乳腺癌X线征象的研究

目的:探讨乳腺癌X线征象,以提高乳腺癌诊断水平。方法:对70例乳腺癌X线征象作回顾性分析,并与病理结果对照。结果:直接征象:肿块58例,其中圆形肿块16例,分叶状肿块14例,毛刺状肿块28例。钙化32例。间接征象:皮肤增厚,乳头内陷16例,血管增粗增多12例,乳腺实质结构紊乱8例,腋窝淋巴结增大8例。结论:乳腺X线片中不同形态的直接征象及间接征象对乳腺癌的诊断具有重要指导价值。     

乳腺癌免疫组化检测在乳腺癌患者诊治中的意义分析

目的：探讨免疫组化检测在乳腺癌患者诊治中的价值。方法：随机选取2011年1月-2013年1月的68例经过空心穿刺活捡并病理确诊的乳腺癌患者为研究对象,均采用免疫组化检测ER、PR、P53、Bcl-2,全部采用CEF化疗方案治疗3个月后手术治疗,再运用免疫组化SP法检测化疗前后乳腺癌组织中以上指标的阳性表达率情况。结果：ER、PR化疗前后比较无统计学意义（P〉0．05）;而P53、Bcl-2比较有明显的差异性（P〈0．05）;ER、PR的阴性和阳性和疗效情况无明显差异性,而P53、Bcl-2的阴性和阳性表达和化疗的效果有明显的差异性,P〈0．05,具有统计学意义。结论：免疫组化检测中ER、PR对乳腺癌化疗前后无明显差异性,而化疗可通过抑制P53的表达来抑制乳腺癌增值并通过升高Bcl-2表达来调整肿瘤细胞分化。     

Automatic Grading System for Diagnosis of Breast Cancer Exploiting Co-occurrence Shearlet Transform and Histogram Features

ObjectivesBreast cancer (BC) is one of the most commonly reported health issues worldwide, especially in females. Early detection and diagnosis of BC can greatly reduce mortality rates. Samples obtained with different imaging methods such as mammography, computerized tomography, magnetic resonance, ultrasound, and biopsy are used in the diagnosis of BC. Histopathological images obtained from a biopsy contain vital information about the stage of the BC. Computer-aided systems are important tools to assist pathologists in the early detection of BC.Material and methodsIn the current study, the use of gray-level co-occurrence matrix (GLCM) of Shearlet Transform (ST) coefficients were first scrutinized as textural features. ST is an advanced decomposition-based method that can analyze images in various directions and is sensitive to edge singularities. These features make ST more robust than other decomposition methods such as Fourier and wavelet. Color channel histogram features were also utilized for a second level of evaluation in the diagnosis of the BC stage. These features are considered one of the most important building blocks that pathologists consider in the course of grading histopathological images. Then, by combining these two features, the classification results were re-assessed utilizing Support Vector Machine (SVM) as a classifier.ResultsThe assessments were performed on a BreaKHis dataset containing benign and malignant histopathological samples. The average accuracy scores were reported as being 98.2%, 97.2%, 97.8%, and 97.3% in the sub-databases with 40×, 100×, 200×, and 400× magnification factors, respectively.ConclusionsThe obtained results showed that the proposed method was quite efficient in histopathological image classification. Despite the relative simplicity of the approach, the obtained results were far superior to previously reported results.     

Recent Trends in Computer Assisted Diagnosis (CAD) System for Breast Cancer Diagnosis Using Histopathological Images

Breast cancer is one of the common type of cancer in females across the world. An early detection and diagnosis of breast cancer may reduce the mortality rate to a great extent. To diagnose breast cancer, different types of imaging modalities are used to collect samples like mammography, Computerized Tomography, Magnetic Resonance Imaging, Ultrasound and Biopsy. Histopathological images obtained from biopsy may influence how and at which stage the cancer is being diagnosed. The Computer Assisted Diagnosis (CAD) system helps the pathologists in early diagnosis of breast cancer. In this survey, the recently reported techniques for breast cancer diagnosis using histopathological images have been summarized. This study could be beneficial for: (i) Clinicians to receive second opinion from the CAD system for early diagnosis, and (ii) Researchers to analyze and enhance the existing state-of-art techniques used in CAD system, which may further reduce the gap of variability between intra and inter observer.     

