Abstract

Introduction Most of the currently available automated breast ultrasound systems require patients to be in the supine position. Previous data, however, show a high recall rate with this method due to artifacts. The novel automated breast ultrasound scanner SOFIA scans the breast with the patient in a prone position, resulting in even compression of breast tissue. We present our initial results with this examination method. Material and Methods 63 patients were analyzed using a handheld B-mode ultrasound. In cases of BI-RADS 1, 2 or 5, a SOFIA scan was performed. Sensitivity, specificity and accuracy were calculated. Interobserver agreement was evaluated using Cohenʼs kappa. The duration of the scan was measured for both methods. Results No BI-RADS 5 lesion was missed with SOFIA. The SOFIA had an additional recall rate of 16.67% compared to B-mode ultrasound. The sensitivity, specificity and accuracy of SOFIA was 100, 83.33 and 88.89%, respectively. Cohenʼs kappa showed substantial agreement (κ = 0.769) between examiner 1 (B-mode) and examiner 2 (SOFIA). The mean scan duration for the B-mode system and the SOFIA system was 24.21 minutes and 12.94 minutes, respectively. In four cases, D-cup breasts were not scanned in their entirety. Conclusion No cancer was missed when SOFIA was used in this preselected study population. The scanning time was approximately half of that required for B-mode ultrasound. The additional unnecessary recall rate was 16.67%. Larger D cup-size breasts were difficult to position and resulted in an incomplete image in four cases.

Highlights

  • Most of the currently available automated breast ultrasound systems require patients to be in the supine position

  • Automated breast ultrasound (ABUS) systems seem to be promising tools for overcoming the time-consuming process of handheld ultrasound (HHUS) whilst possessing the same benefits that B-mode imaging has in the diagnosis of breast lesions in dense breasts

  • ▶ Table 2 shows the distribution of the BI-RADS rating of a lesion using HHUS and SOFIA

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Summary

Introduction

Most of the currently available automated breast ultrasound systems require patients to be in the supine position. Screening mammography can detect cancer at an early stage and help to initiate immediate treatment; the sensitivity of digital mammography for detecting breast cancer is strongly dependent on breast density and declines to 48 % for patients with the densest breasts [1]. This group of patients requires individual clarification since it is known that a dense breast is an independent risk factor for breast cancer [2 – 4]. The scanned raw data is subsequently reconstructed in a workstation to a 3D dataset, which allows analysis of the breast in all planes, in a manner similar to tomography

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