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.

A Parzen Window-based Approach to Detection of Infrared Dim Target Under Sea-sky Background

People attach great importance to high detection probability and low false alarm probability for infrared dim target detection. Consequently, a novel approach is proposed based on inverted local information entropy map and the improved region growing technique. The idea originates from the intrinsic property of natural image, the visual mechanism of flying insects and the information entropy theory. Besides qualitative analyses, other methods including the norms of local signal-to-background ratio, local signal-to-noise ratio, region non-uniformity, single-frame detection probability and single-frame false alarm probability are adopted to quantitatively evaluate the proposed approach. Both qualitative and quantitative comparisons confirm the validity and efficiency of the proposed approach.