Feature Extraction and Classification of Heart Murmurs Based on Acoustic Qualities

Background and objectiveHeart murmur characterization is a crucial part of cardiac auscultation for determining the potential etiology and severity of heart diseases. One such helpful murmur characterization is the sonic qualities, which reflect both structural and hemodynamical states of the heart. Therefore, the objective is to develop a machine learning based solution for classifying murmur qualities.MethodsFour medically defined murmur qualities, namely the musical quality, blowing-like quality, coarse quality, and soft quality were examined. Feature was extracted from heart murmurs signals in their time domain, frequency domain, time-frequency domain, and phase space domain. Sequential forward floating selection (SFFS) was implemented along with three classifiers, including k-nearest neighbor (KNN), Naïve-Bayes (NB), and linear support vector machine (SVM).ResultsIt was found that multi-domain features are suited for better classification results and linear SVM was able to achieve a better balance between performance and the size of feature subsets among tested classifiers. Using the derived features, classification accuracies of 86%, 91%, 90%, and 84% were achieved for musical quality, blowing-like quality, coarse quality, and soft quality classifications respectively.ConclusionsThe study demonstrated that it is possible to effectively characterize heart murmur through its diagnostic characteristics instead of drawing direct conclusions, which is helpful for retaining versatility and generality found in the conventional cardiac auscultation.     

Determination of the Blood,Hormone and Obesity Value Ranges that Indicate the Breast Cancer,Using Data Mining Based Expert System

Breast cancer is a dangerous type of cancer that spreads into other organs over time. Therefore, medical studies are being done for the early diagnosis by means of the anthropometric data and blood analysis values besides the mammographic and histological findings. However, medical studies have identified only cancer-related values but the value ranges indicating the cancer have not been determined yet. Concurrently the automated diagnostic systems are being developed to assist medical specialists in biomedical engineering studies. The range of values or boundaries indicating the cancer are automatically determined in biomedical methods, but only the diagnostic result is presented. Because of this, biomedical studies don't provide enough opportunity for medical experts to evaluate the relationship between values and result. In this study, decision trees that is one of data mining method was applied to anthropometric data and blood analysis values to complete the mentioned deficiencies in breast cancer diagnosis aiming studies. The determined value ranges were also presented visually to medical experts understand them easily. The proposed diagnostic system has accuracy rate up to 90.52% and provides value ranges indicating the breast cancer as well as mathematically presents the relations between the values and cancer.     

吉西他滨联合化疗方案治疗复发转移性乳腺癌的临床疗效观察

目的:观察吉西他滨与顺铂联合以及吉西他滨与紫杉醇联合治疗复发转移性乳腺癌的疗效和不良反应。方法:本研究收集65例女性乳腺癌术后复发转移的患者作为研究对象。随机分成两组,分别应用吉西他滨与顺铂(GP方案组)、吉西他滨与紫杉醇(GT方案组)联合进行治疗。GP方案组患者有30例,第1天、第8天用吉西他滨800mg~1000mg/m2溶于0.9%的100mL生理盐水中静脉滴注;第1天~第3天,21天重复用顺铂30mg/m2溶于0.9%的250mL生理盐水中静脉滴注;GT方案组患者有35例,吉西他滨的使用方法与GP方案组相同,第2天,21重复用紫杉醇135mg/m2溶于0.9%的500mL生理盐水中静脉滴注。对化疗时产生的不良反应进行对症处理。结果 :GP方案组化疗有效率为46.67%,疾病控制率为70.00%;GT方案组化疗有效率为42.86%,疾病控制率为68.57%,两组比较差异均无统计学意义(P0.05)。GT组脱发的发生率为62.86%,明显高于GP组的10.00%(P0.001),其他不良反应在两组之间差异无统计学意义(P0.05)。结论:GP方案和GT方案在治疗复发转移性乳腺癌有较好的疗效,不良反应较轻,可作为复方转移性乳腺的一种化疗方案。     

