Heterogeneous Breast Tissue Research Articles

Impact of Breast Tissue Heterogeneity on Microwave Ablation Performance: Insights from Computational Modeling

Impact of Breast Tissue Heterogeneity on Microwave Ablation Performance: Insights from Computational Modeling

Bevel-Tip Needle Deflection Modeling, Simulation, and Validation in Multi-Layer Tissues.

Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes as an effective alternative to more invasive surgical procedures. However, the outcome of needle-based approaches relies heavily on the accuracy of needle placement, which remains a challenge even with robot assistance and medical imaging guidance due to needle deflection caused by contact with soft tissues. In this paper, we present a novel mechanics-based 2D bevel-tip needle model that can account for the effect of nonlinear strain-dependent behavior of biological soft tissues under compression. Real-time finite element simulation allows multiple control inputs along the length of the needle with full three-degree-of-freedom (DOF) planar needle motions. Cross-validation studies using custom-designed multi-layer tissue phantoms as well as heterogeneous chicken breast tissues result in less than 1mm in-plane errors for insertions reaching depths of up to 61 mm, demonstrating the validity and generalizability of the proposed method.

TS-GCN: A novel tumor segmentation method integrating transformer and GCN.

As one of the critical branches of medical image processing, the task of segmentation of breast cancer tumors is of great importance for planning surgical interventions, radiotherapy and chemotherapy. Breast cancer tumor segmentation faces several challenges, including the inherent complexity and heterogeneity of breast tissue, the presence of various imaging artifacts and noise in medical images, low contrast between the tumor region and healthy tissue, and inconsistent size of the tumor region. Furthermore, the existing segmentation methods may not fully capture the rich spatial and contextual information in small-sized regions in breast images, leading to suboptimal performance. In this paper, we propose a novel breast tumor segmentation method, called the transformer and graph convolutional neural (TS-GCN) network, for medical imaging analysis. Specifically, we designed a feature aggregation network to fuse the features extracted from the transformer, GCN and convolutional neural network (CNN) networks. The CNN extract network is designed for the image's local deep feature, and the transformer and GCN networks can better capture the spatial and context dependencies among pixels in images. By leveraging the strengths of three feature extraction networks, our method achieved superior segmentation performance on the BUSI dataset and dataset B. The TS-GCN showed the best performance on several indexes, with Acc of 0.9373, Dice of 0.9058, IoU of 0.7634, F1 score of 0.9338, and AUC of 0.9692, which outperforms other state-of-the-art methods. The research of this segmentation method provides a promising future for medical image analysis and diagnosis of other diseases.

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

Ultra-Wide Band (UWB) microwave breast cancer detection is a promising new technology for routine physical examination and home monitoring. The existing microwave imaging algorithms for breast tumor detection are complex and the effect is still not ideal, due to the heterogeneity of breast tissue, skin reflection, and fibroglandular tissue reflection in backscatter signals. This study aims to develop a machine learning method to accurately locate breast tumor. A microwave-based breast tumor localization method is proposed by time-frequency feature extraction and neural network technology. First, the received microwave array signals are converted into representative and compact features by 4-level Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA). Then, the Genetic Algorithm-Neural Network (GA-NN) is developed to tune hyper-parameters of the neural network adaptively. The neural network embedded in the GA-NN algorithm is a four-layer architecture and 10-fold cross-validation is performed. Through the trained neural network, the tumor localization performance is evaluated on four datasets that are created by FDTD simulation method from 2-D MRI-derived breast models with varying tissue density, shape, and size. Each dataset consists of 1000 backscatter signals with different tumor positions, in which the ratio of training set to test set is 9:1. In order to verify the generalizability and scalability of the proposed method, the tumor localization performance is also tested on a 3-D breast model. For these 2-D breast models with unknown tumor locations, the evaluation results show that the proposed method has small location errors, which are 0.6076mm, 3.0813mm, 2.0798mm, and 3.2988mm, respectively, and high accuracy, which is 99%, 80%, 94%, and 85%, respectively. Furthermore, the location error and the prediction accuracy of the 3-D breast model are 3.3896mm and 81%. These evaluation results demonstrate that the proposed machine learning method is effective and accurate for microwave breast tumor localization. The traditional microwave-based breast cancer detection method is to reconstruct the entire breast image to highlight the tumor. Compared with the traditional method, our proposed method can directly get the breast tumor location by applying neural network to the received microwave array signals, and circumvent any complicated image reconstruction processing.

