基于模糊贝叶斯网络的星形细胞瘤智能分级模型

本研究提出一种新的融合影像低层视觉特征和语义的模糊贝叶斯网络模型。使用了高斯混合模型（GMM）对连续的视觉特征模糊化处理，解决了传统贝叶斯网络小能操作连续输入的问题，更合理地表达了具有模糊性、不确定性的专业领域的结构性知识。为了验证它的有效性，将它应用于星形细胞瘤恶性程度的分级。建立了一个概率模型。实验结果得出83．33％的正确识别率。该模型为星形细胞瘤恶性程度预测提供了新的定量而客观的辅助手段。     

基于超声影像组学的原发性肝细胞癌分级预测

目的:针对原发性肝细胞癌（HCC）肿瘤分级预测难题，提出一种基于灰阶超声成像的影像组学预测模型。方法:首先，由超声医生对肿瘤区域进行手动分割，其次，采用影像组学方法对肿瘤区域提取形状、一阶统计、纹理特征，计算特征间Pearson相关系数剔除冗余特征，最后通过单变量分析筛选得到特征子集，采用LASSO构建HCC分级预测模型；利用留一法计算模型的受试者操作特性曲线下的面积（AUC）评估模型对HCC分级的预测能力。结果:利用43例经手术病理证实的HCC患者的灰阶超声图像构建HCC分级预测模型，所建模型由6个与分级高度相关的影像特征组成，模型具有较强的预测能力（AUC=0.76）。结论:基于灰阶超声成像的影像特征与HCC分级高度相关，所建影像组学模型能够较好地预测HCC分级。     

基于自组织映射网络的多骨髓瘤预测模型的研究

本研究基于自组织映射网络（SOM），分析多骨髓瘤基因表达数据，建立预测多骨髓瘤的自组织预测模型（SOPM）。该模型使用相关分析法，分析原始数据集中的7129个基因对致病的影响程度，运用自组织映射网络通过自学习的过程，挖掘出25个重要基因，并依据25个重要基因的表达数据分类和预测样本。本研究表明，自组织预测模型能够学习基因调控的复杂规则，发现对致病有重要影响的基因，并能依据105个样本的基因表达数据中蕴含的知识获得近98％的预测准确率。     

星形细胞瘤p16与Rb蛋白的表达及其相关性研究

目的检测ｐ１６与Ｒｂ蛋白在原发性星形细胞瘤中的存在状况，以探讨不同病理类型星形细胞瘤中ｐ１６与Ｒｂ蛋白表达及其相关性。方法使用抗ｐ１６及Ｒｂ蛋白抗体对１０２例星形细胞瘤手术标本进行免疫细胞化学检测。结果低度恶性星形细胞瘤（ＷＨＯⅠ～Ⅱ级）中ｐ１６及Ｒｂ蛋白均为阳性表达，在高度恶性星形细胞瘤（ＷＨＯⅢ～Ⅳ级）中其阳性表达分别为４８．１％（２６／５４）和５７．４％（３１／５４）。在３１例Ｒｂ蛋白阳性标本中，有２４例（７７．４％）显示ｐ１６蛋白表达缺失或低表达，而在２３例Ｒｂ蛋白阴性标本中却有１９例（８２．６％）显示ｐ１６蛋白阳性或强阳性。结论（１）ｐ１６及Ｒｂ蛋白参与星形细胞瘤细胞增殖过程并影响其细胞分化；（２）ｐ１６与Ｒｂ蛋白之间阳性表达的相互抑制，可能是高度恶性星形细胞瘤的标志之一。     

一种基于语义模型的乳腺钙化病灶标注方法

为了实现对乳腺X线影像的医学语义标注,提出一种利用贝叶斯网络(BN)的多层乳腺影像钙化点语义建模方法。该方法首先用支持向量机(SVM)得到从图像底层视觉特征到中层特征语义的映射,然后再利用BN融合特征语义,最终提取出高层病症语义即恶性程度的概率表达,完成语义模型。将模型应用于乳腺图像的语义标注,本实验选用142幅图像作为训练集,50幅图像作为测试集,结果表明,样本标注诊断语义的准确率:恶性为81.48%,良性为73.91%。     

