Power Doppler sonography: Anything to add to BI-RADS US in solid breast masses?

Gökalp, Gökhan; Topal, Uğur; Kızılkaya, Eşref

doi:10.1016/j.ejrad.2007.12.007

Cited by 48 publications

(58 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hence, the inside-tumor vessel could play an important role to supply nutrition and oxygen to affect the histological changes of tumor tissues [1]. Thus using functional vessel distribution of different region could reflect different tissue characterization and evaluate the malignancy likelihood [9,16,17].…”

Section: Discussionmentioning

confidence: 99%

“…Hence, the vessel features related to the tumors were extracted to analyze the tumor, such as blood vessel inside the tumor region, and blood vessel outside the tumor region, and in our study they are useful criteria for differentiating the tumors as malignant or benign. In previous researches [9,16,17], the vessels were separated into several partitions to evaluate vascularity, but they only applied the simplest characteristics to diagnose tumor, the accuracy, sensitivity and specificity in the study [17] were only 77.6, 71.8, and 81.8 %, respectively. In our study, the spatial domain was separately into several regions, inside tumor, outside tumor, and the periphery of inside tumor, and then these regions were used to compute vascularity based on vascularity index [8].…”

Section: Discussionmentioning

confidence: 99%

“…The sensitivity, specificity, and accuracy could achieve 94, 69, and 81 %, respectively. In the Gokalp's study [17], whether the vessel is in the central part of the tumor, periphery of the tumor region, or penetrating into the tumor or not was used to distinguish the different pathological type of the tumor and the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 71.8, 81.8, 73.7, and 80.4 %, respectively. These studies [9,16,17] confirmed that the related information of vascular features and tumor region could be combined well for classification of breast tumors in 3-D US diagnosis system.…”

Section: Introductionmentioning

confidence: 99%

“…However, because in many centers contrast imaging is not available, the use of Doppler images in this study is good and a clear application for such a situation. Because of the heterogeneously growing vessels related to the tumor location, studies have applied several volumes around tumor, divided vessels into different partitions, and then analyzed the vessels individually [9,16,17]. The four specific regions around the tumor [16] were used to evaluate vascularity within different volumes, including inside, upper half, 3-mm shell surrounding, and upper half region of 3-mm shell region of radiologist-defined ellipsoid.…”

Section: Introductionmentioning

confidence: 99%

“…After the tumor volumes of interest (VOI) were determined, the specific regions were used to quantify the vascularity inside, outside, and adjacent to the tumor. These specific tumorrelated regions have been used [9,16,17] to evaluate the vascular degree. Computer-aided diagnosis (CAD) [18][19][20][21][22][23] then can help to analyze these quantitative vascular features and can assist physicians to differentiate the tumor more effectively.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images

et al. 2013

View full text Add to dashboard Cite

This study aims to evaluate whether the distribution of vessels inside and adjacent to tumor region at threedimensional (3-D) power Doppler ultrasonography (US) can be used for the differentiation of benign and malignant breast tumors. 3-D power Doppler US images of 113 solid breast masses (60 benign and 53 malignant) were used in this study. Blood vessels within and adjacent to tumor were estimated individually in 3-D power Doppler US images for differential diagnosis. Six features including volume of vessels, vascularity index, volume of tumor, vascularity index in tumor, vascularity index in normal tissue, and vascularity index in surrounding region of tumor within 2 cm were evaluated. Neural network was then used to classify tumors by using these vascular features. The receiver operating characteristic (ROC) curve analysis and Student's t test were used to estimate the performance. All the six proposed vascular features are statistically significant (p<0.001) for classifying the breast tumors as benign or malignant. The A Z (area under ROC curve) values for the classification result were 0.9138. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the diagnosis performance based on all six proposed features were 82.30 (93/113), 86.79 (46/53), 78.33 (47/60), 77.97 (46/59), and 87.04 % (47/54), respectively. The p value of A Z values between the proposed method and conventional vascularity index method using z test was 0.04.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images

et al. 2013

View full text Add to dashboard Cite

show abstract

Superb Microvascular Imaging for the Differentiation of Benign and Malignant Breast Lesions: A System Review and Meta‐Analysis

Chen

Xiang

Che

et al. 2022

J of Ultrasound Medicine

View full text Add to dashboard Cite

Objective To evaluate the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions. Methods A systematic search of PubMed, EMBASE, Cochrane, OVID, SCI, and SCOPUS was performed to find relevant studies which applied SMI to differentiate benign and malignant breast lesions. All the studies were published before October 10, 2022. Only studies published in English were collected. Quality Assessment of Diagnostic Accuracy Studies‐2 (QUADAS‐2) tool was applied to assess the quality of the included studies. Summary receiver operating characteristic (SROC) modeling was also performed to the diagnostic performance of SMI in the diagnosis of benign and malignant breast lesions. Subgroup analyses and meta‐regression were performed to find out the heterogeneity. Results Twenty studies which include a total of 2873 lesions (1748 benign and 1125 malignant) in 2740 patients were evaluated in this meta‐analysis. The summary sensitivity and specificity were 0.82 (95% confidence interval [CI]: 0.76–0.86), 0.70 (95% CI: 0.64–0.76) for SMI vascular degree, 0.77 (95% CI: 0.67–0.84), 0.79 (95% CI: 0.75–0.83) for SMI vascular distribution, 0.78 (95% CI: 0.70–0.84), 0.75 (95% CI: 0.69–0.80) for SMI vascular morphology, 0.81 (95% CI: 0.72–0.87), 0.80 (95% CI: 0.75–0.85) SMI penetration vessel. For SMI overall vascular features, the summary sensitivity and summary specificity were 0.74 (95% CI: 0.61–0.84) and 0.80 (95% CI: 0.76–0.84). The result of subgroup analysis and meta‐analysis showed malignant rate and country might be the cause of heterogeneity of diagnostic accuracy of vascular grade and morphology. Conclusion SMI vascular features have high sensitivity and specificity in the differentiation of benign and malignant lesions. Future international multicenter studies in various regions with large sample size are required to confirm these findings.

show abstract