Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma

PurposeTo compare morphological imaging features and CT texture histogram parameters between grade 3 pancreatic neuroendocrine tumors (G3-NET) and neuroendocrine carcinomas (NEC).Materials and methodsPatients with pathologically proven G3-NET and NEC, according to the 2017 World Health Organization classification who had CT and MRI examinations between 2006-2017 were retrospectively included. CT and MRI examinations were reviewed by two radiologists in consensus and analyzed with respect to tumor size, enhancement patterns, hemorrhagic content, liver metastases and lymphadenopathies. Texture histogram analysis of tumors was performed on arterial and portal phase CT images. images. Morphological imaging features and CT texture histogram parameters of G3-NETs and NECs were compared.ResultsThirty-seven patients (21 men, 16 women; mean age, 56 ± 13 [SD] years [range: 28-82 years]) with 37 tumors (mean diameter, 60 ± 46 [SD] mm) were included (CT available for all, MRI for 16/37, 43%). Twenty-three patients (23/37; 62%) had NEC and 14 patients (14/37; 38%) had G3-NET. NECs were larger than G3-NETs (mean, 70 ± 51 [SD] mm [range: 18 - 196 mm] vs. 42 ± 24 [SD] mm [range: 8 - 94 mm], respectively; P = 0.039), with more tumor necrosis (75% vs. 33%, respectively; P = 0.030) and lower attenuation on precontrast (30 ± 4 [SD] HU [range: 25-39 HU] vs. 37 ± 6 [SD] [range: 25-45 HU], respectively; P = 0.002) and on portal venous phase CT images (75 ± 18 [SD] HU [range: 43 - 108 HU] vs. 92 ± 19 [SD] HU [range: 46 - 117 HU], respectively; P = 0.014). Hemorrhagic content on MRI was only observed in NEC (P = 0.007). The mean ADC value was lower in NEC ([1.1 ± 0.1 (SD)] × 10⁻³ mm²/s [range: (0.91 - 1.3) × 10⁻³ mm²/s] vs. [1.4 ± 0.2 (SD)] × 10⁻³ mm²/s [range: (1.1 - 1.6) × 10⁻³ mm²/s]; P = 0.005). CT histogram analysis showed that NEC were more heterogeneous on portal venous phase images (Entropy-0: 4.7 ± 0.2 [SD] [range: 4.2-5.1] vs. 4.5 ± 0.4 [SD] [range: 3.7-4.9]; P = 0.023).ConclusionPancreatic NECs are larger, more frequently hypoattenuating and more heterogeneous with hemorrhagic content than G3-NET on CT and MRI.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

Sensory and motor aspects of saccade control

Summary The oculomotor reactions have recently attracted increasing attention for diagnostic purposes. This is in line with the view that the oculomotor system is one of the simpler, machine-like sensorimotor systems. This paper presents two examples to demonstrate that the complexity of sensorimotor processing may be higher than expected from the outcome of experiments under rather restricted stimulus conditions. In the first part it is shown that complex preprocessing including the evaluation of spatial gradients of visual structure is an integral part of the programming of reflex-like saccades. The second part concerns adaptivity of saccadic eye movements. The data demonstrate that saccadic gain control is highly specific to the direction of the saccade. It is suggested that many central deficits may be hidden as a consequence of the effect of specific adaptive mechanisms.This article was presented in part at the Symposium on Eye Movements and Psychopathology, Berlin, 23–24 June 1988     

Cytological detection of epithelial dysplasia in the oral mucosa using Feulgen-DNA-image cytometry

Cytological scrape material of the oral mucosa from 114 patients with epithelial dysplasia and with oral cancer was stained with the Feulgen-reaction and investigated with an image analyzer. The size and the integrated optical density of cell nuclei, and four chromatin texture features were measured. All tumor slides contained cell nuclei with DNA greater than 5c, 16% of the slides had cell nuclei with DNA greater than 8c. A total of 14.5% of the tumor patients showed significantly increased DNA values in nuclei distant from the tumor. Two smears with severe epithelial dysplasia showed nuclei with DNA greater than 5c both in the tumor material and far from the tumor. Texture analysis allowed discrimination between benign, dysplastic and malignant smears. No correlation was found between DNA content and tumor staging. Image cytometry was a reliable method for detecting tumor cells. Epithelial dysplasia in areas distant from the tumor is probably due to "field canceration" of the epithelium.     