基于DTI影像图论分析的正常老化认知表现预测模型

目的 以磁共振扩散张量影像（diffusion tensor imaging,DTI）为基础进行大脑结构网络拓扑属性分析,选择与认知表现分数相关性较大的结构网络特征,并基于这些特征建立认知表现分数预测模型,藉以客观地估测老年人的大脑认知能力.方法 对94例正常老化的DTI影像进行结构脑网络构建,采用图论法分析结构连接矩阵,提取结构网络的特征,并将所有特征与受试者的简单智能状态检查量表（mini-mental status examination,MMSE）分数进行相关性分析,选取出与大脑认知高度相关的网络特征,再基于这些特征建立5种分析模型,预测受试者的认知表现分数,以进一步分析模型的预测效能.结果 通过相关性分析,在相关系数大于0.22且P值小于0.05的条件下,选取出与大脑认知高度相关的30个特征,这些特征分布在AAL（automated anatomical labeling）图谱中的12个脑区.而在模型建立与效能分析部分,以高斯回归模型的效能最佳,其训练组相关系数达0.89,预测误差最小为2.01,对受试者的认知表现分数预测较准确.结论 利用结构脑网络度量指标作为生物标记指针可建立正常老化认知功能预测模型,且能有效预测正常老年人的认知表现分数.     

基于影像组学和剂量组学的放射性肺炎预测研究

目的：旨在利用影像组学和剂量组学的多组学方法，建立并验证一个有效的基于CT图像的放射性肺炎（RP）预测模型。方法：对2019年至2021年在广州医科大学附属肿瘤医院接受放疗的91例非小细胞肺癌患者进行回顾性分析。将除去临床靶区的全肺（Lung-CTV）作为感兴趣区域，从Lung-CTV区域的CT图像和剂量分布中提取影像组学和剂量组学特征。将单独的剂量体积直方图（DVH）特征、影像组学结合DVH(radio+DVH)特征、影像组学结合剂量组学（radio+dose）特征，分别输入11个不同的分类器来构建预测模型，采用五倍交叉验证法来完成分类实验。利用接受者操作特征（ROC）曲线下的面积（AUC）、准确性、精确性、召回率和F1值来评估预测模型的性能。结果：与DVH模型相比，radio+DVH和radio+dose的AUC值更高，差异有统计学意义（P<0.05）。与DVH和radio+DVH模型相比，radio+dose的准确率和F1值更高，差异有统计学意义（P<0.05）。结论：使用基于机器学习的影像组学和剂量组学的多组学方法预测RP的性能更好，有望为临床治疗提供指导。     

Flt-4在不同级别脑星形细胞瘤中的表达意义

目的：探讨不同组织病理分级的脑星形细胞瘤中血管内皮生长因子受体3（Flt-4又称VEGFR-3）的表达意义。 方法： 采用免疫组织化学方法，检测50例不同级别脑星形细胞瘤患者手术切除标本中Flt-4、血管内皮生长因子（VEGF）的表达，并用抗FⅧ因子抗体标记瘤组织血管内皮细胞，计算肿瘤内微血管密度（IMVD）。 结果： Flt-4、VEGF总阳性表达率分别为52%（26/50）、60%（30/50）。Flt-4、VEGF均与脑星形细胞瘤病理分级呈显著正相关（等级相关系数分别为0.359、0.360,P＜0.05）。 结论： 脑星形细胞瘤中有Flt-4表达，主要表达于血管内皮细胞和部分肿瘤细胞，Flt-4既可是内皮细胞自分泌产生，部分还可来自瘤细胞的旁分泌；脑星形细胞瘤中Flt-4阳性表达的脉管是血管；Flt-4的表达与星形细胞瘤的病理分级相关     