基于X线图像的膝关节周围原发性骨肿瘤辅助诊断的机器学习模型研究

摘要 目的：开发机器学习模型,并评估其在膝关节周围原发性骨肿瘤诊断方面的准确性。方法：本文将深度卷积神经网络（DCNN）这一深度学习方法应用于膝关节X线图像的影像组学分析,探讨其辅助诊断膝关节周围原发性骨肿瘤的临床价值。结果：该深度学习模型在区分正常与肿瘤影像方面展现出优异的诊断准确性,使用DCNN模型进行5轮测试的总体准确性为（99.8±0.4）%,而阳性预测值和阴性预测值分别为（100.0±0.0）%和（99.6±0.8）%,各个数据集的曲线下面积（AUC）分别为0.99、1.00、1.00、1.0和1.0,平均AUC为（0.998±0.004）;进一步使用DCNN模型进行了10轮测试显示其在区分良性与恶性骨肿瘤方面的总体准确性为（71.2±1.6）%,且达到了强阳性预测值（91.9±8.5）%,各个数据集的AUC分别为0.63、0.63、0.58、0.69、0.55、0.63、0.54、0.57、0.73、0.63,平均AUC为（0.62±0.06）。结论：本文是首个将人工智能技术应用于骨肿瘤诊断的X线图像影像组学分析方面的研究,人工智能影像组学模型能够帮助医生自动地快速筛查骨肿瘤,确定良性或恶性肿瘤时,阳性预测值较高。     

Advancement in Deep Learning Methods for Diagnosis and Prognosis of Cervical Cancer

Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.     

A Hybrid Deep Learning Model for Predicting Molecular Subtypes of Human Breast Cancer Using Multimodal Data

BackgroundThe prediction of breast cancer subtypes plays a key role in the diagnosis and prognosis of breast cancer. In recent years, deep learning (DL) has shown good performance in the intelligent prediction of breast cancer subtypes. However, most of the traditional DL models use single modality data, which can just extract a few features, so it cannot establish a stable relationship between patient characteristics and breast cancer subtypes.DatasetWe used the TCGA-BRCA dataset as a sample set for molecular subtype prediction of breast cancer. It is a public dataset that can be obtained through the following link: https://portal.gdc.cancer.gov/projects/TCGA-BRCAMethodsIn this paper, a Hybrid DL model based on the multimodal data is proposed. We combine the patient's gene modality data with image modality data to construct a multimodal fusion framework. According to the different forms and states, we set up feature extraction networks respectively, and then we fuse the output of the two feature networks based on the idea of weighted linear aggregation. Finally, the fused features are used to predict breast cancer subtypes. In particular, we use the principal component analysis to reduce the dimensionality of high-dimensional data of gene modality and filter the data of image modality. Besides, we also improve the traditional feature extraction network to make it show better performance.ResultsThe results show that compared with the traditional DL model, the Hybrid DL model proposed in this paper is more accurate and efficient in predicting breast cancer subtypes. Our model achieved a prediction accuracy of 88.07% in 10 times of 10-fold cross-validation. We did a separate AUC test for each subtype, and the average AUC value obtained was 0.9427. In terms of subtype prediction accuracy, our model is about 7.45% higher than the previous average.     

eTumorMetastasis: A Network-based Algorithm Predicts Clinical Outcomes Using Whole-exome Sequencing Data of Cancer Patients

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn’t favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the ‘most recent common ancestor’ of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.     

Application of Artificial Intelligence/Machine Vision & Learning for the Development of a Live Single-cell Phenotypic Biomarker Test to Predict Prostate Cancer Tumor Aggressiveness

