Phylogeny,character evolution,and classification of Selaginellaceae(lycophytes)

Selaginella is the largest and most taxonomically complex genus in lycophytes.The fact that over 750 species are currently treated in a single genus makes Selaginellales/Selaginellaceae unique in pteridophytes.Here we assembled a dataset of six existing and newly sampled plastid and nuclear loci with a total of 684 accessions(74%increase of the earlier largest sampling)representing ca.300 species to infer a new phylogeny.The evolution of 10 morphological characters is studied in the new phylogenetic context.Our major results include:(1)the nuclear and plastid phylogenies are congruent with each other and combined analysis well resolved and strongly supported the relationships of all but two major clades;(2)the Sinensis group is resolved as sister to S.subg.Pulviniella with strong support in two of the three analyses;(3)most morphological characters are highly homoplasious but some characters alone or combinations of characters well define the major clades in the family;and(4)an infrafamilial classification of Selaginellaceae is proposed and the currently defined Selaginella s.l.is split into seven subfamilies(corresponding to the current six subgenera t the Sinensis group)and 19 genera(the major diagnosable clades)with nine new species-poor genera.We support the conservation of Selaginella with a new type,S.flabellata,to minimize nomenclatural instability.We provide a key to subfamilies and genera,images illustrating their morphology,their morphological and geographical synopses,a list of constituent species,and necessary new combinations.This new classification will hopefully facilitate communication,promote further studies,and help conservation.

An Efficient 3D CNN Framework with Attention Mechanisms for Alzheimer’s Disease Classification

Neurodegeneration is the gradual deterioration and eventual death of brain cells,leading to progressive loss of structure and function of neurons in the brain and nervous system.Neurodegenerative disorders,such as Alzheimer’s,Huntington’s,Parkinson’s,amyotrophic lateral sclerosis,multiple system atrophy,and multiple sclerosis,are characterized by progressive deterioration of brain function,resulting in symptoms such as memory impairment,movement difficulties,and cognitive decline.Early diagnosis of these conditions is crucial to slowing down cell degeneration and reducing the severity of the diseases.Magnetic resonance imaging(MRI)is widely used by neurologists for diagnosing brain abnormalities.The majority of the research in this field focuses on processing the 2D images extracted from the 3D MRI volumetric scans for disease diagnosis.This might result in losing the volumetric information obtained from the whole brain MRI.To address this problem,a novel 3D-CNN architecture with an attention mechanism is proposed to classify whole-brain MRI images for Alzheimer’s disease(AD)detection.The 3D-CNN model uses channel and spatial attention mechanisms to extract relevant features and improve accuracy in identifying brain dysfunctions by focusing on specific regions of the brain.The pipeline takes pre-processed MRI volumetric scans as input,and the 3D-CNN model leverages both channel and spatial attention mechanisms to extract precise feature representations of the input MRI volume for accurate classification.The present study utilizes the publicly available Alzheimer’s disease Neuroimaging Initiative(ADNI)dataset,which has three image classes:Mild Cognitive Impairment(MCI),Cognitive Normal(CN),and AD affected.The proposed approach achieves an overall accuracy of 79%when classifying three classes and an average accuracy of 87%when identifying AD and the other two classes.The findings reveal that 3D-CNN models with an attention mechanism exhibit significantly higher classification performance compared to other models,highlighting the potential of deep learning algorithms to aid in the early detection and prediction of AD.

Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of dopamine.Investigators have identified the voice as the underlying symptom of PD.Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection.Machine learning(ML)models have recently helped to solve problems in the classification of chronic diseases.This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system.It includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector machine.The feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini techniques.Random forest kerb feature selection(RFKFS)selects only 17 features from 754 attributes.The proposed technique uses validation metrics to assess the performance of ML models.The results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other models.It was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.

