Automated Breast Lesion Detection and Characterization with the Wavelia Microwave Breast Imaging System: Methodological Proof-of-Concept on First-in-Human Patient Data

Fasoula, Angie; Duchesne, Luc; Cano, Julio Daniel Gil; Moloney, Brian; Elwahab, Sami Abd; Kerin, Michael J.

doi:10.3390/app11219998

Cited by 13 publications

(16 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The limited number of signals and images in this pilot study did not allow for exploration of machine learning techniques that have been recently described in the microwave imaging literature. A more comprehensive set of scans would permit exploration of machine learning techniques to support identification of tumors from signals, 35–37 as well as image reconstruction, segmentation and analysis 38,39 …”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Microwave imaging for monitoring breast cancer treatment: A pilot study

Smith,

Bourqui,

Wang

et al. 2023

Medical Physics

View full text Add to dashboard Cite

BackgroundMicrowave imaging has been proposed for medical applications, creating maps related to water content of tissues. Breast imaging has emerged as a key application because the signals can be coupled directly into the breast and experience limited attenuation in fatty tissues. While the literature contains reports of tumor detection with microwave approaches, there is limited exploration of treatment monitoring.PurposeThis study aims to detect treatment‐related changes in breast tissue with a low‐resolution microwave scanner.MethodsMicrowave scans of 15 patients undergoing treatment for early‐stage breast cancer are collected at up to 4 time points: after surgery (baseline), 6 weeks after accelerated partial breast radiation, as well as 1 and 2 years post‐treatment. Both the treated and untreated breast are scanned at each time point. The microwave scanner consists of planar transmit and receive arrays and uses signals from 0.1 to 10 GHz. The average microwave frequency properties (permittivity) are calculated for each scan to enable quantitative comparison. Baseline and 6‐week results are analyzed with a two‐way ANOVA with blocking.ResultsConsistent properties are observed for the untreated breast over time, similar to a previous study. Comparison of the scans of the treated and untreated breast suggests increased properties related to treatment, particularly at baseline and 6‐weeks following radiotherapy. Analysis of the average properties of the scans with ANOVA indicates statistically significant differences () in the treated and untreated breast at these time points.ConclusionsMicrowave imaging has the potential to track treatment‐related changes in breast tissues.

show abstract

Section: Discussionmentioning

confidence: 99%

“…A more comprehensive set of scans would permit exploration of machine learning techniques to support identification of tumors from signals, [35][36][37] as well as image reconstruction, segmentation and analysis. 38,39…”

Section: Discussionmentioning

confidence: 99%

Microwave imaging for monitoring breast cancer treatment: A pilot study

Smith,

Bourqui,

Wang

et al. 2023

Medical Physics

View full text Add to dashboard Cite

show abstract

“…Only the patients considered for the biopsy were included in the study. The SAFE scan is performed prior to the biopsy due to the post-biopsy impact mentioned by Fasoula et al [ 37 ]. Lesion pathology was provided after the biopsy was performed.…”

Section: Methodsmentioning

confidence: 99%

“…Besides the mentioned prototypes, the Microwave Vision (MVG) Medical Imaging department, France, developed the MBI device named Wavelia and tested it in a small-scale study of 24 patients. They reported that they successfully separated benign from malignant lesions in 88.5% of the cases, whereas their malignancy risk calculation was deemed useful in 70.6% of the cases [ 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

Gradient-Boosting Algorithm for Microwave Breast Lesion Classification—SAFE Clinical Investigation

et al. 2022

View full text Add to dashboard Cite

(1) Background: Microwave breast imaging (MBI) is a promising breast-imaging technology that uses harmless electromagnetic waves to radiate the breast and assess its internal structure. It utilizes the difference in dielectric properties of healthy and cancerous tissue, as well as the dielectric difference between different cancerous tissue types to identify anomalies inside the breast and make further clinical predictions. In this study, we evaluate the capability of our upgraded MBI device to provide breast tissue pathology. (2) Methods: Only patients who were due to undergo biopsy were included in the study. A machine learning (ML) approach, namely Gradient Boosting, was used to understand information from the frequency spectrum, collected via SAFE, and provide breast tissue pathology. (3) Results: A total of 54 patients were involved in the study: 29 of them had benign and 25 had malignant findings. SAFE acquired 20 true-positive, 24 true-negative, 4 false-positive and 4 false-negative findings, achieving the sensitivity, specificity and accuracy of 80%, 83% and 81%, respectively. (4) Conclusions: The use of harmless tissue radiation indicates that SAFE can be used to provide the breast pathology of women of any age without safety restrictions. Results indicate that SAFE is capable of providing breast pathology at a high rate, encouraging further clinical investigations.

show abstract

“…More recently, Deep Learning approaches [4,5,7,11,14,19] were shown to be very successful in computer vision and bioinformatics tasks owing to their ability to automatically extract hierarchical descriptive features from input images or gene expression data. They have also been used in the oncological, neuroimaging, and microscopy imaging domains for automatic disease diagnosis [12,13], tissue segmentation [16,20], and even synthetic image generation. However, the main issue remains the relative sample paucity of the typical datasets that leads to a poor generalization of the employed deep Artificial Neural Networks, considering the high number of required parameters.…”

mentioning

confidence: 99%