基于多参数磁共振影像组学的乳腺癌病理信息预测模型研究

联合动态增强磁共振成像(DCE-MRI)、T2加权成像(T2WI)以及弥散加权成像(DWI)的影像特征,建立基于多参数影像组学的预测模型,分别对乳腺癌分子分型、组织学分级和Ki-67表达进行预测。采集150例术前、化疗前的浸润性导管癌患者乳腺MRI数据,获取DCE-MRI、T2WI和DWI影像。分割各参数影像的病灶区域,并提取多参数影像特征。在训练集采用支持向量机递归特征消除(SVM-RFE)算法,获得影像组学最优特征子集并构建基于SVM的预测模型,在测试集中测试模型性能。采用概率平均法、概率投票法和概率模型优化法,分别将基于不同参数影像构建的预测模型进行融合,得到多参数影像联合预测结果,并计算ROC曲线下的面积(AUC)评估模型的分类性能。单参数影像模型预测LuminalA、LuminalB、HER2和Basal-like等4种分子分型的最佳AUC分别为0.6721、0.6940、0.6777和0.7086,多参数影像模型的预测结果提高到AUC分别为0.7995、0.7279、0.7375和0.7925。单参数影像模型预测分级的最佳AUC为0.7533,多参数影像模型的预测结果提高到0.8017。单参数影像模型预测Ki-67表达的最佳AUC为0.6647,多参数影像模型预测结果提高到0.7718。相比于单参数影像模型的预测结果,多参数影像模型的预测结果有所提升,且差异具有显著性(P<0.05)。实验结果表明,采用多参数磁共振影像(DCE-MRI、T2WI以及DWI)组学的联合,可以显著提高单一参数影像模型预测乳腺癌病理信息的性能,对乳腺癌的诊断和个性化治疗方案的选择具有重要意义。     

人脑星形细胞瘤纤溶酶原激活抑制因子1基因表达研究

目的研究人脑星形细胞瘤纤溶酶原激活抑制因子１（ＰＡＩ１）基因表达及其临床意义。方法采用Ｎｏｒｔｈｅｒｎ杂交和免疫组化ＡＢＣ方法检测３６例人脑星形细胞瘤ＰＡＩ１ｍＲＮＡ和蛋白表达，分析其与临床病理因素之间的关系。结果所有星形细胞瘤组织均可表达３．０ｋｂ和２．２ｋｂ的ＰＡＩ１ｍＲＮＡ转录物；高分级星形细胞瘤ＰＡＩ１ｍＲＮＡ表达水平显著高于低分级星形细胞瘤（Ｐ＜００１）；正常脑组织未检测出ＰＡＩ１ｍＲＮＡ表达。ＰＡＩ１ｍＲＮＡ表达水平与星形细胞瘤的坏死（ｒ＝０．５１，Ｐ＜０．０１）、微血管数（ｒ＝０．３３，Ｐ＜０．０１）及脑水肿（ｒ＝０．２７，Ｐ＜０．０１）呈显著正相关，与患者性别、年龄及瘤体大小无显著相关性。免疫组化染色显示，ＰＡＩ１蛋白主要分布在高分级星形细胞瘤的瘤细胞和内皮细胞，尤以血管增殖部位和坏死灶周围较为显著，低分级星形细胞瘤呈低水平表达。结论ＰＡＩ１基因表达与人脑星形细胞瘤的分级、坏死、血管生成及脑水肿密切相关，可作为星形细胞瘤恶性程度的分子标记。     

On the interplay of machine learning and background knowledge in image interpretation by Bayesian networks

ObjectivesTo obtain a balanced view on the role and place of expert knowledge and learning methods in building Bayesian networks for medical image interpretation.Methods and materialsThe interpretation of mammograms was selected as the example medical image interpretation problem. Medical image interpretation has its own common standards and procedures. The impact of these on two complementary methods for Bayesian network construction was explored. Firstly, methods for the discretisation of continuous features were investigated, yielding multinomial distributions that were compared to the original Gaussian probabilistic parameters of the network. Secondly, the structure of a manually constructed Bayesian network was tested by structure learning from image data. The image data used for the research came from screening mammographic examinations of 795 patients, of whom 344 were cancerous.ResultsThe experimental results show that there is an interesting interplay of machine learning results and background knowledge in medical image interpretation. Networks with discretised data lead to better classification performance (increase in the detected cancers of up to 11.7%), easier interpretation, and a better fit to the data in comparison to the expert-based Bayesian network with Gaussian probabilistic parameters. Gaussian probability distributions are often used in medical image interpretation because of the continuous nature of many of the image features. The structures learnt supported many of the expert-originated relationships but also revealed some novel relationships between the mammographic features. Using discretised features and performing structure learning on the mammographic data has further improved the cancer detection performance of up to 17% compared to the manually constructed Bayesian network model.ConclusionFinding the right balance between expert knowledge and data-derived knowledge, both at the level of network structure and parameters, is key to using Bayesian networks for medical image interpretation. A balanced approach to building Bayesian networks for image interpretation yields more accurate and understandable Bayesian network models.     

