A Novel Approach to Breast Tumor Detection: Enhanced Speckle Reduction and Hybrid Classification in Ultrasound Imaging

Breast cancer detection heavily relies on medical imaging, particularly ultrasound, for early diagnosis and effectivetreatment. This research addresses the challenges associated with computer-aided diagnosis (CAD) of breastcancer fromultrasound images. The primary challenge is accurately distinguishing between malignant and benigntumors, complicated by factors such as speckle noise, variable image quality, and the need for precise segmentationand classification. The main objective of the research paper is to develop an advanced methodology for breastultrasound image classification, focusing on speckle noise reduction, precise segmentation, feature extraction, andmachine learning-based classification. A unique approach is introduced that combines Enhanced Speckle ReducedAnisotropic Diffusion (SRAD) filters for speckle noise reduction, U-NET-based segmentation, Genetic Algorithm(GA)-based feature selection, and Random Forest and Bagging Tree classifiers, resulting in a novel and efficientmodel. To test and validate the hybrid model, rigorous experimentations were performed and results state thatthe proposed hybrid model achieved accuracy rate of 99.9%, outperforming other existing techniques, and alsosignificantly reducing computational time. This enhanced accuracy, along with improved sensitivity and specificity,makes the proposed hybrid model a valuable addition to CAD systems in breast cancer diagnosis, ultimatelyenhancing diagnostic accuracy in clinical applications.

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.

The Epidemiological, Clinical and Radiological (Echography, Mammography) Characteristics of Breast Pathologies at the Diagnostic Imaging Center (C.I.D) “Teriya” in Bamako

Breast pathology is varied, bringing together tumor and non-tumor lesions. Objective: To study the contribution of the ultrasound-mammography pair in the diagnosis of breast pathologies. Materials and Method: This was a retrospective descriptive study, carried out over a period of 3 years (from January 2018 to December 2020) at the Diagnostic Imaging Center (C.I.D) “TERIYA” in BAMAKO. It concerned all patients who came for a mammogram/ultrasound examination of the breast. All women admitted for mammogram or breast ultrasound who were diagnosed with a breast injury during the study period were included. Incomplete records and radiological checks were not included. The variables analyzed were age, sex, clinical data, and ultrasound and mammography aspects. The devices used are: a Voluson 730 PRO ultrasound machine and a G 600T type mammography machine. Results: At the end of our study, we collected 254 breast pathologies on a number of 382 women, i.e. a frequency of 66.49%. The average age of our patients was 41 years old. The dominant clinical data were mastodynia (41.88%) and mammary nodule (21.70%). On imaging (mammo-ultrasound) the lesions predominated on the left in 36% of cases, bilateral in 28% of cases and in the upper-outer quadrants in 31.5% of cases. Tumor pathologies represented 66.54% of which 45.27% were benign mainly composed of fibro-adenoma (20.88%) and cyst (18.50%), 11.8% of suspected cases and 9.45% of cancers. Non-tumor pathologies represented 33.46%, mainly mastitis (16.14%), galactophoric dilations (11.02%) and abscesses (5.51%). These pathologies were classified in 50.3% in ACR2, 17.75% in ACR3 and 4, and in 14.20% in ACR5. Lymphadenopathy was present in 73.21% of cases.

Automatic Optimization of Chirp Setting Parameters in Medical Ultrasound Contrast Imaging

Medical ultrasound contrast imaging is a powerful modality undergoing successive developments in the last decade to date Lately, pulse inversion has been used in both ultrasound tissue harmonic and contrast imaging. However, there was a tradeoff between resolution and penetration. Chirp excitations partially solved the tradeoff, but the chirp setting parameters were not optimized. The present work proposes for the first time combining chirp inversion with ultrasound contrast imaging, with the motivation to improve the contrast, by automatically optimizing the setting parameters of chirp excitation, it is thus an optimal command problem. Linear chirps, 5 μm diameter microbubbles and gradient ascent algorithm were simulated to optimize the chirp setting parameters. Simulations exhibited a gain of 5 dB by automatic optimization of chirp inversion relative to pulse inversion. The automatic optimization process was quite fast. Combining chirp inversion with ultrasound contrast imaging led to a maximum backscattered power permitting high contrast outcomes and optimum parameters.