Cesarean Sections according to the Robson’s Classification in Two University Hospitals of Yaoundé: Indications and Maternofetal Outcome

Introduction: Cesarean section is a surgical intervention which consists in the extraction of a fetus from the uterus after its incision. The rate of cesarean section varies depending on the country and the health facility. For this reason, in 2015, the World Health Organization (WHO) recommended the use of Robson’s classification to evaluate the practice of cesarean sections in order to identify the groups of women who had abnormally high rates. The objective of our study was to evaluate cesarean sections using the Robson’s classification in CHRACERH and in the Yaoundé Central Hospital (YCH). Methodology: We carried out a retrospective cross sectional and descriptive study in two (02) university hospitals in Yaoundé which took place from December 2017 to May 2018. We included in our study all women who gave birth over a period of two (02) years from January 2016 to December 2017 in these two health facilities. Our sampling was exhaustive over the study period. The parturients’ information was collected using an anonymous and pretested questionnaire. The Robson’s group of every parturient was determined. Descriptive parameters like mean and proportions were calculated. We compared the rates and indications of cesarean sections between the both hospitals using Chi2 test. Results: Out of 330 deliveries realized in CHRACERH, we had 90 cesarean sections;hence, a rate of 27.2%. Out of 1863 deliveries carried out at the YCH, 462 were by cesarean section, hence a rate of 24.8%. The women who belonged to groups 1, 3 and 5 contributed to the highest rates of cesarean sections in both hospitals: in CHRACERH, group 5 (31.1%), group 3 (20%) and group 1 (15.6%), at YCH: group 3 (22.5%), group 1 (21.6%) and group 5 (17.3%). The indications of the cesarean sections varied depending on the Robson’s group and the hospital, the principal indication in group 1 was acute fetal distress (28.6%) in CHRACERH and cephalopelvic disproportion (36.7%) at YCH. Cephalopelvic disproportion was the predominant indication in groups 3 of CHRACERH (44.4%) and YCH (39.2%). In groups 5, CHRACERH and of YCH, a scarred uterus was the principal indication for the cesarean section at 82.4% and 78.4% respectively. At CHRACERH, the maternofetal complications were more frequent in groups 1 and 2 at the YCH, this was the case mostly in groups 1 and 3. Conclusion: The Robson’s classification is an adequate tool for the evaluation and comparison of the rates of cesarean sections. The rates of cesarean section in CHRACERH (27.2%) and at YCH (24.8%) were higher than the rates recommended by WHO. Robson’s groups 1, 3 and 5 were identified as the groups most at risk for cesarean sections in the both hospitals.

Assessment of Incidentally Detected Simple Renal Cyst in Pediatric Patient Based on the Modified Bosniak Classification —Incidentally Detected Simple Renal Cyst in Pediatric

Renal cysts in pediatric patients are uncommon lesion. A modified Bosniak classification system for renal cysts based on US has been developed to evaluate pediatric renal cysts to identify the simple cyst or cystic tumour. Nevertheless, it is not widely used. In this retrospective study, all incidentally detected renal cysts by ultrasound performed in children and the reproducibility of modified Bosniak classification to guide the radiological and clinical follow up.

肾脏囊性占位超声造影及CT增强的表现与Bosniak分级的临床应用研究

目的探讨超声造影和增强CT于肾脏囊性占位Bosniak分级的诊断及鉴别诊断的临床应用价值。方法回顾性分析2013年3月至2022年8月共50例经手术病理明确诊断的肾脏囊性占位患者临床资料,所有患者均于同时段进行超声造影与CT增强检查,并予Bosniak分级。结果手术患者中超声造影分级为Ⅰ级3例(6%),Ⅱ级10例(20%),ⅡF级10例(20%),Ⅲ级14例(28%),Ⅳ级13例(26%),CT增强分级为Ⅰ级3例(6%),Ⅱ级15例(30%),ⅡF级5例(10%),Ⅲ级12例(24%),Ⅳ级15例(30%)。此两种检查方法诊断结果均与手术金标准结果高度一致(Kappa值分别为0.841和0.682)。结论超声造影与CT增强对于肾脏囊性占位有相仿的定性诊断准确率,超声造影具有独立的临床应用优势价值。

