肝癌影像组学临床应用研究进展

肝癌是常见消化系统恶性肿瘤,我国肝癌发病率和死亡率都处于较高水平,严重危害着人民的生命健康。目前尚缺乏可靠的检测方法与诊断设备快速评估肝癌患者的肿瘤异质性和侵袭性。随着计算机人工智能技术和图像处理技术的迅速发展,出现了影像组学这一崭新的研究领域,实现了对疾病进展的整体性分析,有望为无创评估肿瘤患者的生物学行为提供可能。影像组学将医学影像诊断和大数据技术相融合,通过提取肉眼无法识别的图像特征,客观量化病灶的像素灰度值变化及分布潜在规律,为肝脏肿瘤的诊断、治疗、预后和评估提供依据,为患者的个体化、综合性、精准性治疗提供强大的辅助。本文拟探讨影像组学对肝癌的诊断与预测价值。     

实时超声弹性图像特征量化方法及其应用研究

0引言检测生物组织力学特性的超声弹性成像是近年来新兴的超声影像学技术,但目前的研究工作主要集中在如何构建弹性图像上,缺乏对弹性图像信息与临床诊断结果的前瞻性研究。五分法[1]及弹性比值[2-4]法是最常用的评估乳腺肿瘤方法,受诊断者主观影响较大,没有形成统一的标准,限制了超声弹性成像技术在临床上的广泛应用。构建科学、系统、有效的肿瘤弹性特征的量化和提取方法就显得尤其     

基于非监督分类的遥感信息提取方法尝试

随着遥感技术的发展,遥感数据已应用到很多领域。从遥感影像中分析出地物空间特征和属性特征是遥感影像解译的关键。遥感图像分类是将图像中每个像元根据其在不同波段的光谱亮度、空间结构特征或其他相关信息,按照一定的规则或算法划分为不同的类别。利用非监督分类法尝试对ETM+遥感影像进行分析解译,提取水体专题信息,从结果来看专题信息提取效果较好。     

浅析CT影像学技术应用于周围性小肺癌中的诊断价值

目的:分析CT影像学技术在周围型小肺癌(SPLC)诊断中的应用价值。方法:以2016年5月-2018年5月收集的SPLC病人为对象,总共68例。对其临床资料加以回顾性分析,对其均给予CT影像学技术扫描,对其影像学情况表现进行观察研究。结果:CT显示病人的病灶多集中于左肺,病灶大小为8-20mm,以毛玻璃样密度小泡征及密度不均且呈小颗粒状堆积为主要表现,病灶边缘的分叶比较深,病灶周围中,11例胸膜边缘有玻璃样晕影,21例有线影,32例有胸膜凹陷征,4例有支气管血管聚集征。经增强扫描,病灶呈均匀强化者21例,不均匀强化者47例,病灶的时间及密度曲线呈现出缓慢升高表现。结论:CT影像技术应用于SPLC中具有较高的诊断价值,可作为SPLC鉴别诊断的首选影像学技术。     

18F-FDG PET图像中肿瘤非均匀性评估模型

肿瘤在功能图像中表现出的非均匀特性能够一定程度上反应出其基本特性和对治疗的响应,对这一特性的数学描述和建模可为治疗和预估治疗效果提供有意义的量化参考数据.本文提出一种新的放射性同位素氟18标记的脱氧葡萄糖(18F-FDG)正电子发射断层影像(PET)中肿瘤内部非均匀性计算模型,通过图像中相邻像素的FDG标准摄取值(SUV)差异和其位置特征,可得出能描述肿瘤图像呈现的非均匀特性的参数H指数.使用矩形和高斯球模体以及3例肺癌患者数据,通过与灰度共生矩阵(GLCM)图像分析法比较研究,验证了该模型的有效性.     

数字重建在肺癌放射影像中的应用

目的:探究肺癌患者在放射影像学检查过程中应用数字重建方法的价值。方法:于我院2017年6月-2018年2月收治的肺癌患者中随机选取66例入组研究,将66例肺癌患者平均分为常规组和数字重建组,常规组基于模拟机的引导下进行常规检查,数字重建组患者采用数字重建方法进行检查。结果:从骨髓量下降程度、心脏剂量下降程度和靶体积缩小程度三个方面进行比较,数字重建组患者的检查结果较优。结论:在肺癌放射影像检查过程中采用数字重建方法,能够减少放射治疗的面积,为体位的合理选择提供科学的参考依据。     

基于深度学习的脑肿瘤影像研究进展

脑肿瘤早期诊断对改善治疗结果,提高患者生存率起着重要作用.脑肿瘤影像具有很强的异质性,且脑肿瘤图像数量大、序列多,人工评估脑肿瘤影像复杂且耗时.因此,迫切需要具有更高准确性和更具效率的计算机辅助方法来进行脑肿瘤的分析.当前,计算机辅助脑肿瘤影像诊断研究主要集中在三个领域,包括肿瘤检测、分割和分类.近年来,人工智能领域内...     