The accuracy of magnetic resonance imaging and ultrasound in evaluating the size of early-stage breast neoplasms

Objective Breast cancer is the most frequently diagnosed cancer in women. Accurate evaluation of the size and extent of the tumor is crucial in selecting a suitable surgical method for patients with breast cancer. Both overestimation and underestimation have important adverse effects on patient care. This study aimed to evaluate the accuracy of breast magnetic resonance imaging(MRI) and ultrasound(US) examination for measuring the size and extent of early-stage breast neoplasms.Methods The longest diameter of breast tumors in patients with T_(1–2)N_(0–1)M_0 invasive breast cancer preparing for breast-conserving surgery(BCS) was measured preoperatively by using both MRI and US and their accuracy was compared with that of postoperative pathologic examination. If the diameter difference was within 2 mm, it was considered to be consistent with pathologic examination.Results A total of 36 patients were imaged using both MRI and US. The mean longest diameter of the tumors on MRI, US, and postoperative pathologic examination was 20.86 mm ± 4.09 mm(range: 11–27 mm), 16.14 mm ± 4.91 mm(range: 6–26 mm), and 18.36 mm ± 3.88 mm(range: 9–24 mm). US examination underestimated the size of the tumor compared to that determined using pathologic examination(t = 3.49, P < 0.01), while MRI overestimated it(t =-6.35, P < 0.01). The linear correlation coefficients between the image measurements and pathologic tumor size were r = 0.826(P < 0.01) for MRI and r = 0.645(P < 0.01) for US. The rate of consistency of MRI and US compared to that with pathologic examination was 88.89% and 80.65%, respectively, and there was no statistically significant difference between them(χ~2 = 0.80, P > 0.05).Conclusion MRI and US are both effective methods to assess the size of breast tumors, and they maintain good consistency with pathologic examination. MRI has a better correlation with pathology. However, we should be careful about the risk of inaccurate size estimation.

Fully Automatic Segmentation of Gynaecological Abnormality Using a New Viola–Jones Model

One of the most complex tasks for computer-aided diagnosis(Intelligent decision support system)is the segmentation of lesions.Thus,this study proposes a new fully automated method for the segmentation of ovarian and breast ultrasound images.The main contributions of this research is the development of a novel Viola–James model capable of segmenting the ultrasound images of breast and ovarian cancer cases.In addition,proposed an approach that can efficiently generate region-of-interest(ROI)and new features that can be used in characterizing lesion boundaries.This study uses two databases in training and testing the proposed segmentation approach.The breast cancer database contains 250 images,while that of the ovarian tumor has 100 images obtained from several hospitals in Iraq.Results of the experiments showed that the proposed approach demonstrates better performance compared with those of other segmentation methods used for segmenting breast and ovarian ultrasound images.The segmentation result of the proposed system compared with the other existing techniques in the breast cancer data set was 78.8%.By contrast,the segmentation result of the proposed system in the ovarian tumor data set was 79.2%.In the classification results,we achieved 95.43%accuracy,92.20%sensitivity,and 97.5%specificity when we used the breast cancer data set.For the ovarian tumor data set,we achieved 94.84%accuracy,96.96%sensitivity,and 90.32%specificity.

Diagnostic performance of contrast-enhanced ultrasound and enhanced magnetic resonance for breast nodules

In the current study, we sought to evaluate the diagnostic efficacies of conventional ultrasound（US）, contrastenhanced US（CEUS）, combined US and CEUS and magnetic resonance imaging（MRI） in detecting focal solid breast lesions. Totally 117 patients with 120 BI-RADS category 4A-5 breast lesions were evaluated by conventional US and CEUS, and MRI, respectively. SonoVue was used as contrast agent in CEUS and injected as an intravenous bolus; nodule scan was performed 4 minutes after bolus injection. A specific sonographic quantification software was used to obtain color-coded maps of perfusion parameters for the investigated lesion, namely the time-intensity curve.The pattern of contrast enhancement and related indexes regarding the time-intensity curve were used to describe the lesions, comparatively with pathological results. Histopathologic examination revealed 46 benign and 74 malignant lesions. Sensitivity, specificity, and accuracy of US in detecting malignant breast lesions were 90.14%, 95.92%, and 92.52%, respectively. Meanwhile, CE-MRI showed sensitivity, specificity, and accuracy of 88.73%, 95.92%, and91.67%, respectively. The area under the ROC curve for combined US and CEUS in discriminating benign from malignant breast lesions was 0.936, while that of MRI was 0.923, with no significant difference between them, as well as among groups. The time-intensity curve of malignant hypervascular fibroadenoma and papillary lesions mostly showed a fast-in/fast-out pattern, with no good correlation between them（kappa 〈0.20）. In conclusion, the combined use of conventional US and CEUS displays good agreement with MRI in differentiating benign from malignant breast lesions.

Magnetic resonance image-guided versus ultrasound-guided high-intensity focused ultrasound in the treatment of breast cancer

Image-guided high-intensity focused ultrasound (HIFU) has been used for more than ten years, primarily in the treatment of liver and prostate cancers. HIFU has the advantages of precise cancer ablation and excellent protection of healthy tissue. Breast cancer is a common cancer in women. HIFU therapy, in combination with other therapies, has the potential to improve both oncologic and cosmetic outcomes for breast cancer patients by providing a curative therapy that conserves mammary shape. Currently, HIFU therapy is not commonly used in breast cancer treatment, and efforts to promote the application of HIFU is expected. In this article, we compare different image-guided models for HIFU and reviewed the status, drawbacks, and potential of HIFU therapy for breast cancer.

