This is a multicentric, single arm, prospective, stratified by breast density clinical investigation to confirm the ability of the microwave mammogram 'MammoWave' to detect breast lesions. MammoWave is a innovative medical device, class IIa marked CE, which uses microwaves instead of ionazing radiation (x-ray) for breast lesions. Specifically MammoWave employs a novel technique wich generates images by processing very low power (\<1mW) microwave. The MammoWave exam takes few minutes for breast and is performed with the patient lying in a confortable facing down position. MammoWave is safe to be used at any age, in any condition (pregnancy, specific illness and for unlimited number of times.
The maximum number of participants to the clinical investigation will be 600 (for all the sites). The study will be composed of two phases: a preliminary phase to 'optimize the imaging algorithm for each apparatus installed at each centre', where 15 volunteers (in each centre) having breast with no lesions (NL) will be examined by MammoWave. In the second phase, the remaining people will be enrolled (BL will be 50% of total) and examined by MammoWave by the clinical investigator, and results will be compared with the effective diagnosis already obtained by standard clinical methods. BL includes malignant lesions (BC) and benign lesions, and may be palpable or non-palpable lesions. BL includes also isolated clustered microcalcifications. The primary gol of the clinical study is to assess MammoWave's ability in BL detection. The study will involve investigational sites in Italy, Germany and Spain.
Study Type
INTERVENTIONAL
Allocation
NA
Purpose
SCREENING
Masking
NONE
Enrollment
600
Patients should perform MammoWave exam. A short visit should be performed and during this visit a qualitative differentiation would be performed between patients with high density breast, and patients with low density breast. After the patients are ready for MammoWave exam. The exam will composed of two phases: the acquisition and data processing. During the acquisition that sholud takes about 10 minutes the patients would be lying in a prone position, on a bed which is part of the MammoWave. The upper part of MammoWave has a container cup shape, which contains the breast, which also has the function of separate it from the internal parts of the device. After patient is on the bed and MammoWave would start to perform the acquisition. Once the acquisition is completed, the data will be processed through an imaging algorithm, which is integrated in the device. The final output will be composed by one or more images, plus one or more parameters describing the images.
IRCCS Policlinico San Martino
Genova, Genova, Italy
RECRUITINGOspedale San Giovanni Battista - USL Umbria 2
Foligno, Perugia, Italy
NOT_YET_RECRUITINGHospital Universitario de Toledo
Toledo, Spain, Spain
RECRUITINGMammoWave sensitivity (number of 'true positive' results)
MammoWave sensitivity (number of 'true positive' results) compared to Reference Standard
Time frame: During the procedure
MammoWave specificity and sensitivity
MammoWave specificity and sensitivity (against Reference Standard)
Time frame: During the baseline
Sensitivity for each breast density group
Sensitivity of MammoWave according to different types of breast density groups
Time frame: During the baseline
Sensitivity for patients which had recent mammography
Sensitivity for patients which had performed recently mammography exam
Time frame: During the baseline
Patient satisfaction questionnaire
Patient satisfaction questionnaire output according to their experience performing
Time frame: During the baseline
MammoWave sensitivity in BC
Sensitivity of MammoWave in Breast Cancer detection (against reference standard)
Time frame: During the baseline)
MammoWave specificity and sensitivity using RadioSpin simulator
Specificity and sensitivity of MammoWave (against Reference Standard) when retrospectively using MammoWave data in one RadioSpin technology simulator / Artificial Intelligence (AI) algorithms. NOTE: RadioSpin (Deep oscillatory neural networks computing and learning through the dynamics of RF neurons interconnected by RF spintronic synapses) is one of the projects funded by EU within the call FUTURE EMERGING TECHNOLOGIES (FET) H2020-FETPROACT. The RadioSpin project aims to build a hardware neural network, as a "Hardware Artificial Intelligence" will be implemented. During the project, these hardware neural networks will be optimized and tested on MammoWave data, being UBT partner of RadioSpin consortium (Grant agreement ID: 101017098)
Time frame: During the Baseline
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