Employment of image oversampling and downsampling techniques for improving grayscale resolution

In multispectral transmission imaging of biological tissues, it is crucial to increase the grayscale resolution for accurate heterogeneity detection. This study proposes a method of obtaining high-grayscale-resolution images via image oversampling and downsampling. A single-wavelength transmission image of a simulated heterogeneous breast tissue was used to validate the effectiveness of the method experimentally. In the experiment, a high-resolution camera was used to capture transmission images of the simulated tissue with high spatial resolution $${\text{I}}_{\lambda }^{h}$$ and low spatial resolution $${\text{I}}_{\lambda }^{l}$$ . The high-spatial-resolution image $${\text{I}}_{\lambda }^{h}$$ was compressed into an image $${\text{I}}_{\lambda }^{hc}$$ with the same spatial resolution as that of the low-spatial-resolution image $${\text{I}}_{\lambda }^{l}$$ , thereby converting the spatial information into image depth information. The experimental results indicate that the compressed image $${\text{I}}_{\lambda }^{hc}$$ had higher grayscale resolution and richer grayscale details than those of the low-spatial-resolution image $${\text{I}}_{\lambda }^{l}$$ . Our results prove that it is feasible to improve the grayscale resolution of an image through image oversampling and downsampling while sacrificing the image spatial resolution to a certain extent. Further, this research provides an effective means to analyze heterogeneity in multispectral transmission imaging of a biological tissue.

A three-dimensional modeling method for quantitative photoacoustic breast imaging with handheld probe

By providing complementary functional information, photoacoustic (PA) breast imaging based on the handheld ultrasound (US) probe has demonstrated promising potential for breast cancer diagnosis. However, the quantitative PA imaging primarily relies on the knowledge of the optical fluence distribution in the three-dimensional (3D) heterogeneous breast tissue. Previous studies based on the handheld system generally provided two-dimensional (2D) B-scan results, which contains limited anatomical information of the tissue and the lesion. This study proposed a method to perform 3D modeling of the photon transportation for dual-modality PA/US system based on the local 3D breast anatomical information by scanning US probe. Then the calculated optical fluence distribution can be used for PA imaging. Our phantom and clinical pilot study results demonstrated that this method has potential to improve the accuracy of the quantitative PA breast imaging, and it can also be used in other clinical implementations.

The identification of the unloaded configuration of breast tissue with unknown non-homogenous stiffness parameters using surface measured data in deformed configuration

Large deformation analysis of the breast is known as a useful approach for locating the tumor and treatment strategies of breast cancer, for which knowing the breast stiffness parameters and unloaded configuration is crucial to obtain reliable results. In this study, an iterative inverse finite element algorithm is developed to identify the unloaded configuration of the breast while its stiffness constants are unknown and its internal structure is assumed to be non-homogeneous. The position vector of surface points in the deformed configuration of the breast is employed to obtain the unknowns of the inverse problem. An objective function based on the difference between the position vector of the calculated and measured deformed configurations is defined. Thereafter, the objective function is minimized using a gradient-based method. The sensitivity analysis for material parameters is performed using an analytic direct differentiation approach. Through several numerical examples, the effectiveness of the proposed inverse method for identifying the unloaded configuration of a uniform, a computational breast phantom with a single inclusion as well as a computational breast phantom with randomly distributed stiffness, is demonstrated. The effects of the number of load cases, measurement error, and initial guesses on the results of the inverse problem are investigated, as well. It is observed that the unloaded configuration of the computational breast phantom with a single inclusion or heterogeneous breast tissues can be accurately found by considering an equivalent homogenous material for the tissue.