局灶性机化性肺炎的多层螺旋CT诊断及与周围型肺癌鉴别

目的:探析局灶性机化性肺炎在多层螺旋CT中的诊断特征,并提高多层螺旋CT鉴别局灶性机化性肺炎与周围型肺癌的能力。方法:选取67例局灶性机化性肺炎患者为对照组,同时选取59例周围型肺癌患者为观察组,对此两组CT影像进行比较。结果:对照组患者在病灶分布、分叶、毛刺上同观察组有显著差异(P0.05)。结论:多层螺旋CT及增强扫描则能够充分显现局灶性机化性肺炎的影像学特点,在鉴别周围型肺癌上具有一定作用,对于无法确诊病例,应在其抗感染治疗后进行影像学随访,以提高其鉴别准确性。     

血管网光声成像的机器学习抗散射仿真研究

乳腺癌已成为全球女性发病率最高的肿瘤疾病,微血管成像对乳腺癌的治疗方案和预后有重要意义。光声层析成像术(Photoacoustic Tomography, PAT)可有效对乳腺癌内微血管网进行成像,但肿瘤组织内部的异质微结构和钙化点的散射对成像质量影响较大。针对该问题,文章基于U-Net的卷积神经网络对不同颗粒散射条件下软组织中血管网图像散斑开展仿真研究。仿真结果表明,该神经网络可以学习光声散斑图像和成像目标之间的映射关系,提取出隐藏在噪声中的血管光声信号,并重建出轮廓清晰、背景清晰的高质量血管图像,表明U-Net网络可以从高度模糊的散射图像中提取出有效的光声信息,实现目标图像的高清重建,在乳腺癌的诊断成像中具有广阔的应用前景。     

消息动态

<正>国内首个乳腺影像诊断标准发布近年来我国乳腺癌发病率逐年增高,为有效防治该病,影像学诊断手段已成为早期发现乳腺癌的最可靠手段。为提升影像学诊断效果,在2010年3月13日天津市召开的第二届全国乳腺学术大会上发布了由天津市肿瘤医院参与制定的我国首个"乳腺影像诊断规范及质量控制方法"。     

Morphological feature extraction and KNG-CNN classification of CT images for early lung cancer detection

Lung cancer is a dangerous disease causing death to individuals. Currently precise classification and differential diagnosis of lung cancer is essential with the stability and accuracy of cancer identification is challenging. Classification scheme was developed for lung cancer in CT images by Kernel based Non-Gaussian Convolutional Neural Network (KNG-CNN). KNG-CNN comprises of three convolutional, two fully connected and three pooling layers. Kernel based Non-Gaussian computation is used for the diagnosis of false positive or error encountered in the work. Initially Lung Image Database Consortium image collection (LIDC-IDRI) dataset is used for input images and a ROI based segmentation using efficient CLAHE technique is carried as preprocessing steps, enhancing images for better feature extraction. Morphological features are extracted after the segmentation process. Finally, KNG-CNN method is used for effectual classification of tumour > 30mm. An accuracy of 87.3% was obtained using this technique. This method is effectual for classifying the lung cancer from the CT scanned image.     

Automated texture‐based characterization of fibrosis and carcinoma using low‐dose lung CT images

Lung cancer is the most common cause of cancer deaths worldwide and account for 1.38 million deaths per year. Patients with lung cancer are often misdiagnosed as pulmonary tuberculosis (TB) leading to delay in the correct diagnosis as well as exposure to inappropriate medication. The diagnosis of TB and lung cancer can be difficult as symptoms of both diseases are similar in computed tomography (CT) images. However, treating TB leads to inflammatory fibrosis in some of the patients. There comes the need of an efficient computer aided diagnosis (CAD) of the fibrosis and carcinoma diseases. To design a fully automated CAD for characterizing fibrous and carcinoma tissues without human intervention using lung CT images. The 18 subjects in this study include seven healthy, two fibrosis and eight carcinoma, and one necrosis cases. The dataset is built by CT cuts representing healthy is 113, fibrosis is 103, necrosis is 39, and carcinoma is 185 totalling 440 images. The gray‐level spatial dependence matrix and gray level run length matrix approach are used for extracting texture‐based features. These features are given to neural network classifier and statistical classifier. These classifier performances are evaluated using receiver‐operating characteristics (ROC). The proposed method characterizes these tissues without human intervention. Sensitivity, specificity, precision, and accuracy followed by ROC curves were obtained and also studied. Thus, the proposed automated image‐based classifier could act as a precursor to histopathological analysis, thereby creating a way to class specific treatment procedures.     

