The Application Value of Ultrasound Imaging in the Differential Diagnosis of Benign and Malignant Breast Nodules of BI-RADS 3 and Above

Objective:To explore the diagnostic value of ultrasound imaging for breast nodules of breast imaging-reporting and data system(BI-RADS)category 3 and above.Methods:From June 2021 to July 2022,163 patients with breast nodules of BI-RADS 3 or above were selected as the research subjects.After pathological diagnosis,24 cases were malignant breast nodules of BI-RADS 3 or above,while 139 cases were benign breast nodules of BI-RADS 3 or above.The diagnosis rate of malignant and benign breast nodules of BI-RADS 3 or above,including 95%CI,was observed and analyzed.Results:The malignant and benign detection rates of conventional ultrasound were 88.63%and 75.00%,respectively,and the malignant and benign detection rates of ultrasound imaging were 93.18%and 87.50%,respectively,with 95%CIs greater than 0.7.Conclusion:Ultrasound imaging can help improve the diagnostic accuracy of benign and malignant breast nodules of BI-RADS 3 and above and reduce the misdiagnosis rate.

Contrast-enhanced ultrasound improved performance of breast imaging reporting and data system evaluation of critical breast lesions

AIM: To determine whether contrast-enhanced ultrasound(CEUS) can improve the precision of breast imaging reporting and data system(BI-RADS) categorization. METHODS: A total of 230 patients with 235 solid breast lesions classified as BI-RADS 4 on conventional ultrasound were evaluated. CEUS was performed within one week before core needle biopsy or surgical resection and a revised BI-RADS classification was assigned based on 10 CEUS imaging characteristics. Receiver operating characteristic curve analysis was then conducted to evaluate the diagnostic performance of CEUS-based BI-RADS assignment with pathological examination as reference criteria. RESULTS: The CEUS-based BI-RADS evaluation classified 116/235(49.36%) lesions into category 3, 20(8.51%), 13(5.53%) and 12(5.11%) lesions into categories 4A, 4B and 4C, respectively, and 74(31.49%) into category 5. Selecting CEUS-based BI-RADS category 4A as an appropriate cut-off gave sensitivity and specificity values of 85.4% and 87.8%, respectively, for the diagnosisof malignant disease. The cancer-to-biopsy yield was 73.11% with CEUS-based BI-RADS 4A selected as the biopsy threshold compared with 40.85% otherwise, while the biopsy rate was only 42.13% compared with 100% otherwise. Overall, only 4.68% of invasive cancers were misdiagnosed.CONCLUSION: This pilot study suggests that evaluation of BI-RADS 4 breast lesions with CEUS results in reduced biopsy rates and increased cancer-to-biopsy yields.

Predictive model for contrast-enhanced ultrasound of the breast: Is it feasible in malignant risk assessment of breast imaging reporting and data system 4 lesions?

AIM: To build and evaluate predictive models for contrast-enhanced ultrasound(CEUS) of the breast to distinguish between benign and malignant lesions. METHODS: A total of 235 breast imaging reporting and data system(BI-RADS) 4 solid breast lesions were imaged via CEUS before core needle biopsy or surgical resection. CEUS results were analyzed on 10 enhancing patterns to evaluate diagnostic performance of three benign and three malignant CEUS models, with pathological results used as the gold standard. A logistic regression model was developed basing on the CEUS results, and then evaluated with receiver operating curve(ROC). RESULTS: Except in cases of enhanced homogeneity, the rest of the 9 enhancement appearances were statistically significant(P < 0.05). These 9 enhancement patterns were selected in the final step of the logistic regression analysis, with diagnostic sensitivity and specificity of 84.4% and 82.7%, respectively, and the area under the ROC curve of 0.911. Diagnostic sensitivity, specificity, and accuracy of the malignant vs benign CEUS models were 84.38%, 87.77%, 86.38% and 86.46%, 81.29% and 83.40%, respectively. CONCLUSION: The breast CEUS models can predict risk of malignant breast lesions more accurately, decrease false-positive biopsy, and provide accurate BIRADS classification.