ADASYN与类别逆比例加权法在阿尔茨海默病不平衡数据中的应用

目的利用自适应合成抽样(adaptive synthetic sampling,ADASYN)与类别逆比例加权法处理类别不平衡数据,结合分类器构建模型对阿尔茨海默病(alzheimer′s disease,AD)患者疾病进程进行分类预测。方法数据源自阿尔茨海默病神经影像学计划(Alzheimer′s disease neuroimaging initiative,ADNI),经随机森林填补缺失值,弹性网络筛选特征子集后,利用ADASYN与类别逆比例加权法处理类别不平衡数据。分别结合随机森林(random forest,RF)、支持向量机(support vector machine,SVM)构建四种模型:ADASYN-RF、ADASYN-SVM、加权随机森林(weighted random forest,WRF)、加权支持向量机(weighted support vector machine,WSVM),与RF、SVM比较分类性能。模型评价指标为宏观平均精确率(macro-average of precision,macro-P)、宏观平均召回率(macro-average of recall,macro-R)、宏观平均F1值(macro-average of F1-score,macro-F1)、准确率(accuracy,ACC)、Kappa值和AUC(area under the ROC curve)。结果ADASYN-RF的分类性能最优(Kappa值为0.938,AUC为0.980),ADASYN-SVM次之。利用ADASYN-RF预测得到的重要分类特征分别为CDRSB、LDELTOTAL、MMSE,在临床上均可得到证实。结论ADASYN与类别逆比例加权法都能辅助提升分类器性能,但ADASYN算法更优。

CT评估2019版Bosniak分级系统对肾囊性病变的诊断价值

目的评估2019版Bosniak分级系统(Bosniak.v2019)对提高肾脏囊性病变影像诊断准确率的价值。方法选取经手术治疗的肾脏囊性病变患者的病理和影像学资料,共纳入411例患者,发现囊性病变共495枚,2位医师分别使用2005版(Bosniak.v2005)和2019版Bosniak(Bosniak.v2019)分级系统对囊性病变进行影像学评估。使用Kappa检验评估2位医师评分结果间的一致性;使用ROC曲线评估Bosniak.v2005、Bosniak.v2019对肾恶性囊性病变的诊断效能;采用卡方检验对各级分级评分进行分析。结果Bosniak.v2019的一致性较Bosniak.v2005有所提高(Kappa系数全部病变为0.851、0.887,肾恶性囊性病变为0.878、0.917);Bosniak.v2005、Bosniak.v2019诊断恶性囊性病变的敏感度差异无统计学意义(P>0.05);Bosniak.v2019诊断肾恶性病变的特异度、准确度和AUC值均高于Bosniak.v2005(P均<0.05)。Bosniak.v2019中对Ⅰ、Ⅱ类病变诊断更为精确,Ⅱ型检出恶性率更高,对ⅡF~IV各级的诊断准确性也有所提高;因有25例直接排除囊性病变(诊断为癌),Bosniak.v2019诊断非肿瘤性囊性病变更可靠。结论Bosniak.v2019较Bosniak.v2005一致性更好,诊断效能更高,Bosniak.v2019应用于肾囊性病变的诊断,有助于临床诊疗决策。

Diagnostic classification of endosonography for differentiating colorectal ulcerative diseases: A new statistical method

