Using echography to biopsy breast lesions that are invisible with echography Is it even possible?

One out of seven women will get breast cancer which is among the most frequent causes of death. Biopsy, a technique to acquire a piece of the suspicious lesion with a needle for histologic analysis, is the “Gold standard” for accurate cancer diagnosis. To navigate the needle to the lesion, radiologists prefer to use echography because of its real-time feedback on needle position and its portability. However, sometimes, the lesions are not detectable with echography requiring a more complicated, time-consuming, and expensive MRI-biopsy procedure.

Anton Nikolaev and Chris de Korte from the Medical Ultrasound Imaging Center (MUSIC) developed a unique Automated Cone-based Ultrasound Scanning and Biopsy System (ACBUS-BS) which can biopsy echographically hidden lesions using echography for biopsy needle navigation. During the biopsy procedure with ACBUS-BS, the patient lays on her belly with her breast placed in a special conical container. The ACBUS-BS scans the breast with 3D echography using a rotating container around the breast. The key feature of the developed system is that the 3D echography images can be fused with MRI data from the same breast, to determine the location of lesion invisible for echography. So the lesion can be accurately localized in ACBUS-BS and the biopsy procedure can be guided using real-time echography. The dedicated algorithm can even follow the lesion during breast displacement and needle insertion.

During the feasibility study, the ACBUS-BS biopsied 8 lesions embedded in an artificial breast phantom. 3 out of 8 lesions were not visible in echography. The technical evaluation demonstrated, the ACBUS-BS can biopsy lesion less than 1 cm.

The ACBUS demonstrated a good result in phantom study. A clinical study is required to demonstrate the efficacy of ACBUS-BS. The development can make breast biopsy cheaper, more accessible, and user-independent.

Publication

Ultrasound-guided breast biopsy using an adapted automated cone-based ultrasound scanner: a Feasibility study Anton V Nikolaev, Leon de Jong, Patrik Zamecnik, Vincent Groenhuis, Françoise J Siepel, Stefano Stramigioli, Hendrik H G Hansen, Chris L de Korte