Effects of subsampling on characteristics of RNA-seq data from triple-negative breast cancer patients

Background:Data from RNA-seq experiments provide a wealth of information about the transcriptome of an organism.However,the analysis of such data is very demanding.In this study,we aimed to establish robust analysis procedures that can be used in clinical practice.Methods:We studied RNA-seq data from triple-negative breast cancer patients.Specifically,we investigated the subsampling of RNA-seq data.Results:The main results of our investigations are as follows:(1) the subsampling of RNA-seq data gave biologically realistic simulations of sequencing experiments with smaller sequencing depth but not direct scaling of count matrices;(2) the saturation of results required an average sequencing depth larger than 32 million reads and an individual sequencing depth larger than 46 million reads;and(3) for an abrogated feature selection,higher moments of the distribution of all expressed genes had a higher sensitivity for signal detection than the corresponding mean values.Conclusions:Our results reveal important characteristics of RNA-seq data that must be understood before one can apply such an approach to translational medicine.

Research spot and trend of integrative nursing in breast diseases based on data mining

Objective:To explore the development status,research hotspots and future development trend of traditional Chinese medicine nursing in the field of breast diseases by the bibliometric method.Methods:using CNKI,Wanfang,VIP and SINOMED databases as data sources,the literature related to breast traditional Chinese medicine nursing were obtained by keyword search,and BICOMB 2.01 was used to extract and count the information such as publication year,keywords and so on,and the word matrix was formed.SPSS 21.0 was used to cluster the extracted keywords.Results:a total of 839 articles were included,which came from 348 journals and 48 high-frequency words.Four main research topics were formed by keyword clustering:TCM nursing of breast cancer,TCM nursing of breast abscess/mass,TCM nursing of postpartum breast,TCM nursing of acute mastitis.Conclusion:the TCM nursing literature in the field of breast disease is increasing rapidly,but theoverall quality of the published literature is low,so more scientific and standardized TCM nursing original research is urgently needed.

Breast Cancer Detection Using Breastnet-18 Augmentation with Fine Tuned Vgg-16

Women from middle age to old age are mostly screened positive for Breast cancer which leads to death.Times over the past decades,the overall sur-vival rate in breast cancer has improved due to advancements in early-stage diag-nosis and tailored therapy.Today all hospital brings high awareness and early detection technologies for breast cancer.This increases the survival rate of women.Though traditional breast cancer treatment takes so long,early cancer techniques require an automation system.This research provides a new methodol-ogy for classifying breast cancer using ultrasound pictures that use deep learning and the combination of the best characteristics.Initially,after successful learning of Convolutional Neural Network(CNN)algorithms,data augmentation is used to enhance the representation of the feature dataset.Then it uses BreastNet18 withfine-tuned VGG-16 model for pre-training the augmented dataset.For feature classification,Entropy controlled Whale Optimization Algorithm(EWOA)is used.The features that have been optimized using the EWOA were utilized to fuse and optimize the data.To identify the breast cancer pictures,training classifiers are used.By using the novel probability-based serial technique,the best-chosen characteristics are fused and categorized by machine learning techniques.The main objective behind the research is to increase tumor prediction accuracy for saving human life.The testing was performed using a dataset of enhanced Breast Ultrasound Images(BUSI).The proposed method improves the accuracy com-pared with the existing methods.

Decipher Clinical and Genetic Underpins of Breast Cancer Survival with Machine Learning Methods

Breast cancer is one of the most common cancers among women in the world, with more than two million new cases of breast cancer every year. This disease is associated with numerous clinical and genetic characteristics. In recent years, machine learning technology has been increasingly applied to the medical field, including predicting the risk of malignant tumors such as breast cancer. Based on clinical and targeted sequencing data of 1980 primary breast cancer samples, this article aimed to analyze these data and predict living conditions after breast cancer. After data engineering, feature selection, and comparison of machine learning methods, the light gradient boosting machine model was found the best with hyperparameter tuning (precision = 0.818, recall = 0.816, f1 score = 0.817, roc-auc = 0.867). And the top 5 determinants were clinical features age at diagnosis, Nottingham Prognostic Index, cohort and genetic features rheb, nr3c1. The study shed light on rational allocation of medical resources and provided insights to early prevention, diagnosis and treatment of breast cancer with the identified risk clinical and genetic factors.