AIM To establish a classification method for differential diagnosis of colorectal ulcerative diseases, especially Crohn's disease(CD), primary intestinal lymphoma(PIL) and intestinal tuberculosis(ITB).METHODS We searched the in-patient medical record database for confirmed cases of CD, PIL and ITB from 2008 to 2015 at our center, collected data on endoscopic ultrasound(EUS) from randomly-chosen patients who formed the training set, conducted univariate logistic regression analysis to summarize EUS features of CD, PIL and ITB, and created a diagnostic classification method. All cases found to have colorectal ulcers using EUS were obtained from the endoscopy database and formed the test set. We then removed the cases which were easily diagnosed, and the remaining cases formed the perplexing test set. We re-diagnosed the cases in the three sets using the classification method, determined EUS diagnostic accuracies, and adjusted the classification accordingly. Finally, the re-diagnosing and accuracy-calculating steps were repeated.RESULTS In total, 272 CD, 60 PIL and 39 ITB cases were diagnosed from 2008 to 2015 based on the in-patient database, and 200 CD, 30 PIL and 20 ITB cases were randomly chosen to form the training set. The EUS features were summarized as follows: CD: Thickened submucosa with a slightly high echo level and visible layer; PIL: Absent layer and diffuse hypoechoic mass; and ITB: Thickened mucosa with a high or slightly high echo level and visible layer. The test set consisted of 77 CD, 30 PIL, 23 ITB and 140 cases of other diseases obtained from the endoscopy database. Seventy-four cases were excluded to form the perplexing test set. After adjustment of the classification, EUS diagnostic accuracies for CD, PIL and ITB were 83.6%(209/250), 97.2%(243/250) and 85.6%(214/250) in the training set, were 89.3%(241/270), 97.8%(264/270) and 84.1%(227/270) in the test set, and were 86.7%(170/196), 98.0%(192/196) and 85.2%(167/196) in the perplexing set, respectively.CONCLUSION The EUS features of CD, PIL and ITB are different. The diagnostic classification method is reliable in the differential diagnosis of colorectal ulcerative diseases.

Classification of Short Time Series in Early Parkinson’s Disease With Deep Learning of Fuzzy Recurrence Plots

There are many techniques using sensors and wearable devices for detecting and monitoring patients with Parkinson’s disease(PD).A recent development is the utilization of human interaction with computer keyboards for analyzing and identifying motor signs in the early stages of the disease.Current designs for classification of time series of computer-key hold durations recorded from healthy control and PD subjects require the time series of length to be considerably long.With an attempt to avoid discomfort to participants in performing long physical tasks for data recording,this paper introduces the use of fuzzy recurrence plots of very short time series as input data for the machine training and classification with long short-term memory(LSTM)neural networks.Being an original approach that is able to both significantly increase the feature dimensions and provides the property of deterministic dynamical systems of very short time series for information processing carried out by an LSTM layer architecture,fuzzy recurrence plots provide promising results and outperform the direct input of the time series for the classification of healthy control and early PD subjects.

超声造影联合2019版Bosniak分级对肾脏囊性占位的诊断效能

目的探讨超声造影(contrast-enhanced ultrasound,CEUS)联合2019版Bosniak分级(B_(2019))在肾脏囊性占位(cystic renal mass,CRM)诊断中的应用价值。方法分析2011年10月至2021年10月复旦大学附属中山医院收治的经手术病理证实的99例CRM患者共99个病灶的病例资料,所有病灶均分别按照B_(2019)、2005版Bosniak分级(B_(2005))进行评价。采用ROC曲线评价CEUS+B_(2019)和CEUS+B_(2005)对CRM的诊断效能。结果CEUS+B_(2019)及CEUS+B_(2005)诊断CRM的曲线下面积(area under curve,AUC)分别为0.943、0.885,差异有统计学意义(P<0.05)。CEUS+B_(2019)诊断CRM的特异度、准确度分别为97.80%、93.94%,明显高于CEUS+B_(2005)(84.40%、88.89%),灵敏度为90.70%,低于CEUS+B_(2005)(92.60%),差异均有统计学意义(均P<0.05)。CEUS+B_(2019)、CEUS+B_(2005)诊断CRM的Kappa值分别为0.879、0.775。结论在CRM的诊断中,CEUS+B_(2019)较CEUS+B_(2005)有更高的特异度、准确度,有助于避免良性CRM的误诊,减少不必要的随访及过度治疗。