Breast imaging:A survey

Breast cancer is the second leading cause of death in women.It occurs when cells in the breast start to grow out of proportion and invade neighboring tissues or spread throughout the body.Mammography is one of the most effective and popular modalities presently used for breast cancer screening and detection.Efforts have been made to improve the accuracy of breast cancer diagnosis using different imaging modalities.Ultrasound and magnetic resonance imaging have been used to detect breast cancers in high risk patients.Recently,electrical impedance imaging and nuclear medicine techniques are also being widely used for breast cancer screening and diagnosis.In this paper,we discuss the capabilities of various breast imaging modalities.

Application Value of Contrast-Enhanced Ultrasound in the Diagnosis of Breast Lesions

Objective:To explore the value and effect of contrast-enhanced ultrasound in the diagnosis of breast lesions.Methods:Seventy-two patients with breast lesions in Shaanxi Provincial People’s Hospital from June 2020 to December 2021 were selected as the research subjects.All 72 patients met the diagnostic criteria of breast lesions.Two patients with incomplete clinical data were excluded;hence,there were 70 patients remaining.The diagnostic results of the two examination methods and the diagnostic value of the joint examination for breast lesions were analyzed and compared.Results:The results of benign,malignant,missed,and misdiagnosed breast lesions by contrast-enhanced ultrasound were 31,32,6,and 1 cases,respectively,accounting for 44.29%,45.71%,8.57%,and 1.43%,respectively.The results of benign,malignant,missed,and misdiagnosed breast lesions by ultrasound automatic volume imaging were 21,24,17,and 8 cases,respectively,accounting for 30.00%,34.28%,24.29%,and 11.43%,respectively.There were statistical differences between the two groups for missed diagnosis and misdiagnosis,but there was no significant difference between the two groups for benign and malignant lesions.The accuracy,sensitivity,and specificity of contrast-enhanced ultrasound were 87.69%,83.62%,and 83.45%,respectively;the accuracy,sensitivity,and specificity of ultrasound automatic volume imaging were 71.39%,68.99%,and 74.69%,respectively;the accuracy,sensitivity,and specificity of contrast-enhanced ultrasound combined with ultrasound automatic volume imaging were 96.29%,92.68%,and 91.78%,respectively.Conclusion:Contrast-enhanced ultrasonography has a high clinical application value and a low inspection error rate in the diagnosis of breast lesions.It merits clinical advancement since it helps doctors diagnose and treat breast lesions more effectively.

Pre-Trained Deep Neural Network-Based Computer-Aided Breast Tumor Diagnosis Using ROI Structures

Deep neural network(DNN)based computer-aided breast tumor diagnosis(CABTD)method plays a vital role in the early detection and diagnosis of breast tumors.However,a Brightness mode(B-mode)ultrasound image derives training feature samples that make closer isolation toward the infection part.Hence,it is expensive due to a metaheuristic search of features occupying the global region of interest(ROI)structures of input images.Thus,it may lead to the high computational complexity of the pre-trained DNN-based CABTD method.This paper proposes a novel ensemble pretrained DNN-based CABTD method using global-and local-ROI-structures of B-mode ultrasound images.It conveys the additional consideration of a local-ROI-structures for further enhan-cing the pretrained DNN-based CABTD method’s breast tumor diagnostic performance without degrading its visual quality.The features are extracted at various depths(18,50,and 101)from the global and local ROI structures and feed to support vector machine for better classification.From the experimental results,it has been observed that the combined local and global ROI structure of small depth residual network ResNet18(0.8 in%)has produced significant improve-ment in pixel ratio as compared to ResNet50(0.5 in%)and ResNet101(0.3 in%),respectively.Subsequently,the pretrained DNN-based CABTD methods have been tested by influencing local and global ROI structures to diagnose two specific breast tumors(Benign and Malignant)and improve the diagnostic accuracy(86%)compared to Dense Net,Alex Net,VGG Net,and Google Net.Moreover,it reduces the computational complexity due to the small depth residual network ResNet18,respectively.