Abstract P2-10-12: Relationship between predicted risks of carrying breast cancer susceptibility genes and the presence of germline variants in Japanese patients with primary breast cancer

Abstract Background: Breast cancer risk models are used to predict the risk of carrying a variant, for one of the most common breast cancer susceptibility genes such as BRCA1 and BRCA2, and the lifetime risk of developing breast cancer. The prediction of harboring a germline variant of the BRCA gene and the development of breast or ovarian cancer over time affects the decision-making for undergoing genetic testing and screening using imaging techniques as the common practice. For instance, the American Cancer Society and the National Comprehensive Cancer Network (NCCN) recommends screening using MRI in women with 20% or greater lifetime risk of having breast cancer. We aimed to investigate the prediction of these risks in Japanese women, particularly on the relationship between the presence of pathogenic germline variants and breast cancer susceptibility genes, using a cohort of 1016 primary breast cancer patients. Patients and Methods: We analyzed a cohort of Japanese patients with primary breast cancer who were treated at the Kyoto University Hospital and the related institutions or hospitals from the period of 2011 to 2016. The germline variants were examined for a set of 13 breast cancer susceptibility genes, using targeted-capture sequencing of pooled DNA, and it was found that 66 out of 1016 patients had pathogenic variants. These included 11 functionally well-established genes (BRCA1, BRCA2, TP53, PTEN, CDH1, STK11, NF1, PALB2, ATM, CHEK2, and NBN) and two additional genes (BARD1 and BRIP1), which are recommended for the screening of high-risk patients with hereditary breast cancer in the NCCN guidelines. Using this cohort, we studied the association of the calculated risk of carrying a germline variant of BRCA1/ BRCA2, using the Tyrer-Cuzick model Breast Cancer Risk Evaluation Tool, within the laboratory germline test results. Results: Pathogenic germline variants of BRCA1/ BRCA2 were carried by 54 (5.3%) out of the 1016 patients (12 cases of BRCA1 and 42 cases of BRCA2). According to the NCCN guidelines, which focus on Genetic/ Familial High-Risk Assessment: Breast and Ovarian, it was found that 500 out of 1016 (49.2%)patients were categorized for considering germline testing. In fact, 38 (7.6%) of the 500 patients, harbored a pathogenic germline variant of BRCA1/ BRCA2. In the remaining 516 patients, 16 (3.1%) harbored the pathogenic germline variant of BRCA1/ BRCA2. The predictive risks of the Tyrer-Cuzick model Breast Cancer Risk Evaluation Tool were recorded as follows: Area under the ROC curve, BRCA1 (area 0.750, 95% CI- 0.581-0.919), BRCA2 (area 0.741, 95% CI- 0.661-0.820), BRCA1 or BRCA2 (Area 0.749, 95% CI: 0.675-0.822), suggesting that the Tyrer-Cuzick model may be useful for the Japanese population. In the mammography breast density analysis, 484 patients showed almost entirely fat or scattered fibroglandular breast tissue, and 362 cases had heterogeneous or extreme fibroglandular breast tissue. In this study, the correlations of breast tissue density with age and breast or ovarian cancer familial history have been reported in greater detail. Discussion and Conclusions: In a retrospective cohort of 1016 Japanese patients with primary breast cancer, 5.3% had pathogenic germline variants of BRCA1/ BRCA2. In a group recommended by NCCN guidelines for considering genetic testing, the BRCA1/ BRCA2 variant rate was 7.6%. Predictive risks calculated by the Tyrer-Cuzick model similar with the known data. Further data are reported. Citation Format: Noriko Senda, Nobuko Kawaguchi-Sakita, Masahiro Kawashima, Yukiko Inagaki-Kawata, Kenichi Yoshida, Tomomi Nishimura, Masahiro Takada, Eiji Suzuki, Yuki Kataoka, Fumiaki Sato, Yoshiaki Matsumoto, Masae Torii, Hiroshi Yoshibayashi, Kazuhiro Yamagami, Shigeru Tsuyuki, Akira Yamauchi, Nobuhiko Shinkura, Hironori Kato, Yoshio Moriguchi, Ryuji Okamura, Norimichi Kan, Hirofumi Suwa, Shingo Sakata, Susumu Mashima, Fumiaki Yotsumoto, Tsuyoshi Tachibana, Mitsuru Tanaka, Takashi Inamoto, Masahiro Sugimoto, Seishi Ogawa, Masakazu Toi. Relationship between predicted risks of carrying breast cancer susceptibility genes and the presence of germline variants in Japanese patients with primary breast cancer [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-10-12.

