Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images’ parameters

Sani, Lorenzo; Vispa, Alessandro; Loretoni, Riccardo; Duranti, Michele; Ghavami, Navid; Sánchez-Bayuela, Daniel Álvarez; Caschera, Stefano; Paoli, Martina; Bigotti, Alessandra; Badia, Mario; Scorsipa, Michele; Raspa, Giovanni; Ghavami, Mohammad; Tiberi, Gianluigi

doi:10.1371/journal.pone.0250005

Cited by 52 publications

(45 citation statements)

References 17 publications

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“…When ideal skin suppression is used, as displayed in Figure 3a, no beamformer has a practical sensitivity. If adipose and fibroglandular reference subtraction is performed, the beamformers have improved performance that is within the range of reported sensitivity estimates in the literature (74-100% in [8,9,[32][33][34][35][36][37]). The specificity of the DAS and DMAS beamformers was observed to be worse than a random classifier acting on images of healthy breasts.…”

Section: Resultssupporting

confidence: 78%

“…The other study estimated the specificity to be 20% and estimated the specificity using five phantoms [32]. Neither estimates were promising, and while the sensitivity of BMS has been estimated in several publications and has been reported to be between 74% and 100% [8,9,[32][33][34][35][36][37], insufficient attention has been awarded to the specificity of the modality, which must be considered in tandem with the sensitivity.…”

Section: The Diagnostic Performance Of Das Dmas and Orrmentioning

confidence: 98%

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An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Reimer

Pistorius

2021

Sensors

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Breast microwave sensing (BMS) has been studied as a potential technique for cancer detection due to the observed microwave properties of malignant and healthy breast tissues. This work presents a novel radar-based image reconstruction algorithm for use in BMS that reframes the radar image reconstruction process as an optimization problem. A gradient descent optimizer was used to create an optimization-based radar reconstruction (ORR) algorithm. Two hundred scans of MRI-derived breast phantoms were performed with a preclinical BMS system. These scans were reconstructed using the ORR, delay-and-sum (DAS), and delay-multiply-and-sum (DMAS) beamformers. The ORR was observed to improve both sensitivity and specificity compared to DAS and DMAS. The estimated sensitivity and specificity of the DAS beamformer were 19% and 44%, respectively, while for ORR, they were 27% and 56%, representing a relative increase of 42% and 27%. The DAS reconstructions also exhibited a hot-spot image artifact, where a localized region of high intensity that did not correspond to any physical phantom feature would be present in an image. This artifact appeared like a tumour response within the image and contributed to the lower specificity of the DAS beamformer. This artifact was not observed in the ORR reconstructions. This work demonstrates the potential of an optimization-based conceptualization of the radar image reconstruction problem in BMS. The ORR algorithm implemented in this work showed improved diagnostic performance and fewer image artifacts compared to the widely employed DAS algorithm.

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Section: Resultssupporting

confidence: 78%

Section: The Diagnostic Performance Of Das Dmas and Orrmentioning

confidence: 98%

An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Reimer

Pistorius

2021

Sensors

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“…The sensitivity in malignant lesions was 59%. Their device was also stationary and the sensitivity to malignancy was not 100% [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Feasibility of Portable Microwave Imaging Device for Breast Cancer Detection

et al. 2021

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Purpose: Microwave radar-based breast imaging technology utilizes the principle of radar, in which radio waves reflect at the interface between target and normal tissues, which have different permittivities. This study aims to investigate the feasibility and safety of a portable microwave breast imaging device in clinical practice. Materials and methods: We retrospectively collected the imaging data of ten breast cancers in nine women (median age: 66.0 years; range: 37–78 years) who had undergone microwave imaging examination before surgery. All were Japanese and the tumor sizes were from 4 to 10 cm. Using a five-point scale (1 = very poor; 2 = poor; 3 = fair; 4 = good; and 5 = excellent), a radiologist specialized in breast imaging evaluated the ability of microwave imaging to detect breast cancer and delineate its location and size in comparison with conventional mammography and the pathological findings. Results: Microwave imaging detected 10/10 pathologically proven breast cancers, including non-invasive ductal carcinoma in situ (DCIS) and micro-invasive carcinoma, whereas mammography failed to detect 2/10 breast cancers due to dense breast tissue. In the five-point evaluation, median score of location and size were 4.5 and 4.0, respectively. Conclusion: The results of the evaluation suggest that the microwave imaging device is a safe examination that can be used repeatedly and has the potential to be useful in detecting breast cancer.

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“…The most significant problem is that microwave imaging produces an image with limited spatial resolution [65][66][67]. Microwave tomography and radar-based imaging are the two main types of microwave imaging [68,69].…”

Section: Microwave Imaging-based Technologymentioning

confidence: 99%

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

et al. 2021

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Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges.

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Breast lesion detection through MammoWave device: Empirical detection capability assessment of microwave images’ parameters

Cited by 52 publications

References 17 publications

An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Feasibility of Portable Microwave Imaging Device for Breast Cancer Detection

Existing and Emerging Breast Cancer Detection Technologies and Its Challenges: A Review

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