Classification and 3-D distribution of upper layer water masses in the northern South China Sea

Using the fuzzy cluster analysis and the temperature-salinity(T-S) similarity number analysis of cruise conductivity-temperature-depth(CTD) data in the upper layer(0–300 m) of the northern South China Sea(NSCS), we classify the upper layer water of the NSCS into six water masses: diluted water(D), surface water(SS),the SCS subsurface water mass(U_S), the Pacific Ocean subsurface water mass(U_P), surface-subsurface mixed water(SU) and subsurface-intermediate mixed water(UI). A new stacked stereogram is used to illustrate the water mass distribution, and to examine the source and the distribution of U_P, combining with the sea surface height data and geostrophic current field. The results show that water mass U_P exists in all four seasons with the maximum range in spring and the minimum range in summer. In spring and winter, the U_P intrudes into the Luzon Strait and the southwest of Taiwan Island via the northern Luzon Strait in the form of nonlinear Rossby eddies, and forms a high temperature and high salinity zone east of the Dongsha Islands. In summer, the U_P is sporadically distributed in the study area. In autumn, the U_P is located in the upper 200 m layer east of Hainan Island.

Performance of the Montreal classification for inflammatory bowel diseases

AIM:To validate the Montreal classification system for Crohn's disease(CD) and ulcerative colitis(UC) within the Netherlands.METHODS:A selection of 20 de-identified medical records with an appropriate representation of the inflammatory bowel disease(IBD) sub phenotypes were scored by 30 observers with different professions(gastroenterologist specialist in IBD,gastroenterologist in training and IBD-nurses) and experience level with IBD patient care.Patients were classified according to the Montreal classification.In addition,participants were asked to score extra-intestinal manifestations(EIM) and disease severity in CD based on their clinical judgment.The inter-observer agreement was calculated by percentages of correct answers(answers identical to the "expert evaluation") and Fleiss-kappa(k).Kappa cutoffs:< 0.4-poor; 0.41-0.6-moderate; 0.61-0.8-good; > 0.8 excellent.RESULTS:The inter-observer agreement was excellent for diagnosis(k = 0.96),perianal disease(k = 0.92) and disease location in CD(k = 0.82) and good for age of onset(k = 0.67),upper gastrointestinal disease(k = 0.62),disease behaviour in CD(k = 0.79) and disease extent in UC(k = 0.65).Disease severity in UC was scored poor(k = 0.23).The additional items resulted in a good inter-observer agreement for EIM(k = 0.68) and a moderate agreement for disease severity in CD(k = 0.44).Percentages of correct answers over all Montreal items give a good reflection of the inter-observer agreement(> 80%),except for disease severity(48%-74%).IBD-nurses were significantly worse in scoring upper gastrointestinal disease in CD compared to gastroenterologists(P = 0.008) and gastroenterologists in training(P = 0.040).Observers with less than 10 years of experience were significantly better at scoring UC severity than observers with 10-20 years(P = 0.003) and more than 20 years(P = 0.003) of experience with IBD patient care.Observers with 10-20 years of experience with IBD patient care were significantly better at scoring upper gastrointestinal disease in CD than observers with less than 10 years(P = 0.007) and more than 20 years(P = 0.007) of experience with IBD patient care.CONCLUSION:We found a good to excellent interobserver agreement for all Montreal items except for disease severity in UC(poor).

DTI and Structural MRI Classification in Alzheimer’s Disease

In this paper, we propose a fully automated method to individually classify patients with Alzheimer’s disease (AD) and elderly control subjects based on diffusion tensor (DTI) and anatomical magnetic resonance imaging (MRI). We propose a new multimodal measure that combines anatomical and diffusivity measures at the voxel level. Our approach relies on whole-brain parcellation into 73 anatomical regions and the extraction of multimodal characteristics in these regions. Discriminative features are identified using different feature selection (FS) methods and used in a Support Vector Machine (SVM) for individual classification. Fifteen AD patients and 16 elderly controls were discriminated using mean diffusivity alone, combination of mean diffusivity and fractional anisotropy, and multimodal measures in the 73 ROIs and the overall accuracy obtained was 65.2%, 68.6% and 72% respectively. Overall accuracy reached 99% in multimodal measures when relevant regions were selected.

