Classification of breast tumor models with a prototype microwave imaging system

Abstract

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.

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

“…Conversely, benign tumors tend to have the following characteristics: well-circumscribed contours, compactness, and a smooth surface. Previous research works on breast lesion characterization/classification with MBI [26][27][28][29][30] considered principally the MBI received signals as input to a classifier, with or without dimensionality reduction. These state-of-the-art research works [26][27][28][29][30] have been based on simulated datasets and/or simplified experimental setups; no evaluation of such methods on patient clinical datasets has been published to date.…”

“…Previous research works on breast lesion characterization/classification with MBI [26][27][28][29][30] considered principally the MBI received signals as input to a classifier, with or without dimensionality reduction. These state-of-the-art research works [26][27][28][29][30] have been based on simulated datasets and/or simplified experimental setups; no evaluation of such methods on patient clinical datasets has been published to date. Among the state-of-the-art MBI prototypes which have been tested on clinical datasets, two of them published studies on breast lesion classification with MBI.…”

Development of 3D MRI-Based Anatomically Realistic Models of Breast Tissues and Tumours for Microwave Imaging Diagnosis

“…Breast tumours have been modelled, more realistically, with Gaussian Random Spheres. These were used for validation tests of a microwave imaging device in [32], and for tumour classification using a MWI prototype system in [33]. In [34], realistic benign and malignant breast phantoms were carved by hand, resulting in approximate spherical and spiculated models for benign and malignant tumours, respectively.…”

Microwave breast tumor localization using wavelet feature extraction and genetic algorithm‐neural network

“…On the other hand, artificial intelligence technology with strong learning and representation ability has great attraction and potential in microwave breast cancer detection. Recently, this technology was used to detect the presence, benign and malignant of breast tumors 30,34–37 . Song et al.…”