models based on the integral value combination to get the best and and optimized yield estimation model. The results showed that the accuracy rate of extracting wheat and maize sowing area based on time-series features using QGIS platform reached 94.6%, with the overall accuracy and Kappa coefficient were 5.9% and 0.12 higher than those of the support vector machine algorithm, respectively. The remote sensing yield estimation in sensitive spectral period was better than that in single growth period. The normalized differential vegetation index and ratio vegetation index integral group of wheat and enhanced vegetation index and structure intensify pigment vegetable index integral group of maize could more effectively aggregate spectral information. The optimal combination of vegetation index integral was difference, and the fitting accuracy of machine learning algorithm was higher than that of empirical statistical model. The optimal yield estimation model was the difference value group-random forest (DVG-RF) model of machine learning algorithm (R2=0.843, root mean square error=2.822 kg·hm-2), with a yield estimation accuracy of 93.4%. We explored the use of QGIS platform to extract the sowing area, and carried out a systematical case study on grain yield estimation method of wheat-maize rotation cultivated land. The established multi-vegetation index integral combination model was effective and feasible, which could improve accuracy and efficiency of yield estimation.
Subject(s)
Triticum , Zea mays , Remote Sensing Technology/methods , Edible Grain , ChinaABSTRACT
It is objective needs during utilization and management of regional cultivated land resource to use remote sensing to accurately and efficiently retrieve the status of cultivated land fertility at county level and realize the gradation of cultivated land rapidly. In this study, with Dongping County as a case, using Landsat TM satellite imagery and cultivated land fertility evaluation data, the moisture vegetation fertility index (MVFI) was constructed based on surface water capacity index (SWCI) and normalized difference vegetation index (NDVI), and then the optimal inversion model was optimized to obtain the best inversion model, which was further applied and verified at the county scale. The results showed that the correlation coefficient between MVFI and integrated fertility index (IFI) was -0.753, which could comprehensively reflect the growth of winter wheat, soil moisture and land fertility, and had clear biophysical significance. The best inversion model was the quadratic model, with high inversion accuracy. This model was suitable for the inversion of cultivated land fertility in the county. The spatial distribution and uniformity of the inversion results were similar to the results of soil fertility evaluation. The area differences between the high, medium and low grades were all less than 2.9%. This study provided a remote sensing inversion method of cultivated land fertility based on the feature space theory, which could effectively improve the evaluation efficiency and prediction accuracy of cultivated land fertility at the county scale.
Subject(s)
Remote Sensing Technology , Water , Satellite Imagery , Seasons , SoilABSTRACT
Soil salinization severely hinders the development of agricultural economy in the Yellow River Delta. Clarifying the spatial variability of soil salinity at multiple scales in the field is of great significance for the improvement and utilization of saline soils and agricultural production. In this study, by dividing the three dimensions of field, plot and ridge, we collceted 152 sets of conducti-vity data through field survey sampling in a summer maize field in Kenli County of the Yellow River delta. The methods of classic statistics, geostatistics and Kriging interpolation were used to analyze the spatial variability and scale effects of multi-scale soil salt in the field. The results showed that soil in this area was moderately salinized, with the extent of soil salinity moderately varying at three scales. From the field, plot to the ridge scale, with the decreases of sampling scale, the variability of soil salinity increased and the standard deviation increased. The ridge and plot scales showed strong spatial correlation. The optimal model was Gaussian model, which was mainly affected by structural factors. The field scale was of medium spatial correlation, with exponential model as the optimal one, which was influenced by both random factors and structural factors. The spatial distribution characteristics of soil salinity at different scales were significantly different. The spatial chara-cteristics at small scale were masked at large scale, showing obvious scale effect. The distribution of soil salinity at the micro-ridge scale between ridges had obvious variation. Soil salt content gradually decreased with the micro-topography from high to low, while vegetation coverage changed from sparse to dense.
Subject(s)
Rivers , Soil , Agriculture , China , Salinity , SeasonsABSTRACT
This study chooses the core demonstration area of 'Bohai Barn' project as the study area, which is located in Wudi, Shandong Province. We first collected near-ground and multispectral images and surface soil salinity data using ADC portable multispectral camera and EC110 portable salinometer. Then three vegetation indices, namely NDVI, SAVI and GNDVI, were used to build 18 models respectively with the actual measured soil salinity. These models include linear function, exponential function, logarithmic function, exponentiation function, quadratic function and cubic function, from which the best estimation model for soil salinity estimation was selected and used for inverting and analyzing soil salinity status of the study area. Results indicated that all models mentioned above could effectively estimate soil salinity and models using SAVI as the dependent variable were more effective than the others. Among SAVI models, the linear model (Y = -0.524x + 0.663, n = 70) is the best, under which the test value of F is the highest as 141.347 at significance test level, estimated R2 0.797 with a 93.36% accuracy. Soil salinity of the study area is mainly around 2.5 per thousand - 3.5 per thousand, which gradually increases from southwest to northeast. The study has probed into soil salinity estimation methods based on near-ground and multispectral data, and will provide a quick and effective technical soil salinity estimation approach for coastal saline soil of the study area and the whole Yellow River Delta.
ABSTRACT
OBJECTIVE: To observe the anti-tumor effect of silencing the expression of HIF-1alpha on cervical cancer in nude mice and to explore its mechanism of action. METHODS: Human cervical cancer cell line Siha cells were divided into 3 groups: mock control group, control group transfected with scrambled sequence plasmid, and experimental group transfected with pU-HIF-1alpha-shRNA eukaryotic expression plasmid. Cultured cells of the three groups were inoculated in nude mice to establish cervical cancer-bearing nude mice. HIF-1alpha RNAi assay was performed to evaluate the tumor-suppressive effect of HIF-1alpha silencing on cervical cancer-bearing nude mice. Immunohistochemistry and Western blot were used to observe the distribution and protein expression of HIF-1alpha and GLUT1, while RT-PCR was adopted to detect the gene expression of HIF-1alpha, GLUT1 and HKII. The product of glycolysis (lactic acid) and apoptosis in tumor cells were detected by colorimetry and semi-quantitative TUNEL staining, respectively. RESULTS: The tumor growth in experimental group was significantly slower than that in the two control groups (P < 0.05). On the 50th day after transplantation, the tumor weight in the experimental group was (1.90 +/- 0.28) g, significantly lower than (2.95 +/- 0.77) g in the control group and (2.54 +/- 0.56) g in the mock group (P < 0.01). In the experimental group, the gene and protein levels of HIF-1alpha were 0.45 +/- 0.04 and 1.25 +/- 0.92, and the levels of GLUT1 were 0.32 +/- 0.02 and 1.25 +/- 0.48, respectively. Both indicators in HIF-1alpha and GLUT1 were lower than that in the two control groups (P < 0.05). The expression levels of HKII gene and lactic acid in the experimental group were lower than that in the two control groups (P < 0.05), but the apoptotic cells were much more numerous in the experimental group than that in matched control groups (P < 0.01). CONCLUSION: The gene therapy by siRNA targeted silencing of HIF-1alpha may down-regulate its downstream genes GLUT1 and HKII expression, therefore, to reduce the tumor glycolysis activity and promote tumor cell apoptosis, and exert a tumor-suppressing effect in vivo.