超声造影与DCE-MRI对乳腺癌患者新辅助化疗的评估价值

目的探究超声造影(CEUS)与动态增强磁共振成像(DCE-MRI)对乳腺癌患者新辅助化疗(NAC)的评估价值。方法选取2020年1月~2023年1月于我院接受NAC的乳腺癌患者68例,对所有入选患者NAC治疗后行CEUS、DCE-MRI检查,以手术病理结果为金标准,将治疗后患者分为有效组与无效组,比较CEUS、DCE-MRI以及两者联合对乳腺癌患者NAC疗效的评估价值,对比有效组与无效组CEUS、DCEMRI检查的参数水平。结果68例乳腺癌患者NAC后,经手术病理诊断发现49例有效,19例无效。采用CEUS检查评估NAC疗效的敏感性、特异性及准确性分别为81.63%、68.42%、67.65%,经手术病理评估为有效患者的峰值强度(PI)、曲线下面积(AUC)水平低于无效患者(P<0.05);采用DCE-MRI检查时的敏感性、特异性及准确性分别为83.67%、73.68%、67.65%,经手术病理评估为有效患者的容量转移常数(K^(trans))、速率常数(K_(ep))均低于无效患者(P<0.05)。CEUS联合DCE-MRI评估NAC疗效的敏感性及准确性分别为97.96%、94.83%,均显著高于单一检查方法(P<0.05)。结论CEUS联合DCE-MRI能提高对乳腺癌患者NAC疗效的评估价值,其敏感性及准确性较高,值得推广。

多种超声影像技术在乳腺癌诊断中的研究进展

乳腺癌是我国女性发病率最高的恶性肿瘤,常规超声是乳腺癌筛查常用的影像检查方法。弹性成像、超声造影、自动乳腺超声成像系统、S-detect技术及超声引导下穿刺活检等新技术正在飞速发展并逐渐应用于临床,为乳腺癌的诊断提供了更有价值的信息。本文就多种超声影像技术诊断乳腺癌的临床应用进展进行综述。

高频超声联合磁共振扩散加权成像对乳腺癌预后的预测价值

目的 探讨高频超声联合磁共振扩散加权成像对乳腺癌预后的预测价值。方法 选取本院2020年7月到2022年12月收治的95例乳腺癌患者作为研究对象,均行根治性手术治疗,患者术前行高频超声及磁共振扩散加权成像检查。术后随访2年,根据预后分为预后良好组(70例)和预后不良组(25例)。比较组间表观弥散系数(ADC)、收缩期峰值血流速度(PSV)、阻力指数(RI)等参数变化。单因素及多因素Logistic回归分析高频超声及磁共振扩散加权成像对乳腺癌预后的预测风险价值,绘制受试者工作特征(ROC)曲线评估其对乳腺癌预后的预测价值。结果 相较于预后良好组,预后不良组ADC降低,PSV、RI升高(P<0.05);多因素Logistic回归分析显示,ADC、PSV、RI为预测乳腺癌预后的影响因素(P<0.05);ROC曲线显示,ADC、PSV、RI联合检测时对乳腺癌预后不良的预测价值较高。结论 高频超声与磁共振扩散加权成像联合检测对乳腺癌预后有一定的预测价值。

多模态超声在乳腺非肿块型病变鉴别诊断中的应用价值探析

目的 探析多模态超声在乳腺非肿块型病变鉴别诊断中的应用价值。方法 选择2022年3月—2023年3月本院收治的94例乳腺非肿块型病变患者为研究对象,对其进行常规超声、超声血流成像、超声弹性成像检查,以病理检查结果作为金标准,分析多模态超声(三者联合)在乳腺非肿块型病变鉴别诊断中的应用价值。结果 94例乳腺非肿块型病变患者经病理检查,其中41例确诊为乳腺癌,53例确诊为良性乳腺病变。常规超声、超声血流成像、超声弹性成像及多模态超声鉴别诊断乳腺非肿块型病变与病理检查的一致性kappa值分别为0.411、0.617、0.742、0.913。多模态超声检查鉴别诊断乳腺非肿块型病变的灵敏度为95.12%(39/41)、特异度为96.23%(51/53)、准确度为95.74%(90/94)、阳性预测值为95.12%(39/41)、阴性预测值为96.23%(51/53),高于单独的常规超声、超声血流成像、超声弹性成像的鉴别诊断结果,差异显著,具有统计学意义(P<0.05)。结论 多模态超声在乳腺非肿块型病变鉴别诊断中的应用价值较高,与病理结果的一致性较高,可提升诊断准确性。

影像组学及影像基因组学预测乳腺癌分子分型的研究进展

影像组学可从医学图像中高通量地提取大量影像特征,影像基因组学则侧重于挖掘影像数据与基因组之间的相关性。基于动态对比增强MRI(DCE-MRI)、乳腺X线摄影、乳腺超声等成像技术,影像组学及影像基因组学能够评估乳腺癌的肿瘤异质性及生物学行为,为乳腺癌诊断、疗效评估及预后预测等方面的研究提供了新途径和新思路,尤其在术前无创性地预测乳腺癌分子分型方面显示出极大的潜能。就影像组学及影像基因组学预测乳腺癌分子亚型方面的研究进展予以综述。

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。