超声乳腺影像报告和数据系统联合患者年龄鉴别良性与交界性/恶性乳腺叶状肿瘤

目的观察超声乳腺影像报告和数据系统(BI-RADS)联合患者年龄鉴别良性与交界性/恶性乳腺叶状肿瘤(PTB)的价值。方法回顾性分析74例经乳腺超声检查及术后病理证实的PTB女性患者共76个病灶,包括良性57个、交界性14个及恶性5个,比较3种病变患者年龄及其超声BI-RADS分类,观察以单一年龄、超声BI-RADS及其联合鉴别诊断价值。结果良性、交界性及恶性PTB之间,患者年龄及超声BI-RADS分类差异均有统计学意义(P=0.026、0.015)。以44.5岁、超声BI-RADS 4B类及其联合鉴别良性与交界性/恶性PTB的敏感度分别为94.74%、36.84%及73.68%,特异度分别为56.36%、87.27%及72.73%,曲线下面积分别为0.769、0.649及0.780。结论超声BI-RADS分类联合患者年龄有助于鉴别良性与交界性/恶性PTB。

基于数据挖掘探析陈焕朝教授治疗乳腺癌用药经验

目的运用数据挖掘技术探析湖北省名老中医陈焕朝教授治疗乳腺癌用药经验。方法收集陈焕朝教授治疗乳腺癌病案,经筛选、整理后录入患者首诊处方。运用Excel 2019对药物频次、功效频次和性味归经进行分析,采用SPSS Modeler 18.0软件对药物进行关联规则分析及网络展示,利用SPSS 27.0软件对高频药物进行聚类分析,总结用药规律。结果共纳入处方185首,涉及中药180味,筛选出高频药物29味。药性以平、寒、温为主,药味以甘、苦、辛为主,归经多归肝经、肺经、脾经、肾经,药物功效以补虚药、清热药、利水渗湿药为主。关联规则分析得到20组药对,聚类分析主要呈现2个类别。结论陈焕朝教授认为乳腺癌属本虚标实,应从“虚、瘀、痰、毒”论治,扶正祛邪兼施,标本兼治,重视疏肝理气,调补脾肾,兼以活血化瘀、化痰消积、清热解毒,为中医药治疗乳腺癌提供思路。

王笑民教授治疗乳腺癌处方用药规律探析

目的统计和收录王笑民教授治疗乳腺癌的病案,分析王教授诊治乳腺癌的思路及处方用药经验。方法选择2023年3月至2024年3月就诊于首都医科大学附属北京中医医院肿瘤科门诊诊断明确的激素受体阳性乳腺癌术后患者,经筛选后采集数据录入中医传承辅助平台(V 2.5),采用软件“统计报表”及“数据分析”功能进行数据挖掘,分析王教授治疗乳腺癌常用药物、性味归经、核心处方及用药规律。结果共收集313首有效处方,涉及145味中药,出现频次排前3位分别为半枝莲、白花蛇舌草和黄芩。药物四气分布以寒、温、平为主;五味分布以甘、苦、辛常见;归经分布以肝、脾经为最多。最终得出5个药物核心组合及5首新处方,其中有重要临床意义的新处方2首。结论王教授治疗乳腺癌术后患者主要从肝、脾入手,以疏肝健脾、清热解毒、软坚散结和引火归元治则为主。

2013年版超声BI-RADS出版10年:回顾与展望

自2013年美国放射学会出版第二版乳腺影像报告和数据系统(Breast Imaging Reporting and Data System,BI-RADS)后,乳腺超声的临床实践与科学研究均从中获益。本文总结了2013年版超声BI-RADS出版这10年间,乳腺超声影像技术临床应用与革新、存在的问题与面临的挑战及未来的发展机遇,以期为临床诊治、指南推广与应用提供帮助。