Clinical Observation and Proposed Classification of Vitiliginous Patches by a Wood’s Lamp

Although vitiligo lesion especially in static state is characterized as sharply demarcated and complete depigmented macule, we encounter patients who have various manners of hypopigmented lesions. We examined the 81 lesions using the newly released Wood’s lamp (Woody®) and investigated whether or not vitiliginous lesions could be uniformly classified under Wood’s lamp illumination and also this classification helped to estimate the tendency of repigmentation after treatment. As result, the vitiliginous lesions were categorized into 4 types on intra- and peri-lesions prior to treatment by using the Wood’s lamp. The inside and border of the lesions were classified as follows: clear white, faint, multi-dot, and perifollicular for the inside, and sharp, blunt, confetti, and trichrome for the border. Suggestive residual pigmentation was detected in 73.6% of patients at the first visit and repigmentation was observed in 67.9% of patients at least 3 months after treatment. Lesions with the “clear white” inside pattern showed significantly lower repigmentation frequency in 38.5% of patients compared to others. The borders with 4 enlarged lesions were composed of 3 of confetti-type and one of sharp-type. This preliminary study demonstrated that detailed observation with a Wood’s lamp could be the basis to classify vitiliginous lesions and might be useful for predicting not only disease progression but also repigmentation prior to treatment.

Extracting Multiple Nodes in a Brain Region of Interest for Brain Functional Network Estimation and Classification

Purpose: Brain functional networks (BFNs) has become important approach for diagnosis of some neurological or psychological disorders. Before estimating BFN, obtaining blood oxygen level dependent (BOLD) representative signals from brain regions of interest (ROIs) is important. In the past decades, the common method is generally to take a ROI as a node, averaging all the voxel time series inside it to extract a representative signal. However, one node does not represent the entire information of this ROI, and averaging method often leads to signal cancellation and information loss. Inspired by this, we propose a novel model extraction method based on an assumption that a ROI can be represented by multiple nodes. Methods: In this paper, we first extract multiple nodes (the number is user-defined) from the ROI based on two traditional methods, including principal component analysis (PCA), and K-means (Clustering according to the spatial position of voxels). Then, canonical correlation analysis (CCA) was issued to construct BFNs by maximizing the correlation between the representative signals corresponding to the nodes in any two ROIs. Finally, to further verify the effectiveness of the proposed method, the estimated BFNs are applied to identify subjects with autism spectrum disorder (ASD) and mild cognitive impairment (MCI) from health controls (HCs). Results: Experimental results on two benchmark databases demonstrate that the proposed method outperforms the baseline method in the sense of classification performance. Conclusions: We propose a novel method for obtaining nodes of ROId based on the hypothesis that a ROI can be represented by multiple nodes, that is, to extract the node signals of ROIs with K-means or PCA. Then, CCA is used to construct BFNs.

Multimodal 3D Convolutional Neural Networks for Classification of Brain Disease Using Structural MR and FDG-PET Images

The classification and identification of brain diseases with multimodal information have attracted increasing attention in the domain of computer-aided. Compared with traditional method which use single modal feature information, multiple modal information fusion can classify and diagnose brain diseases more comprehensively and accurately in patient subjects. Existing multimodal methods require manual extraction of features or additional personal information, which consumes a lot of manual work. Furthermore, the difference between different modal images along with different manual feature extraction make it difficult for models to learn the optimal solution. In this paper, we propose a multimodal 3D convolutional neural networks framework for classification of brain disease diagnosis using MR images data and PET images data of subjects. We demonstrate the performance of the proposed approach for classification of Alzheimer’s disease (AD) versus mild cognitive impairment (MCI) and normal controls (NC) on the Alzheimer’s Disease National Initiative (ADNI) data set of 3D structural MRI brain scans and FDG-PET images. Experimental results show that the performance of the proposed method for AD vs. NC, MCI vs. NC are 93.55% and 78.92% accuracy respectively. And the accuracy of the results of AD, MCI and NC 3-classification experiments is 68.86%.