A multi-agent intelligent environment for medical knowledge

AMPLIA is a multi-agent intelligent learning environment designed to support training of diagnostic reasoning and modelling of domains with complex and uncertain knowledge. AMPLIA focuses on the medical area. It is a system that deals with uncertainty under the Bayesian network approach, where learner-modelling tasks will consist of creating a Bayesian network for a problem the system will present. The construction of a network involves qualitative and quantitative aspects. The qualitative part concerns the network topology, that is, causal relations among the domain variables. After it is ready, the quantitative part is specified. It is composed of the distribution of conditional probability of the variables represented. A negotiation process (managed by an intelligent MediatorAgent) will treat the differences of topology and probability distribution between the model the learner built and the one built-in in the system. That negotiation process occurs between the agents that represent the expert knowledge domain (DomainAgent) and the agent that represents the learner knowledge (LearnerAgent).     

基于模糊小波神经网络的磁共振图像分割方法

针对磁共振图像分割的特点,将小波分析优异的局部特性、模糊逻辑的定性知识表达能力和神经网络的自学习能力结合起来,提出了一种基于模糊小波神经网络的磁共振图像分割方法.该方法采用小波函数作为模糊隶属函数,利用神经网络实现模糊推理,并采用反向误差传播算法对网络进行训练.实验结果表明,这种基于模糊小波神经网络的磁共振图像分割方法与普通神经网络分割方法相比,具有更高的分割精度和更快的训练收敛速度.     

Computer-Aided Diagnosis of Breast DCE-MRI Images Using Bilateral Asymmetry of Contrast Enhancement Between Two Breasts

Dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI) of breasts is an important imaging modality in breast cancer diagnosis with higher sensitivity but relatively lower specificity. The objective of this study is to investigate a new approach to help improve diagnostic performance of DCE-MRI examinations based on the automated detection and analysis of bilateral asymmetry of characteristic kinetic features between the left and right breast. An image dataset involving 130 DCE-MRI examinations was assembled and used in which 80 were biopsy-proved malignant and 50 were benign. A computer-aided diagnosis (CAD) scheme was developed to segment breast areas depicted on each MR image, register images acquired from the sequential MR image scan series, compute average contrast enhancement of all pixels in one breast, and a set of kinetic features related to the difference of contrast enhancement between the left and right breast, and then use a multi-feature based Bayesian belief network to classify between malignant and benign cases. A leave-one-case-out validation method was applied to test CAD performance. The computed area under a receiver operating characteristic (ROC) curve is 0.78 ± 0.04. The positive and negative predictive values are 0.77 and 0.64, respectively. The study indicates that bilateral asymmetry of kinetic features between the left and right breasts is a potentially useful image biomarker to enhance the detection of angiogenesis associated with malignancy. It also demonstrates the feasibility of applying a simple CAD approach to classify between malignant and benign DCE-MRI examinations based on this new image biomarker.     

Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network

This study investigates a simple Bayesian belief network for the diagnosis of breast cancer, and specifically addresses the question of whether integrating image and non-image based features into a single network can yield better performance than hybrid combinations of independent networks. From a dataset of 419 cases, including 92 malignancies, 13 features relating to mammographic findings, physical examinations and patients' clinical histories, were extracted to build three Bayesian belief networks. The scenarios tested included a network incorporating all features and two hybrids which combined the outputs of sub-networks corresponding to the image or non-image features. Average areas (Az) under the corresponding ROC curves were used as measures of performance. The network incorporating only image based features performed better (Az =0.81) than that using nonimage features (Az = 0.71). Both hybrid classifiers yielded better performance (Az =0.85 for averaging and Az = 0.87 for logistic regression), but neither hybrid was as accurate as the network incorporating all features (Az = 0.89). This preliminary study suggests that, like human observers who concurrently consider different types of information, a single classifier that simultaneously evaluates both image and non-image information can achieve better diagnostic performance than the hybrid combinations considered here.     