自动乳腺容积超声成像在BI-RADS4A类乳腺结节诊断中的价值

目的探讨自动乳腺容积超声成像(ABUS)在乳腺影像报告和数据系统(BI-RADS)4A类乳腺结节良、恶性诊断中的应用价值。方法回顾性分析广东省中医院2018年1月至2022年9月经ABUS发现乳腺BI-RADS 4A类结节的202例女性患者,共216个乳腺结节[结节最大径(17.92±11.60)mm],年龄23~72岁。患者均接受ABUS、常规超声、乳腺X线摄影,并根据美国放射学会(ACR)建立的2013年版BI-RADS进行分类,以术后病理结果作为金标准,分析3种检查方法的诊断效能。计数资料的比较采用χ^(2)检验或Fisher确切概率法。结果经手术病理证实,178个结节为良性,38个结节为恶性,ABUS、常规超声、乳腺X线摄影诊断BI-RADS 4A类结节的恶性构成比分别为17.59%(38/216)、12.94%(22/170)、19.61%(10/51),3种检查诊断BI-RADS 4A类结节的恶性构成比进行组间比较(ABUS与常规超声、ABUS与X线、常规超声与X线),差异均无统计学意义(χ^(2)=1.568、0.114、1.408,均P>0.05)。结论ABUS对BI-RADS 4A类乳腺结节的诊断效能与常规超声、乳腺X线摄影相仿,ABUS在早期乳腺癌筛查中有一定临床价值,有利于基层单位乳腺疾病筛查及疾病分级管理。

用卷积神经网络U-Net分割对比增强锥光束乳腺CT数据

乳腺肿瘤是一种常见的恶性肿瘤,其临床诊断不但费时费力,还容易出现误诊.本文旨在建立一个基于乳腺数据自动分割的乳腺肿瘤计算机辅助诊断模型,提高临床诊断的速度和准确率.为了用卷积神经网络U-Net模型分割对比增强锥光束乳腺计算机断层扫描(Contrast-Enhanced Cone-Beam Breast CT,CE-CBBCT)数据,本文首先沿冠状面将3维数据转换成2维切片,通过数据默认的窗口对其进行归一化处理.实验结果显示,使用U-Net卷积神经网络对数据进行分割,Dice系数和IoU(Intersection over Union)分别为0.7920和0.6962.然后,本文用不同骨干网络(即各种深度学习分类网络)去替换U-Net的编码器,再次进行分割并对比不同特征提取对分割性能的影响,发现旋转增广方式可以提升各分割网络的性能.其中,基于ResNet152的U形分割网络的性能最好,Dice系数和IoU分别达到0.8410和0.7576.随后,本文又在所有模型中选取5个性能最好的模型组成一个集成模型,重复分割实验,发现此模型有最佳分割性能,平均Dice系数和IoU可达0.8463和0.7676,性能显著提升.值得指出的是,在处理数据时本文仅使用数据默认的窗口,降低了对人工的依赖.

超声造影联合年龄在乳腺BI-RADS 4类结节再分类中的应用价值

目的 探讨超声造影联合年龄在乳腺影像学报告及数据系统(BI-RADS)4类结节再分类中的应用价值。方法 收集成都市第三人民医院常规超声分类为BI-RADS 4类,且经过超声造影评分并有病理结果的116个乳腺结节患者的临床资料。以病理诊断为金标准,构建受试者工作特征(ROC)曲线,计算超声造影评分及年龄诊断乳腺良恶性结节的最佳截断值,并分析其诊断价值。利用超声造影评分及年龄的最佳截断值,对116个BI-RADS 4类结节重新进行分类调整。结果 通过绘制ROC曲线得到超声造影评分的最佳截断值为3.5分,年龄的最佳截断值为50.5岁。超声造影评分的ROC曲线下面积为0.887,其诊断乳腺结节良恶性的敏感性及特异性分别为80.8%、96.7%;年龄的ROC曲线下面积为0.793,其诊断乳腺结节良恶性的敏感性及特异性分别为73.1%、85.6%。BI-RADS 4类结节经超声造影评分联合年龄临界值重新调整后,59个结节降为3类,病理结果均为良性;11个结节升为4c类,病理结果均为恶性。结论 超声造影评分联合年龄对BI-RADS 4类结节进行重新分类后可有效提高乳腺结节诊断的准确性,能为临床诊疗提供参考。