To assess the usefulness and applications of machine vision (MV) and machine learning (ML) techniques that have been used to develop a single cell-based phenotypic (live and fixed biomarkers) platform that correlates with tumor biological aggressiveness and risk stratification, 100 fresh prostate samples were acquired, and areas of prostate cancer were determined by post-surgery pathology reports logged by an independent pathologist. The prostate samples were dissociated into single-cell suspensions in the presence of an extracellular matrix formulation. These samples were analyzed via live-cell microscopy. Dynamic and fixed phenotypic biomarkers per cell were quantified using objective MV software and ML algorithms. The predictive nature of the ML algorithms was developed in two stages. First, random forest (RF) algorithms were developed using 70% of the samples. The developed algorithms were then tested for their predictive performance using the blinded test dataset that contained 30% of the samples in the second stage. Based on the ROC (receiver operating characteristic) curve analysis, thresholds were set to maximize both sensitivity and specificity. We determined the sensitivity and specificity of the assay by comparing the algorithm-generated predictions with adverse pathologic features in the radical prostatectomy (RP) specimens. Using MV and ML algorithms, the biomarkers predictive of adverse pathology at RP were ranked and a prostate cancer patient risk stratification test was developed that distinguishes patients based on surgical adverse pathology features. The ability to identify and track large numbers of individual cells over the length of the microscopy experimental monitoring cycles, in an automated way, created a large biomarker dataset of primary biomarkers. This biomarker dataset was then interrogated with ML algorithms used to correlate with post-surgical adverse pathology findings. Algorithms were generated that predicted adverse pathology with >0.85 sensitivity and specificity and an AUC (area under the curve) of >0.85. Phenotypic biomarkers provide cellular and molecular details that are informative for predicting post-surgical adverse pathologies when considering tumor biopsy samples. Artificial intelligence ML-based approaches for cancer risk stratification are emerging as important and powerful tools to compliment current measures of risk stratification. These techniques have capabilities to address tumor heterogeneity and the molecular complexity of prostate cancer. Specifically, the phenotypic test is a novel example of leveraging biomarkers and advances in MV and ML for developing a powerful prognostic and risk-stratification tool for prostate cancer patients.     

PET/CT和超声弹性成像在乳腺癌诊断中的价值对比分析

目的:探讨PET/CT和超声弹性成像(UE)在乳腺癌诊断中的价值。方法:回顾性分析2011年1月至2012年5月在我院确诊的173例乳腺患者的临床资料,所有患者均行PET/CT和UE检查。依据病理组织活检和临床随访分别评价PET/CT和UE对乳腺癌诊断的灵敏性、特异性、准确性,并比较两者的结果。结果:PET/CT和UE诊断乳腺癌的灵敏性分别为98.8%和81.3%;特异性分别为84.3%和97.2%;准确性分别为90.7%和90.2%;两种方法联合检测诊断乳腺癌的灵敏性、特异性、准确性分别为98.8%、98.1%、98.4%,UE检测乳腺癌的敏感性明显低于PET/CT及PET/CT+UE,PET/CT检测乳腺癌的特异性明显低于UE及PET/CT+UE,PET/CT+UE诊断乳腺癌的准确性显著提高(P0.05)。结论:PET/CT和超声弹性成像在乳腺癌诊断中均有较高的应用价值,各有优缺点,二者联合检测可提高乳腺癌诊断的准确率。     

Relationship of Psychiatric Diagnosis and Weight Loss Maintenance in Obese Breast Cancer Survivors

Objective: Obese breast cancer survivors are a unique population for weight loss counseling because both obesity and a diagnosis of breast cancer can increase the risk of depression. In this pilot study, weight loss maintenance was examined in obese breast cancer survivors with relationship to psychiatric diagnosis. Research Methods and Procedures: Forty‐eight subjects were enrolled. The intervention, which used individualized counseling for diet and exercise, lasted 24 months. After a 6‐month period of no contact with study subjects, a follow‐up body weight was obtained at 30 months. Results: The nine subjects who dropped out of the study before 12 months all failed to complete a structured psychiatric interview. Of the remaining 39 subjects, 9 had major depressive disorder, and 10 had a definable psychiatric disorder of lesser severity such as adjustment disorder. Subjects with any type of psychiatric diagnosis displayed significantly less weight loss at the 12‐month time‐point than those with no diagnosis (6.3% vs. 12.6% loss of baseline weight, respectively). At the 30‐month follow‐up visit, subjects with any psychiatric disorder had a mean weight loss of 1.2% of baseline weight compared with 7.8% weight loss in subjects with no diagnosis. Discussion: These results suggest that the presence of psychiatric disorders can interfere with weight loss. Therefore, recognition and treatment of psychiatric disorders may be important in attempts at weight reduction, and this will be especially important in populations such as cancer survivors, who seem to have higher rates of depression and other disorders than the general population.     

