Currently, breast cancer often requires invasive biopsies for diagnosis, motivating researchers to design and develop non-invasive and automated diagnosis systems. Recent microwave breast imaging studies have shown how backscattered signals carry relevant information about the shape of a tumour, and tumour shape is often used with current imaging modalities to assess malignancy. This paper presents a comprehensive analysis of microwave breast diagnosis systems which use machine learning to learn characteristics of benign and malignant tumours. The state-of-the-art, the main challenges still to overcome and potential solutions are outlined. Specifically, this work investigates the benefit of signal pre-processing on diagnostic performance, and proposes a new set of extracted features that capture the tumour shape information embedded in a signal. This work also investigates if a relationship exists between the antenna topology in a microwave system and diagnostic performance. Finally, a careful machine learning validation methodology is implemented to guarantee the robustness of the results and the accuracy of performance evaluation.
Purpose: The assessment of the size and shape of breast tumors is of utter importance to the correct diagnosis and staging of breast cancer. In this paper, we classify breast tumor models of varying sizes and shapes using signals collected with a monostatic ultra-wideband radar microwave imaging prototype system with machine learning algorithms specifically tailored to the collected data. Methods: A database comprising 13 benign and 13 malignant tumor models with sizes between 13 and 40 mm was created using dielectrically representative tissue mimicking materials. These tumor models were placed inside two breast phantoms: a homogeneous breast phantom and a breast phantom with clusters of fibroglandular mimicking tissue, accounting for breast heterogeneity. The breast phantoms with tumors were imaged with a monostatic microwave imaging prototype system, over a 1-6 GHz frequency range. The classification of benign and malignant tumors embedded in the two breast phantoms was completed, and tumor classification was evaluated with Principal Component Analysis as a feature extraction method, and tuned Na€ ıve Bayes (NB), decision trees (DT), and knearest neighbours (kNN) as classifiers. We further study which antenna positions are better placed to classify tumors, discuss the feature extraction method and optimize classification algorithms, by tuning their hyperparameters, to improve sensitivity, specificity and the receiver operating characteristic curve, while ensuring maximum generalization and avoiding overfitting and data contamination. We also added a realistic synthetic skin response to the collected signals and examined its global effect on classification of benign vs malignant tumors. Results: In terms of global classification performance, kNN outperformed DT and NB machine learning classifiers, achieving a classification accuracy of 96.2% when classifying between benign and malignant tumor phantoms in a homogeneous breast phantom (both when the skin artifact is and is not considered). Conclusions:We experimentally classified tumor models as benign or malignant with a microwave imaging system, and we showed a methodology that can potentially assess the shape of breast tumors, which will give further insight into the correct diagnosis and staging of breast cancer.
The number of metastasised Axillary Lymph Nodes (ALNs) is a key indicator for breast cancer staging. Its correct assessment affects subsequent therapeutic decisions. Common ALN screening modalities lack high enough sensitivity and specificity. Level I ALNs produce detectable backscattering of microwaves, opening the way for Microwave Imaging (MWI) as a complementary screening modality. Radar-based MWI is a low-cost, non-invasive technique, widely studied for breast cancer and brain stroke detection. However, new specific challenges arise for ALN detection, which deter a simple extension of existing MWI methods. The geometry of the axillary region is more complex, limiting the antenna travel range required for maximum resolution. Additionally, unlike breast MWI setups, it is impractical to use liquid immersion to enhance energy coupling to the body; therefore, higher skin reflection masks ALNs response. We present a complete study that proposes dedicated imaging algorithms to detect ALNs dealing with the above constraints, and evaluate their effectiveness experimentally. We describe the developed setup based on a 3D-printed anthropomorphic phantom, and the antenna-positioning configuration. To the authors' knowledge, this is the first ALN-MWI study involving a fully functional anatomically compliant setup. A Vivaldi antenna, operating in a monostatic radar mode at 2-5 GHz, scans the axillary region. Pre-clinical assessment in different representative scenarios shows Signal-to-Clutter Ratio higher than 2.8 dB and Location Error lower than 15 mm, which is smaller than considered ALN dimensions. Our study shows promising level I ALN detection results despite the new challenges, confirming MWI potential to aid breast cancer staging.
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