超声造影结合Bosniak分级诊断囊性肾癌的可行性

目的探讨超声造影结合Bosniak分级诊断囊性肾癌的可行性。方法回顾性分析经手术病理证实的24例囊性肾癌的常规超声及超声造影表现,评估指标包括囊隔数目、囊隔或囊壁厚度、有无实性结节以及囊壁、囊隔或结节处血供情况。根据超声造影声像图特征进行Bosniak分级并统计其诊断符合率。结果常规超声声像图表现:2个病灶内未见分隔,8个见少量分隔,14个见多条分隔;5个囊壁或囊隔厚度≤1 mm,19个>1 mm;7个病灶内见实性结节;11个病灶的囊壁、囊隔或结节处可见彩色血流信号。超声造影声像图表现:8个病灶内见少量分隔,16个见多条分隔;1个囊壁或囊隔厚度≤1 mm,23个>1 mm;9个病灶内见实性结节;所有病灶的囊壁、囊隔或结节处均可见造影剂灌注增强。Bosniak分级示Ⅲ级15个,Ⅳ级9个,诊断符合率为100%。结论与常规超声相比,超声造影能清晰显示囊性肾癌的血供和复杂的内部结构,超声造影结合Bosniak分级可用于诊断囊性肾癌。

多层螺旋CT结合Bosniak分级在肾囊性肿物诊断中的价值

目的评价多层螺旋CT（MSCT）结合Bosniak分级对肾囊性肿物的诊断价值。资料与方法回顾性分析经手术、病理证实的109例患者共129个肾囊性占位术前MSCT的影像表现，并根据肾囊性占位的CT征象对其进行Bosniak分级；将MSCT的Bosniak分级结果与病理进行对照分析，判断各个级别中恶性囊肿的发生率；采用lo—gistic回归分析预测Ⅰ～Ⅳ级中恶性肾囊肿的可靠CT征象。结果129个囊性肿物中，17个为恶性，112个为良性。对129个Ⅰ-Ⅳ级囊性病灶的CT特征进行logistic回归分析，结果显示实性成分强化是预测恶性肾囊肿的唯一可靠征象。结论高分辨CT结合Bosniak分级对大部分囊性肿物能够准确诊断；实性成分强化是预测恶性囊性肿物的唯一可靠征象。

超声造影结合Bosniak标准在良恶性肾囊性病变鉴别诊断中的价值

目的评价超声造影鉴别良恶性肾囊性病变的准确性,探讨超声造影结合Bosniak标准分级诊断肾囊性病变的可行性。资料与方法对30例患者的36个肾囊性病灶进行超声造影,分析良恶性肾囊性病变的超声造影征象;按照Bosniak标准对超声造影的表现进行分级诊断,计算其敏感性、特异性、诊断符合率、阳性预测值、阳性似然比、Kappa值及验后概率,分析该方法分级诊断肾囊性病变的准确性。结果超声造影可显示常规超声不能发现的囊内分隔及实性结构。超声造影结合Bosniak标准诊断肾囊性病变的敏感性、特异性、诊断符合率、阳性预测值、阳性似然比分别为92.9%、90.9%、94.8%、86.7%、10.2,Kappa=0.83;应用超声造影结合Bosniak标准可提高肾囊性病变良恶性诊断的验后概率。结论超声造影结合Bosniak标准可以提高鉴别诊断肾囊性病变良恶性的准确性。