Overcoming Obstacles in Pathological Diagnosis of Pulmonary Nodules through Circulating Tumor Cell Enrichment

With the popularity of low‐dose computed tomography (LDCT) in clinical examination of the lung, the prevalence of pulmonary nodules has significantly increased, thus significantly improving the early diagnosis of lung cancer, but also potentially contributing to overtreatment. This study aims to develop a noninvasive method to assist in diagnosing the pulmonary nodules. To do so, 3798 patients are recruited from the Department of Thoracic Surgery at Shanghai Pulmonary Hospital and peripheral blood samples are collected from them before surgery. From these samples, circulating tumor cells (CTC) are isolated using folate receptor (FR) positivity, and then enriched and analyzed in relation to cancer gene expression, stage, and level of invasion. The average CTC concentration of patients with lung disease is 11.97 functional unit (FU) in a 3 mL sample of blood. FR‐positive CTC levels correlate with the expression of lung cancer driver genes tumor‐node‐matastasis (TNM) stage, and pleura invasion. The sensitivity of CTC levels to lung cancer diagnosis is 87.05%. Results from this study demonstrate that the determination of FR‐positive CTC concentration is a convenient and time‐saving strategy to improve the pathological diagnosis of pulmonary nodules.     

Pre-trained convolutional neural networks as feature extractors for diagnosis of breast cancer using histopathology

Several researchers are trying to develop different computer-aided diagnosis system for breast cancer employing machine learning (ML) methods. The inputs to these ML algorithms are labeled histopathological images which have complex visual patterns. So, it is difficult to identify quality features for cancer diagnosis. The pre-trained Convolutional Neural Networks (CNNs) have recently emerged as an unsupervised feature extractor. However, a limited investigation has been done for breast cancer recognition using histopathology images with CNN as a feature extractor. This work investigates ten different pre-trained CNNs for extracting the features from breast cancer histopathology images. The breast cancer histopathological images are obtained from publicly available BreakHis dataset. The classification models for the different feature sets, which are obtained using different pre-trained CNNs in consideration, are developed using a linear support vector machine. The proposed method outperforms the other state of art methods for cancer detection, which can be observed from the results obtained.     

Performance of Lung Cancer Prediction Methods Using Different Classification Algorithms

In 2018, 1.76 million people worldwide died of lung cancer. Most of these deaths are due to late diagnosis, and early-stage diagnosis significantly increases the likelihood of a successful treatment for lung cancer. Machine learning is a branch of artificial intelligence that allows computers to quickly identify patterns within complex and large datasets by learning from existing data. Machine-learning techniques have been improving rapidly and are increasingly used by medical professionals for the successful classification and diagnosis of early-stage disease. They are widely used in cancer diagnosis. In particular, machine learning has been used in the diagnosis of lung cancer due to the benefits it offers doctors and patients. In this context, we performed a study on machine-learning techniques to increase the classification accuracy of lung cancer with 32 × 56 sized numerical data from the Machine Learning Repository web site of the University of California, Irvine. In this study, the precision of the classification model was increased by the effective employment of pre-processing methods instead of direct use of classification algorithms. Nine datasets were derived with pre-processing methods and six machine-learning classification methods were used to achieve this improvement. The study results suggest that the accuracy of the k-nearest neighbors algorithm is superior to random forest, naïve Bayes, logistic regression, decision tree, and support vector machines. The performance of pre-processing methods was assessed on the lung cancer dataset. The most successful pre-processing methods were Z-score (83% accuracy) for normalization methods, principal component analysis (87% accuracy) for dimensionality reduction methods, and information gain (71% accuracy) for feature selection methods.     

