Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range (IQR)=6%]. When excluding low-confidence patches, the achieved median recall was increased to 98% (IQR=5%), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.

We produced an anatomically and dielectrically realistic phantom of the axillary region to enable the experimental assessment of Axillary Lymph Node (ALN) imaging using microwave imaging technology. We segmented a thoracic Computed Tomography (CT) scan and created a computer-aided designed file containing the anatomical configuration of the axillary region. The phantom comprises five 3D-printed parts representing the main tissues of interest of the axillary region for the purpose of microwave imaging: fat, muscle, bone, ALNs, and lung. The phantom allows the experimental assessment of multiple anatomical configurations, by including ALNs of different size, shape, and number in several locations. Except for the bone mimicking organ, which is made of solid conductive polymer, we 3D-printed cavities to represent the fat, muscle, ALN, and lung and filled them with appropriate tissue-mimicking liquids. Existing studies about complex permittivity of ALNs have reported limitations. To address these, we measured the complex permittivity of both human and animal lymph nodes using the standard open-ended coaxial-probe technique, over the 0.5 GHz–8.5 GHz frequency band, thus extending current knowledge on dielectric properties of ALNs. Lastly, we numerically evaluated the effect of the polymer which constitutes the cavities of the phantom and compared it to the realistic axillary region. The results showed a maximum difference of 7 dB at 4 GHz in the electric field magnitude coupled to the tissues and a maximum of 10 dB difference in the ALN response. Our results showed that the phantom is a good representation of the axillary region and a viable tool for pre-clinical assessment of microwave imaging technology.

We assess the application of microwave tomography (MWT) for the detection of axillary lymph nodes (ALNs) in breast cancer patients. We numerically study the effects of limiting angular view in axillary MWT, as probes can only be placed on a limited arc around the axillary region. We also numerically study the possibility of increasing the amount of retrievable information by acquiring data in two consecutive steps, with a single antenna set in two different angular positions. We finally experimentally test axillary MWT on anthropomorphic phantoms with different levels of anatomical fidelity, and different ALN positions. Our MWT system (0.8–2.5 GHz) employs six monopole antennas placed on a single transverse plane, facing the axillary region. The reconstruction algorithm implements the distorted Born iterative method, combined with the two-step iterative shrinkage/thresholding for the inversion (DBIM-TwIST). Our numerical results i) highlight the challenges associated with the limited angular view, and ii) show that performing two-step angular measurements enhances imaging results, suggesting that rotating the antenna set between consecutive measurements is an effective means to increase the retrievable information in ALN MWT. Our experimental results show that our MWT system can detect an ALN in different positions. To the best of our knowledge this is the first paper to assess ALN MWT in a realistic 3-D experimental scenario.

In this letter we study the effect of freezing and defrosting on the dielectric properties of biological tissues. The electromagnetic characterization of tissues at microwave frequencies is crucial for the development of microwave-based biomedical devices. These measurements are often not practical, as tissue degradation restricts the time available between tissue excision and dielectric measurements. For this reason, measurement of tissues that underwent freezing and defrosting may provide researchers with more flexibility in setting measurement campaigns, thus speeding up the development of microwave-based biomedical devices. To this end, this letter presents dielectric measurement on frozen and defrosted tissue, which translates into the following objectives: first, investigate if the dielectric properties of defrosted tissues depend on frozen storage time; second, determine if defrosted tissue dielectric properties differ from those of fresh tissues. As a result, we measure the dielectric properties of ten samples of chicken muscle, bovine liver, and bovine fat, each before and after freezing (up to 14 days) and defrosting. The measurements are performed with the open-ended coaxial probe method at the frequency band of 0.5–8.5 GHz. We observe a slight increase—less than 10%—in complex permittivity of high-water-content tissues (muscle and liver) after defrosting, and negligible effect on fat tissues.