彩超联合钼靶在未扪及肿块乳腺癌诊断中的应用分析

目的:探讨彩色多普勒超声联合钼靶在未扪及肿块乳腺癌中的临床诊断价值。方法:选取我院乳腺科收治的未扪及肿块乳腺癌患者92例,回顾性分析92例患者术前彩色多普勒超声和钼靶检查结果与手术病理相印证,对比不同的检查方法与病理诊断结果的符合率。结果:单纯彩色多普勒超声检查与单纯钼靶机检查诊断准确率无明显差异(P0.05),而彩色多普勒超声联合钼靶机检查诊断准确率明显高于单纯彩色多普勒超声检查诊断准确率和单纯钼靶机检查诊断准确率,差异有统计学意义(P0.05)。结论:彩色多普勒超声联合钼靶能够明显提高未扪及肿块乳腺癌诊断的准确率,对临床具有指导意义,值得临床推广。     

Caveolin-1 和nm23在乳腺癌组织中的表达及意义

目的：探讨Caveolin-1 和nm23 在乳腺癌组织中的表达及意义。方法：选取于我院就诊的172 例乳腺癌患者的乳腺组织和40 例乳腺增生患者的正常乳腺组织,采用免疫组化技术检测标本中Caveolin-1和nm23的表达,并分析其与患者的临床病理之间的 关系。结果：免疫组化结果显示Caveolin-1 和nm23 在乳腺癌组织中的表达均低于正常乳腺组织（P<0.05）,且两者的表达呈正相 关（r= 0.609,P<0.05）;其中Caveolin-1 的表达与乳腺癌的临床分期及淋巴结转移有关（P<0.05）,nm23 的表达与乳腺癌的临床分 期、组织学类型及淋巴结转移有关（P<0.05）。结论：Caveolin-1 和nm23 的表达可能是乳腺癌发生发展的重要原因,可应用于临床 诊治乳腺癌患者。     

乳腺X 线摄影癌周透亮带的影像特征及其病理基础与价值

目的:研究乳腺X线摄影癌周透亮带影像学特征,分析其病理基础及临床意义。方法:回顾性分析2010年6月-2011年10月期间我院经手术病理证实为乳腺癌患者196例,筛选出术前进行过乳腺X线摄影检查并且图像上癌周出现透亮带征象的患者共47例51个病灶,测量肿块直径、癌周透亮带宽度等,与病理大体标本切面和镜下切片进行对比研究分析。结果:双乳病灶多分布于外上象限(19/51),临床触诊病灶大小平均值约35.45±1.25 mm。乳腺X摄影观察病灶均为肿块样,影像测量病灶大小平均值约20.49±1.18 mm,与临床触诊大小之间的差别具有统计学意义(t=2.85,P<0.01);肿块周围可观察到宽窄不均透亮带,平均宽度约15.07±0.86 mm,乳腺癌癌周透亮带宽度与肿块大小之间没有显著相关性(r=0.188,P=0.186)。病理大体标本观察病灶周围包绕一圈连续的黄色脂肪组织;HE染色镜下切片观察瘤灶周围为一圈成熟脂肪细胞,局部被瘤灶边缘增生的致密结缔组织为主的毛刺分割,脂肪组织中散在分布炎性细胞,部分区域见灶状癌细胞团浸润。结论:乳腺X线摄影癌周透亮带病理基础为伴随瘤周间质反应的富含脂肪的组织层,此征象对乳腺癌的诊断、以及临床评估肿瘤浸润范围具有一定意义。     