An approach for classification of malignant and benign microcalcification clusters

The only reliable and successful treatment of breast cancer is its detection through mammography at initial stage. Clusters of microcalcifications are important signs of breast cancer. Manual interpretation of mammographic images, in which the suspicious regions are indicated as areas of varying intensities, is not error free due to a number of reasons. These errors can be reduced by using computer-aided diagnosis systems that result in reduction of either false positives or true negatives. The purpose of the study in this paper is to develop a methodology for distinguishing malignant microcalcification clusters from benign microcalcification clusters. The proposed approach first enhances the region of interest by using morphological operations. Then, two types of features, cluster shape features and cluster texture features, are extracted. A Support Vector Machine is used for classification. A new set of shape features based on the recursive subsampling method is added to the feature set, which improves the classification accuracy of the system. It has been found that these features are capable of differentiating malignant and benign tissue regions. To investigate the performance of the proposed approach, mammogram images are taken from Digital Database for Screening Mammography database and an accuracy of 94.25% has been achieved. The experiments have shown that the proposed classification system minimizes the classification errors and is more efficient in correct diagnosis.     

Analysis and classification of malignancy in pancreatic magnetic resonance images using neural network techniques

Computer-aided diagnosis (CAD) is a computerized way of detecting tumors in MR images. Magnetic resonance imaging (MRI) has been generally used in the diagnosis and detection of pancreatic tumors. In a medical imaging system, soft tissue contrast and noninvasiveness are clear preferences of MRI. Inaccurate detection of tumor and long time consumption are the disadvantages of MRI. Computerized classifiers can greatly renew the diagnosis activity, in terms of both accuracy and time necessity by normal and abnormal images, automatically. This article presents an intelligent, automatic, accurate, and robust method to classify human pancreas MRI images as normal or abnormal in terms of pancreatic tumor. It represents the response of artificial neural network (ANN) and support vector machine (SVM) techniques for pancreatic tumor classification. For this, we extract features from MR images of pancreas using the GLCM method and select the best features using JAFER algorithm. These features are analyzed by five classification techniques: ANN BP, ANN RBF, SVM Linear, SVM Poly, and SVM RBF. We compare the results with benchmark data set of MR brain images. The analytical outcome presents that the two best features used to classify the MR images using ANN BP technique have 98% classification accuracy.     

Development of an Ultrasonic Nomogram for Preoperative Prediction of Castleman Disease Pathological Type

An ultrasonic nomogram was developed for preoperative prediction of Castleman disease (CD) pathological type (hyaline vascular (HV) or plasma cell (PC) variant) to improve the understanding and diagnostic accuracy of ultrasound for this disease. Fifty cases of CD confirmed by pathology were gathered from January 2012 to October 2018 from three hospitals. A grayscale ultrasound image of each patient was collected and processed. First, the region of interest of each gray ultrasound image was manually segmented using a process that was guided and calibrated by radiologists who have been engaged in imaging diagnosis for more than 5 years. In addition, the clinical characteristics and other ultrasonic features extracted from the color Doppler and spectral Doppler ultrasound images were also selected. Second, the chi-square test was used to select and reduce features. Third, a naïve Bayesian model was used as a classifier. Last, clinical cases with gray ultrasound image datasets from the hospital were used to test the performance of our proposed method. Among these patients, 31 patients (18 patients with HV and 13 patients with PC) were used to build a training set for the predictive model and 19 (11 patients with HV and 8 patients with PC) were used for the test set. From the set, 584 high-throughput and quantitative image features, such as mass shape size, intensity, texture characteristics, and wavelet characteristics, were extracted, and then 152 images features were selected. Comparing the radiomics classification results with the pathological results, the accuracy rate, sensitivity, and specificity were 84.2%, 90.1%, and 87.5%, respectively. The experimental results show that radiomics was valuable for the differentiation of CD pathological type.     

End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is easily accessible to people around the globe. Nonetheless, this is not eminently efficacious considering human inspection of medical images can yield a high false positive rate. Ineffective and inefficient diagnosis is a crucial reason for such a high mortality rate for this malady. However, the conspicuous advancements in deep learning and artificial intelligence have stimulated the development of exceedingly precise diagnosis systems. The development and performance of these systems rely prominently on the data that is used to train these systems. A standard problem witnessed in publicly available medical image datasets is the severe imbalance of data between different classes. This grave imbalance of data can make a deep learning model biased towards the dominant class and unable to generalize. This study aims to present an end-to-end convolutional neural network that can accurately differentiate lung nodules from non-nodules and reduce the false positive rate to a bare minimum. To tackle the problem of data imbalance, we oversampled the data by transforming available images in the minority class. The average false positive rate in the proposed method is a mere 1.5 percent. However, the average false negative rate is 31.76 percent. The proposed neural network has 68.66 percent sensitivity and 98.42 percent specificity.