scholarly journals Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer

Diagnostics ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. 631
Author(s):  
Afaf F. Moustafa ◽  
Theodore W. Cary ◽  
Laith R. Sultan ◽  
Susan M. Schultz ◽  
Emily F. Conant ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Breast Imaging ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Ensemble Classifier ◽  
Diagnostic Model ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Roc Area

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.

Qualitative Diagnosis of Solid Breast Mass by Blood Flow in Solid Breast Mass Based on Color Doppler Ultrasound

2021 ◽  
Vol 11 (6) ◽  
pp. 1608-1615
Author(s):  
Ding Zuopeng ◽  
Liu Weiyong ◽  
Hu Chunmei ◽  
Wang Tao ◽  
Wang Mingming
Keyword(s):  
Breast Cancer ◽  
Blood Flow ◽  
Differential Diagnosis ◽  
Color Doppler ◽  
Flow Characteristics ◽  
Flow Distribution ◽  
Color Doppler Ultrasound ◽  
Blood Flow Distribution ◽  
Breast Masses ◽  
Malignant Breast

The incidence of breast cancer ranks first among female malignant tumor. With the increase of the sensitivity of color Doppler ultrasound blood flow, the blood flow distribution in and around the tumor can be clearly displayed, and the analysis of hemodynamic parameters is provided, which provides convenience for the study of tumor blood flow characteristics. Studies have shown that tumor cells can secrete a substance called angiogenesis factor, which makes the tumor site form a rich vascular network to promote tumor growth and metastasis. The tumor has many new blood vessels, abnormal structure, thin wall, lack of muscle layer, and is prone to form arteriovenous rash. These characteristics provide a pathological basis for color Doppler flow imaging (CDFI) for the diagnosis of breast cancer. This article discusses the role of two-dimensional sonographic features in the differential diagnosis of benign and malignant breast masses, CDFI was used to study the blood flow distribution and hemodynamic characteristics in benign and malignant breast masses; explore the value of blood flow characteristics and blood flow parameters in the differential diagnosis of breast masses. The experimental results show that the detection rate of blood flow signals and the classification of blood flow signals in the malignant group are higher than those in the benign group, mainly level II and III blood flow, and the irregular branched blood flow is more common, especially when the tumor appears penetrating blood flow supports the diagnosis of malignancy. PSV, RI and PI have a certain differential meaning in the diagnosis of benign and malignant breast masses. PSV, RI and PI of malignant masses are higher than benign masses. For tumors without obvious necrosis, the larger the tumor diameter, the richer the blood flow and the higher the blood flow grade is. The malignant tumors have more blood flow than the benign ones.

scholarly journals Combination of shear wave elastography and BI-RADS in identification of solid breast masses

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xue Zheng ◽  
Fei Li ◽  
Zhi-Dong Xuan ◽  
Yu Wang ◽  
Lei Zhang
Keyword(s):  
Shear Wave ◽  
Breast Imaging ◽  
Color Doppler ◽  
Shear Wave Elastography ◽  
Definitive Diagnosis ◽  
Pathological Examination ◽  
Diagnostic Specificity ◽  
Breast Masses ◽  
Malignant Breast ◽  
Grade Ii

Abstract Background To explore the value of quantitative shear wave elastography (SWE) plus the Breast Imaging Reporting and Data System (BI-RADS) in the identification of solid breast masses. Methods A total of 108 patients with 120 solid breast masses admitted to our hospital from January 2019 to January 2020 were enrolled in this study. The pathological examination served as the gold standard for definitive diagnosis. Both SWE and BI-RADS grading were performed. Results Out of the 120 solid breast masses in 108 patients, 75 benign and 45 malignant masses were pathologically confirmed. The size, shape, margin, internal echo, microcalcification, lateral acoustic shadow, and posterior acoustic enhancement of benign and malignant masses were significantly different (all P < 0.05). The E mean, E max, SD, and E ratio of benign and malignant masses were significantly different (all P < 0.05). The E min was similar between benign and malignant masses (P > 0.05). The percentage of Adler grade II-III of the benign masses was lower than that of the malignant masses (P < 0.05). BI-RADS plus SWE yielded higher diagnostic specificity and positive predictive value than either BI-RADS or SWE; BI-RADS plus SWE yielded the highest diagnostic accuracy among the three methods (all P < 0.05). Conclusion SWE plus routine ultrasonography BI-RADS has a higher value in differentiating benign from malignant breast masses than color doppler or SWE alone, which should be further promoted in clinical practice.

scholarly journals Prenatal Sonographic Image of Sirenomelia with Anencephaly and Craniorachischisis Totalis