Caveolin-1和nm23在乳腺癌组织中的表达及意义

目的：探讨Caveolin-1和nm23在乳腺癌组织中的表达及意义。方法：选取于我院就诊的172例乳腺癌患者的乳腺组织和40例乳腺增生患者的正常乳腺组织,采用免疫组化技术检测标本中Caveolin．1和nm23的表达,并分析其与患者的临床病理之间的关系。结果：免疫组化结果显示Caveolin-1和nm23在乳腺癌组织中的表达均低于正常乳腺组织（P〈0．05）,且两者的表达呈正相关（r=0．609,P〈0．05）;其中Caveolin-1的表达与乳腺癌的临床分期及淋巴结转移有关（P〈0．05）,nm23的表达与乳腺癌的临床分期、组织学类型及淋巴结转移有关（P〈0．05）。结论：Caveolin-1和nm23的表达可能是乳腺癌发生发展的重要原因,可应用于临床诊治乳腺癌患者。     

Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning

Background and objectiveBreast cancer, the most intrusive form of cancer affecting women globally. Next to lung cancer, breast cancer is the one that provides a greater number of cancer deaths among women. In recent times, several intelligent methodologies were come into existence for building an effective detection and classification of such noxious type of cancer. For further improving the rate of early diagnosis and for increasing the life span of victims, optimistic light of research is essential in breast cancer classification. Accordingly, a new customized method of integrating the concept of deep learning with the extreme learning machine (ELM), which is optimized using a simple crow-search algorithm (ICS-ELM). Thus, to enhance the state-of-the-art workings, an improved deep feature-based crow-search optimized extreme learning machine is proposed for addressing the health-care problem. The paper pours a light-of-research on detecting the input mammograms as either normal or abnormal. Subsequently, it focuses on further classifying the type of abnormal severities i.e., benign type or malignant.Materials and methodsThe digital mammograms for this work are taken from the Curated Breast Imaging Subset of DDSM (CBIS-DDSM), Mammographic Image Analysis Society (MIAS), and INbreast datasets. Herein, the work employs 570 digital mammograms (250 normal, 200 benign and 120 malignant cases) from CBIS-DDSM dataset, 322 digital mammograms (207 normal, 64 benign and 51 malignant cases) from MIAS database and 179 full-field digital mammograms (66 normal, 56 benign and 57 malignant cases) from INbreast dataset for its evaluation. The work utilizes ResNet-18 based deep extracted features with proposed Improved Crow-Search Optimized Extreme Learning Machine (ICS-ELM) algorithm.ResultsThe proposed work is finally compared with the existing Support Vector Machines (RBF kernel), ELM, particle swarm optimization (PSO) optimized ELM, and crow-search optimized ELM, where the maximum overall classification accuracy is obtained for the proposed method with 97.193% for DDSM, 98.137% for MIAS and 98.266% for INbreast datasets, respectively.ConclusionThe obtained results reveal that the proposed Computer-Aided-Diagnosis (CAD) tool is robust for the automatic detection and classification of breast cancer.     

HIF-1alpha在乳腺癌中的表达及临床意义

目的：分析乳腺癌患者HIF-1alpha的表达水平与临床病理的关系,探讨HIF-1alpha表达水平对于乳腺癌诊治的意义。方法：选取我 院收治的乳腺癌患者共46 例作为实验组,随机选取同期收治的42 例乳腺良性病变患者作为对照组,所有患者均采取手术治疗, 手术中留取病变的乳腺组织,应用免疫组织化学法对乳腺组织中的HIF-1alpha表达水平进行检测,同时分析HIF-1alpha表达水平与乳 腺癌临床病理特征的相关性,并应用统计学软件对结果进行分析。结果：①实验组患者病变乳腺组织中HIF-1alpha表达水平（95.65 %）显著高于对照组（2.50 %）,差异具有显著性（P<0.05）;②HIF-1alpha表达水平与乳腺癌的患者的年龄、肿瘤直径以及雌激素受体状 态无显著相关（P＞0.05）;③HIF-1alpha表达水平与乳腺癌的临床分期、组织学分级以及淋巴结转移情况具有显著相关性（P<0.05）。结 论：乳腺癌患者的HIF-1alpha表达异常升高,其表达水平与癌组织分级、分期以及淋巴结转移情况密切相关,对预后不良具有一定的 提示作用,值得临床深入探讨。