大肠腺癌DNA和RNA测定与转移及预后的关系

探讨大肠腺癌DNA和RNA含量的变化意义及预后的关系。方法 :对我院 1985年～ 1992年收治的术后随访 74例病人的大肠腺癌石腊标本重新切片 ,用流式细胞技术进行DNA和RNA含量测定 ,探讨其变化意义与转移预后的关系。结果 :大肠腺癌DNA异倍体与组织学类型无关 ,RNA含量与组织学类型关系密切。DNA异倍体和RNA含量大肠腺癌的Dukes’分期、淋巴结转移、其它脏器转移和根治术后复发均有关。术后生存 5年以上病人的DNA异倍体出现率和RNA含量较术后 1年内死亡病人为低 ,两者有显著性差异 (P <0 .0 1)。结论 :DNA和RNA含量测定可作为判断大肠腺癌的恶性程度、估价预后的客观可靠指标。     

B超图像纹理的复杂性分析

本文提出一种复杂性度量法对肝脏Ｂ超图像纹理的分类进行了研究,图像的灰度值复杂性作为纹理特征。     

A neural model of 3D shape-from-texture: multiple-scale filtering, boundary grouping, and surface filling-in

A neural model is presented of how cortical areas V1, V2, and V4 interact to convert a textured 2D image into a representation of curved 3D shape. Two basic problems are solved to achieve this: (1) Patterns of spatially discrete 2D texture elements are transformed into a spatially smooth surface representation of 3D shape. (2) Changes in the statistical properties of texture elements across space induce the perceived 3D shape of this surface representation. This is achieved in the model through multiple-scale filtering of a 2D image, followed by a cooperative-competitive grouping network that coherently binds texture elements into boundary webs at the appropriate depths using a scale-to-depth map and a subsequent depth competition stage. These boundary webs then gate filling-in of surface lightness signals in order to form a smooth 3D surface percept. The model quantitatively simulates challenging psychophysical data about perception of prolate ellipsoids [Todd, J., & Akerstrom, R. (1987). Perception of three-dimensional form from patterns of optical texture. Journal of Experimental Psychology: Human Perception and Performance, 13(2), 242-255]. In particular, the model represents a high degree of 3D curvature for a certain class of images, all of whose texture elements have the same degree of optical compression, in accordance with percepts of human observers. Simulations of 3D percepts of an elliptical cylinder, a slanted plane, and a photo of a golf ball are also presented.     

Prediction of oral disintegration time of fast disintegrating tablets using texture analyzer and computational optimization

One of the promising approaches to predict in vivo disintegration time of orally disintegrating tablets (ODT) is the use of texture analyzer instrument. Once the method is able to provide good in vitro in vivo correlation (IVIVC) in the case of different tablets, it might be able to predict the oral disintegration time of similar products. However, there are many tablet parameters that influence the in vivo and the in vitro disintegration time of ODT products. Therefore, the measured in vitro and in vivo disintegration times can occasionally differ, even if they coincide in most cases of the investigated products and the in vivo disintegration times may also change if the aimed patient group is suffering from a special illness. If the method is no longer able to provide good IVIVC, then the modification of a single instrumental parameter may not be successful and the in vitro method must be re-set in a complex manner in order to provide satisfactory results. In the present experiment, an optimization process was developed based on texture analysis measurements using five different tablets in order to predict their in vivo disintegration times, and the optimized texture analysis method was evaluated using independent tablets.     

基于CT平扫纹理分析的影像组学预测肾透明细胞癌WHO/ISUP分级的初步研究

目的:探讨基于肾脏CT平扫图像纹理分析的影像组学模型在预测肾透明细胞癌（ccRCC）WHO/国际泌尿病理学会（ISUP）病理分级中的价值。方法:回顾性分析2016年12月至2019年5月中国科学院大学附属肿瘤医院经手术病理证实且有明确病理分级的90例ccRCC患者,按照7∶3的比例将所有患者随机分为训练组（63例）及测...