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Takako Sugiura ◽  
Yuka Sato ◽  
Naoyuki Nakanami ◽  
Kiyomi Tsukimori
Keyword(s):  
Ultrasound Examination ◽  
Color Doppler ◽  
Three Dimensional ◽  
Final Diagnosis ◽  
Color Doppler Ultrasound ◽  
Lower Limbs ◽  
Ultrasound Images ◽  
Two Dimensional ◽  
Prenatal Counselling ◽  
Medical Termination

Sirenomelia is a rare congenital malformation characterized by varying degrees of fusion of the lower extremities. It is commonly associated with severe urogenital and gastrointestinal malformations; however, the association of sirenomelia with anencephaly and rachischisis totalis is extremely rare. To our knowledge, the prenatal sonographic images of this association have not been previously published. Here, we present prenatal sonographic images of this association, detected during the 17th week of gestation through combined two-dimensional, four-dimensional, and color Doppler ultrasound. Two-dimensional ultrasound images showed anencephaly, spina bifida, and possible fusion of the lower limbs. Three-dimensional HDlive rendering images confirmed the final diagnosis of sirenomelia with anencephaly and rachischisis totalis. The patient opted to undergo medical termination of pregnancy and delivered a fetus with fused lower limbs, anencephaly, and rachischisis totalis confirming the in utero imaging findings. Awareness of these rare associations will help avoid misdiagnoses and facilitate prenatal counselling. This case highlights the importance of a thorough ultrasound examination.

scholarly journals Posterior breast cancer: Mammographic and ultrasonographic features

2013 ◽  
Vol 70 (11) ◽  
pp. 1034-1038
Author(s):  
Ana Jankovic ◽  
Mirjan Nadrljanski ◽  
Vesna Plesinac-Karapandzic ◽  
Nebojsa Ivanovic ◽  
Zoran Radojicic ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Imaging ◽  
Color Doppler ◽  
Lobular Carcinoma ◽  
Architectural Distortion ◽  
Breast Cancers ◽  
Significant Difference ◽  
Diagnostic Mammography ◽  
Anatomical Localization ◽  
Mass Lesions

Background/Aim. Posterior breast cancers are located in the prepectoral region of the breast. Owing to this distinctive anatomical localization, physical examination and mammographic or ultrasonographic evaluation can be difficult. The purpose of the study was to assess possibilities of diagnostic mammography and breast ultrasonography in detection and differentiation of posterior breast cancers. Methods. The study included 40 women with palpable, histopathological confirmed posterior breast cancer. Mammographic and ultrasonographic features were defined according to Breast Imaging Reporting and Data System (BI-RADS) lexicon. Results. Based on standard two-view mammography 87.5%, of the cases were classified as BI-RADS 4 and 5 categories, while after additional mammographic views all the cases were defined as BIRADS 4 and 5 categories. Among 96 mammographic descriptors, the most frequent were: spiculated mass (24.0%), architectural distortion (16.7%), clustered microcalcifications (12.6%) and focal asymmetric density (12.6%). The differentiation of the spiculated mass was significantly associated with the possibility to visualize the lesion at two-view mammography (p = 0.009), without the association with lesion diameter (p = 0.083) or histopathological type (p = 0.055). Mammographic signs of invasive lobular carcinoma were significantly different from other histopathological types (architectural distortion, p = 0.003; focal asymmetric density, p = 0.019; association of four or five subtle signs of malignancy, p = 0.006). All cancers were detectable by ultrasonography. Mass lesions were found in 82.0% of the cases. Among 153 ultrasonographic descriptors, the most frequent were: irregular mass (15.7%), lobulated mass (7.2%), abnormal color Doppler signals (20.3%), posterior acoustic attenuation (18.3%). Ultrasonographic BI-RADS 4 and 5 categories were defined in 72.5% of the cases, without a significant difference among various histopathological types (p = 0.109). Conclusion. Standard two-view mammography followed by additional mammographic projections is an effective way to demonstrate the spiculated mass and to classify the prepectoral lesion as category BI-RADS 4 or 5. Additional ultrasonography can overcome the mimicry of invasive lobular breast carcinoma at mammography.

scholarly journals Color doppler ultrasound images and their types in liver tumors.

Kanzo ◽  
1989 ◽  
Vol 30 (11) ◽  
pp. 1637-1638 ◽  
Author(s):  
Yousuke ARITA ◽  
Kazuaki YASUHARA ◽  
Jyunji FURUSE ◽  
Shoichi MATSUTANI ◽  
Masaaki EBARA ◽  
...  
Keyword(s):  
Doppler Ultrasound ◽  
Liver Tumors ◽  
Color Doppler ◽  
Color Doppler Ultrasound ◽  
Ultrasound Images

scholarly journals Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Bian ◽  
Xiyu Zhang ◽  
Ruihong Liu ◽  
Huijie Li ◽  
Qingqing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Ultrasound Image ◽  
Color Doppler Ultrasound ◽  
Control Group ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Before And After ◽  
Experimental Group

The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant ( P < 0.05 ); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance ( P < 0.05 ). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.

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