基于数据挖掘探讨古今医家治疗乳腺癌的用药规律及差异

目的探讨古今医家运用中医药治疗乳腺癌的处方用药规律及差异。方法古方以鼎秀古籍全文检索平台、中医药基本古籍数据库(博古通儒)、古今医案云平台为检索来源;今方以中国知网数据库(CNKI)、万方数据库、维普数据库(VIP)为检索来源,筛选建库至2022年9月发表文章中涉及的治疗乳腺癌方剂。应用古今医案云平台对纳入方剂包含的中药进行频次、性味归经、关联分析、复杂网络分析和聚类分析。结果古今医家治疗乳腺癌使用频次靠前的中药均有当归、甘草、白芍、茯苓、白术,均为温、平、寒性,甘、苦、辛味药物应用较多,归经以脾、肝、肺经较多。古代医家治疗乳腺癌的高频药对为白芍—当归,现代医家治疗乳腺癌的高频药对为白术—茯苓。复杂网络分析显示,古代医家治疗乳腺癌的核心处方为“白术、茯苓、白芍、当归、川芎、人参、熟地黄、甘草”,现代医家治疗乳腺癌的核心处方为“白术、黄芪、茯苓、当归、白芍、柴胡、党参”。聚类分析显示,古代医家治疗乳腺癌相关高频药物共得到5个新方,现代医家治疗乳腺癌相关高频药物得到3个新方。结论古今医家治疗乳腺癌均遵循“扶正固本”的原则。在此基础上,古代医家侧重健脾益气,现代医家侧重疏肝健脾。

多模态超声校正BI-RADS 4类乳腺肿块的价值

目的 探讨乳腺自动容积成像(ABVS)、超声弹性评分(UES)及两者联合在校正乳腺影像报告和数据系统分级(BI-RADS) 4类乳腺肿块BI-RADS分级中的应用价值。方法 收集我院经常规超声诊断为BI-RADS 4类的乳腺肿块患者109例,共113个肿块。经ABVS及UES校正BI-RADS分级后,与病理结果对比,绘制ROC曲线,比较常规超声、ABVS、UES、ABVS联合UES诊断BI-RADS 4类乳腺肿块的差异。结果 109例患者113个肿块中包含良性78个,恶性35个,ABVS联合UES校正后的敏感度、特异性、准确性、ROC曲线下面积分别为94.29%、93.59%、93.80%、0.975。结论 ABVS联合UES有助于提高BI-RADS 4类肿块的总体诊断效能。两者联合诊断可以取长补短,提高诊断率。

