Dataset Information

Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound.

ABSTRACT:

Background

This study prospectively investigates the agreement between radial (r-US) and meander-like (m-US) breast ultrasound with regard to lesion location, lesion size, morphological characteristics and final BI-RADS classification of individual breast lesions.

Methods

Each patient of a consecutive, unselected, mixed collective received a dual ultrasound examination.

Results

The agreement between r-US and m-US for lesion location ranged from good (lesion to mammilla distance ICC 0.64; lesion to skin distance ICC 0.72) to substantial (clock-face localization κ 0.70). For lesion size the agreement was good (diameter ICC 0.72; volume ICC 0.69), for lesion margin and architectural distortion it was substantial (κ 0.68 and 0.70, respectively). Most importantly, there was a substantial agreement (κ 0.76) in the final BI-RADS classification between r-US and m-US.

Conclusions

Our recent comparison of radial and meander-like breast US revealed that the diagnostic accuracy of the two scanning methods was comparable. In this study, we observe a high degree of agreement between m-US and r-US for the lesion description (location, size, morphology) and final BI-RADS classification. These findings corroborate that r-US is a suitable alternative to m-US in daily clinical practice. Trial registration NCT02358837. Registered January 2015, retrospectively registered https://clinicaltrials.gov/ct2/results?cond=&term=NCT02358837&cntry=&state=&city=&dist =.

SUBMITTER: Brasier-Lutz P

PROVIDER: S-EPMC8220682 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Publications

Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound.

Brasier-Lutz Pascale P Jäggi-Wickes Claudia C Schaedelin Sabine S Burian Rosemarie R Schoenenberger Cora-Ann CA Zanetti-Dällenbach Rosanna R

BMC medical imaging 20210622 1

<h4>Background</h4>This study prospectively investigates the agreement between radial (r-US) and meander-like (m-US) breast ultrasound with regard to lesion location, lesion size, morphological characteristics and final BI-RADS classification of individual breast lesions.<h4>Methods</h4>Each patient of a consecutive, unselected, mixed collective received a dual ultrasound examination.<h4>Results</h4>The agreement between r-US and m-US for lesion location ranged from good (lesion to mammilla dist ...[more]

PMID: 34157997

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Project description:ObjectiveQualification tasks in mammography and breast ultrasound were developed for the American College of Radiology Imaging Network (ACRIN) 6666 Investigators. We sought to assess the effects of feedback on breast ultrasound interpretive performance and agreement in BI-RADS feature analysis among a subset of these experienced observers.Materials and methodsAfter a 1-hour didactic session on BI-RADS: Ultrasound, an interpretive skills quiz set of 70 orthogonal sets of breast ultrasound images including 25 (36%) malignancies was presented to 100 experienced breast imaging observers. Thirty-five observers reviewed the quiz set twice: first without and then with immediate feedback of consensus feature analysis, management recommendations, and pathologic truth. Observer performance (sensitivity, specificity, area under the curve [AUC]) was calculated without feedback and with feedback. Kappas were determined for agreement on feature analysis and assessments.ResultsFor 35 observers without feedback, the mean sensitivity was 89% (range, 68-100%); specificity, 62% (range, 42-82%); and AUC, 82% (range, 73-89%). With feedback, the mean sensitivity was 93% (range, 80-100%; mean increase, 4%; range of increase, 0-12%; p < 0.0001), the mean specificity was 61% (range, 45-73%; mean decrease, 1%; range of change, -18% to 11%; p = 0.19), and the mean AUC was 84% (range, 78-90%; mean increase, 2%; range of change, -3% to 9%; p < 0.0001). Three breast imagers in the lowest quartile of initial performance showed the greatest improvement in sensitivity with no change or improvement in AUC. The kappa values for feature analysis did not change, but there was improved agreement about final assessments, with the kappa value increasing from 0.53 (SE, 0.02) without feedback to 0.59 (SE, 0.02) with feedback (p < 0.0001).ConclusionMost experienced breast imagers showed excellent breast ultrasound interpretive skills. Immediate feedback of consensus BI-RADS: Ultrasound features and histopathologic results improved performance in ultrasound interpretation across all experience variables.

Project description:Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P?=?0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

Dataset Information

Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound.

Background

Methods

Results

Conclusions

Publications

Agreement in breast lesion assessment and final BI-RADS classification between radial and meander-like breast ultrasound.

Similar Datasets

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