A comparative study on superb microvascular imaging and conventional ultrasonography in differentiating BI‑RADS 4 breast lesions

Zhu, Yicheng; Zu, Dao-Ming; Zhang, Yuan; Shan, Jun; Shi, Xiu-Rong; Deng, Shuhao; Jiang, Quan

doi:10.3892/ol.2019.10603

Cited by 16 publications

(34 citation statements)

References 25 publications

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“…We reviewed the titles and abstracts of all articles and excluded 21 articles; full texts and data integrity were also reviewed and 11 were further excluded. Finally, 15 studies that met all inclusion criteria were included in this meta-analysis [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Figure 1 showed the selection process of eligible articles.…”

Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Diagnostic accuracy of ultrasound superb microvascular imaging for breast tumor: a meta-analysis

Zhong

2020

Med Ultrason

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Aim: This meta-analysis aimed to identify the accuracy of ultrasound superb microvascular imaging (SMI) for the diagnosis of a breast tumor.Material and method: We searched PubMed, Web of Science, Google Scholar, Cochrane Library, EBSCO, and CBM databases from January 1st, 2013 until February 1st, 2020 without language restrictions. Meta-analysis was conducted using STATA version 14.0 and Meta-Disc version 1.4 software. We calculated the summary statistics for sensitivity (Sen), specificity (Spe), positive and negative likelihood ratio (LR+/LR−), diagnostic odds ratio (DOR) and receiver operating characteristic (SROC) curve.Results: Fifteen studies that met all inclusion criteria were included in this meta-analysis. A total of 955 breast neoplasm patients and 1116 patients with benign breast tumors were assessed. All breast lesions were histologically confirmed after SMI. The pooled Sen was 0.81 (95%CI=0.78-0.83); the pooled Spe was 0.71 (95%CI=0.68-0.73) The pooled LR+ was 3.24 (95%CI=2.27-4.64); the pooled negative LR−was 0.25 (95%CI=0.18-0.34) The pooled DOR of SMI in the diagnosis of breast tumor was 46.97 (95 % CI=16.72∼131.97). The area under the SROC curve was 0.87 (95%CI=0.84- 0.90). We found no evidence for publication bias (t=−0.84, p=0.42).Conclusion: Our meta-analysis indicates that SMI may have a high diagnostic accuracy in differential diagnosis between benign and malignant breast tumors. Thus, SMI may be a good tool for the diagnosis of breast tumors.

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Section: Characteristics Of Included Studiesmentioning

confidence: 99%

Diagnostic accuracy of ultrasound superb microvascular imaging for breast tumor: a meta-analysis

Zhong

2020

Med Ultrason

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“…Only 1 prior study has reported a case of calcifications in lung cancer metastatic to the breast (13). Color blood flow signal was detected in 2 patients, which is different from the abundant vascularity of primary breast carcinoma (21). Sippo et al (20) and Moreno-Astudillo et al (22) reported that the US manifestation of other cancer metastases to the breast were round or oval hypoechoic lesions, which could be diagnosed as benign nodules.…”

Section: Discussionmentioning

confidence: 98%

Breast metastases from primary lung cancer: a retrospective case series on clinical, ultrasonographic, and immunohistochemical features

Wang

Jiang

et al. 2021

Transl Lung Cancer Res

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Background: Lung cancer metastases to the breast are less common and consequently have received much less attention in clinical practice. The purpose of this study was to provide a better understanding of clinical, ultrasonographic, and immunohistochemical features of breast metastases from primary lung cancer.Methods: This retrospective case series included patients with breast metastases from primary lung cancer between January 2012 and December 2020. Clinical features, ultrasonographic characteristics, and immunohistochemical findings were evaluated in this analysis.Results: In all, 7 cases (mean ± standard deviation age: 57.4±8.3 years; range, 49-70 years) were evaluated. The maximum size of breast lesions in 6 cases ranged from 1.2 to 4.5 cm, while 1 case showed a diffused pattern. Ultrasound features of breast metastases from lung cancer were irregular (5/7, 71.4%), indistinct (6/7, 85.7%), hypoechoic (7/7, 100.0%), and parallel (6/7, 85.7%) masses without calcification.Immunohistochemical staining test was positive for thyroid transcription factor 1 (TTF-1) in all patients (7/7, 100.0%), 3 cases (3/5, 60.0%) were negative for p63, 5 cases (5/5, 100.0%) were positive for cytokeratin 7 (CK7), 4 cases (4/5, 80.0%) were positive for napsin A. Conclusions:The ultrasonographic features of lung metastases to the breast are clinically important to understand. A known history of the primary lung cancer is of great importance when evaluating patients with a breast nodule. The presence of an ipsilateral lung cancer, breast nodule and axillary lymphadenopathy should be considered with pathological and immunohistochemical data to differentiate breast metastases from a primary breast malignancy in this setting.

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“…Here, recently published articles can provide this information and be used for reference. A published article reported that the AUC of malignancy detection by B-mode US was only 0.698 in differentiating BI‑RADS 4 breast lesions 35 . For the human readout performance of malignant lesions classification in US image, the AUC from several previous studies and using similar estimated methods was between 0.6 and 0.91, and PPV (from 0.46 to 0.81) and sensitivity (47–85%) varied widely.…”

Section: Discussionmentioning

confidence: 99%

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches

Shia

Lin

Chen

2021

Sci Rep

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Traditional computer-aided diagnosis (CAD) processes include feature extraction, selection, and classification. Effective feature extraction in CAD is important in improving the classification’s performance. We introduce a machine-learning method and have designed an analysis procedure of benign and malignant breast tumour classification in ultrasound (US) images without a need for a priori tumour region-selection processing, thereby decreasing clinical diagnosis efforts while maintaining high classification performance. Our dataset constituted 677 US images (benign: 312, malignant: 365). Regarding two-dimensional US images, the oriented gradient descriptors’ histogram pyramid was extracted and utilised to obtain feature vectors. The correlation-based feature selection method was used to evaluate and select significant feature sets for further classification. Sequential minimal optimisation—combining local weight learning—was utilised for classification and performance enhancement. The image dataset’s classification performance showed an 81.64% sensitivity and 87.76% specificity for malignant images (area under the curve = 0.847). The positive and negative predictive values were 84.1 and 85.8%, respectively. Here, a new workflow, utilising machine learning to recognise malignant US images was proposed. Comparison of physician diagnoses and the automatic classifications made using machine learning yielded similar outcomes. This indicates the potential applicability of machine learning in clinical diagnoses.

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A comparative study on superb microvascular imaging and conventional ultrasonography in differentiating BI‑RADS 4 breast lesions

Cited by 16 publications

References 25 publications

Diagnostic accuracy of ultrasound superb microvascular imaging for breast tumor: a meta-analysis

Diagnostic accuracy of ultrasound superb microvascular imaging for breast tumor: a meta-analysis

Breast metastases from primary lung cancer: a retrospective case series on clinical, ultrasonographic, and immunohistochemical features

Classification of malignant tumours in breast ultrasound using unsupervised machine learning approaches

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