自动乳腺全容积扫描影像组学联合临床和超声特征列线图鉴别良、恶性乳腺导管内病变

目的 观察自动乳腺全容积扫描(ABVS)影像组学联合临床及超声特征列线图鉴别良、恶性乳腺导管内病变的价值。方法 回顾性分析144例经病理证实乳腺导管内病变女性患者的临床及超声资料;按照2∶1比例将其随机分为训练集(n=96)及验证集(n=48)。基于ABVS图像提取并筛选最优影像组学特征,构建影像组学模型,计算影像组学评分(Radscore);将临床、超声特征及Radscore纳入单因素和多因素logistic回归分析,筛选鉴别良、恶性乳腺导管内病变的独立影响因素,构建临床-超声模型,并联合影像组学模型构建列线图模型;以受试者工作特征(ROC)曲线评估各模型鉴别良、恶性乳腺导管内病变的效能。结果 患者年龄[OR(95%CI)=1.104(1.045,1.180),P=0.001]、病变边缘[OR(95%CI)=0.273(0.075,0.917),P=0.039]、微小钙化灶[OR(95%CI)=9.759(2.240,60.730),P=0.006]及Radscore[OR(95%CI)=3.818(1.435,11.994),P=0.012]均为良、恶性乳腺导管内病变的独立影响因素。影像组学模型、临床-超声模型及列线图模型鉴别良、恶性乳腺导管内病变的曲线下面积(AUC)在训练集分别为0.766、0.866及0.901,在验证集分别为0.770、0.765及0.854。结论 ABVS影像组学联合临床及超声特征列线图鉴别良、恶性乳腺导管内病变效能良好。

基于数据挖掘分析古方治疗乳腺癌的用药规律

目的应用统计学与数据挖掘的方法,探讨古方治疗乳腺癌的用药规律,以便为当前临床实践提供一定参考。方法采用计算机检索中国方剂数据库、中国知网、维普数据库、万方数据库及手工查阅古籍的方法,将自晋代到唐代明确提出治疗乳岩的29首古方纳入分析,应用Microsoft Excel 2021、SPSS Statistic 24.0、SPSS Modeler 18.0软件进行频数、聚类及关联分析。结果纳入分析的29首中药古方总用药频次为191次,古方涉及中药共93味。古方中最常用的中药类别为补虚药、清热药、解表药,甘草、大黄、防风等24味中药是高频药物,使用频次占所有涉及药味的54.97%,药性以寒居多,药味以甘、苦为主,归经以脾、心、肺三经为主。高频药物聚类分析结果形成3个新方,关联规则分析,支持度≥10.00%,得出常见核心用药组合13个,关联规则14条。结论古方在乳腺癌的治疗上以益气养血、疏肝解郁、清热解毒等法则为主。

超声新技术辅助诊断BI-RADS 4类病变的应用进展

乳腺影像-报告和数据系统(BI-RADS)4类良、恶性病变的常规超声表现存在一定重叠,易致不必要的穿刺或延误治疗;准确判断乳腺病变性质可为临床决策提供可靠参考。本文围绕应用自动乳腺全容积扫描、超微血管成像、超声弹性成像、超声造影及人工智能等超声新技术辅助诊断BI-RADS 4类病变的研究进展进行综述。

二维超声结合临床特征预测乳腺癌腋窝淋巴结转移的单中心前瞻性研究

目的探讨二维超声结合临床特征对乳腺癌患者腋窝淋巴结(ALN)转移的预测价值,并分析乳腺癌ALN转移的危险因素。方法前瞻性收集2022年5月至2023年8月四川大学华西医院乳腺疾病中心收治的335例资料齐全且首次治疗的浸润性乳腺癌患者作为研究对象,收集所有患者术前的ALN临床特征、乳腺超声检查结果、术前穿刺活检结果,分析乳腺癌患者ALN转移的危险因素,并初步建立乳腺癌患者ALN转移的预测模型。结果335例乳腺癌患者中159例有ALN转移,176例无ALN转移。单因素分析结果显示,淋巴结形态、淋巴结边缘形态(清楚/模糊)、皮质厚度、皮质厚度不均匀等均为ALN转移的危险因素;多因素分析结果显示,激素受体为HR+、肿瘤浸润乳腺后间隙、淋巴结短径>8 mm、淋巴结长径/短径≤2、淋巴结皮髓质分界模糊为乳腺癌ALN转移的独立影响因素;二维超声结合临床特征的预测模型预测ALN状态的AUC为0.870(95%CI:0.832~0.908,P<0.001)。结论乳腺癌激素受体为HR+、肿瘤浸润乳腺后间隙、淋巴结短径>8 mm、淋巴结长径/短径≤2、淋巴结皮髓质分界模糊可能是判断乳腺癌ALN转移的重要指标;联合应用可较好预测乳腺癌ALN转移的状态。