Fuzzy Evidential Network and Its Application as Medical Prognosis and Diagnosis Models

Uncertainty is one of the important facts of the medical knowledge. Medical prognosis and diagnosis, as the essential parts of medical knowledge, is affected by different aspects of uncertainty, which must be managed. In the previous studies, different theories such as Bayesian probability theory, evidence theory, and fuzzy set theory have been developed to represent and manage different aspects of uncertainty. Recently, hybrid frameworks are suggested to deal with various types of uncertainty in a single framework. Evidential networks are general frameworks for dealing explicitly with total and partial ignorance and offer powerful combination rule of contradictory evidence. In this framework, the fuzziness of linguistic variables is neglected while these variables commonly appear in the medical domain knowledge and different sources of medical information. In addition, the evidential network parameters are determined based on the experts' knowledge and no data-driven algorithm is provided to learn these parameters. In this study, a novel hybrid framework called fuzzy evidential network was suggested to manage the imprecision and epistemic uncertainty of medical prognosis and diagnosis. Also, a data-driven algorithm based on the fuzzy set theory and the fuzzy maximum likelihood is provided to learn the network parameters from clinical databases. The performance of the proposed framework as various prognosis and diagnosis models, compared with well-known machine learning algorithms and the results showed its superiority. Also, an evidential method is suggested to handle the missing values and its results were compared with KNN imputation method.     

基于贝叶斯网络的脑胶质瘤恶性高低度的自动诊断

贝叶斯网络（Bayesian Network）可以把统计学和图论有效地结合起来，近年来成为数据挖掘中的研究热点，其优点可以综合先验信息和样本信息，适于处理不完整数据集。本文采用280例病例作为训练数据，利用Bayesian network进行大脑胶质瘤高低度的自动诊断，利用严格的Bayes规则进行推理，在推理过程中采用了D分离来简化过程，其诊断正确率达到80％以上，达到了领域专家的要求，而且在可理解性方面要比多层感知器和决策树要好。     

From vagueness in medical thought to the foundations of fuzzy reasoning in medical diagnosis

OBJECTIVE: This article delineates a relatively unknown path in the history of medical philosophy and medical diagnosis. It is concerned with the phenomenon of vagueness in the physician's "style of thinking" and with the use of fuzzy sets, systems, and relations with a view to create a model of such reasoning when physicians make a diagnosis. It represents specific features of medical ways of thinking that were mentioned by the Polish physician and philosopher Ludwik Fleck in 1926. The paper links Lotfi Zadeh's work on system theory before the age of fuzzy sets with system-theory concepts in medical philosophy that were introduced by the philosopher Mario Bunge, and with the fuzzy-theoretical analysis of the notions of health, illness, and disease by the Iranian-German physician and philosopher Kazem Sadegh-Zadeh. MATERIAL: Some proposals to apply fuzzy sets in medicine were based on a suggestion made by Zadeh: symptoms and diseases are fuzzy in nature and fuzzy sets are feasible to represent these entity classes of medical knowledge. Yet other attempts to use fuzzy sets in medicine were self-contained. The use of this approach contributed to medical decision-making and the development of computer-assisted diagnosis in medicine. CONCLUSION: With regard to medical philosophy, decision-making, and diagnosis; the framework of fuzzy sets, systems, and relations is very useful to deal with the absence of sharp boundaries of the sets of symptoms, diagnoses, and phenomena of diseases. The foundations of reasoning and computer assistance in medicine were the result of a rapid accumulation of data from medical research. This explosion of knowledge in medicine gave rise to the speculation that computers could be used for the medical diagnosis. Medicine became, to a certain extent, a quantitative science. In the second half of the 20th century medical knowledge started to be stored in computer systems. To assist physicians in medical decision-making and patient care, medical expert systems using the theory of fuzzy sets and relations (such as the Viennese "fuzzy version" of the Computer-Assisted Diagnostic System, CADIAG, which was developed at the end of the 1970s) were constructed. The development of fuzzy relations in medicine and their application in computer-assisted diagnosis show that this fuzzy approach is a framework to deal with the "fuzzy mode of thinking" in medicine.