Robust Short-Lag Spatial Coherence Imaging of Breast Ultrasound Data: Initial Clinical Results.

Ultrasound is frequently used in conjunction with mammography in order to detect breast cancer as early as possible. However, due largely to the heterogeneity of breast tissue, ultrasound images are plagued with clutter that obstructs important diagnostic features. Short-lag spatial coherence (SLSC) imaging has proven to be effective at clutter reduction in noisy ultrasound images. M -Weighted SLSC and Robust-SLSC (R-SLSC) imaging were recently introduced to further improve image quality at higher lag values, while R-SLSC imaging has the added benefit of enabling the adjustment of tissue texture to produce a tissue signal-to-noise ratio (SNR) that is quantitatively similar to B-mode speckle SNR. This paper investigates the initial application of SLSC, M -Weighted SLSC, and R-SLSC imaging to nine targets in the female breast [two simple cysts, one complicated cyst, two fibroadenomas, one hematoma, one complex cystic and solid mass, one invasive ductal carcinoma (IDC), and one ductal carcinoma in situ (DCIS)]. As expected, R-SLSC beamforming improves cyst and hematoma contrast by up to 6.35 and 1.55 dB, respectively, when compared to the original B-mode image, and similar improvements are achieved with SLSC and M -Weighted SLSC imaging. However, an interesting finding from this initial investigation is that the solid masses (i.e., fibroadenoma, complex cystic and solid mass, IDC, and DCIS), which appear as hypoechoic in the B-mode image, have similarly high coherence to that of surrounding tissue in coherence-based images. This work holds promise for using SLSC, M -Weighted SLSC, and/or R-SLSC imaging to distinguish between fluid-filled and solid hypoechoic breast masses.

A Multithreshold Iterative DBIM-Based Algorithm for the Imaging of Heterogeneous Breast Tissues.

This paper proposes a novel microwave imaging (MWI) multifrequency technique, which combines compressive sensing (CS) with the well-known distorted Born iterative method. CS strategies are emerging as a promising tool in MWI applications, which can improve reconstruction quality and/or reduce the number of data samples. The proposed approach is based on iterative shrinkage thresholding algorithm (ISTA), which has been modified to include an automatic and adaptive selection of multithreshold values. This adaptive multithreshold ISTA implementation is applied in reconstruction of two-dimensional (2-D) numerical heterogeneous breast phantoms, where it outperforms the standard thresholding implementation. We show that our approach is also successful in 3-D simulations of a realistic imaging experiment, despite the mismatch between the data and our algorithm's forward model. These results suggest that the proposed algorithm is a promising tool for medical MWI applications. Important novelties of this approach are the use of multiple thresholds to recover the different unknowns in the Debye model as well as the adaptive selection of these thresholds. Moreover, we have shown that employing modified hard constraints inside the linear step of the inversion procedure can enhance reconstruction quality.

Intra-individual Gene Expression Variability of Histologically Normal Breast Tissue

Several studies have sought to identify novel transcriptional biomarkers in normal breast or breast microenvironment to predict tumor risk and prognosis. However, systematic efforts to evaluate intra-individual variability of gene expression within normal breast have not been reported. This study analyzed the microarray gene expression data of 288 samples from 170 women in the Normal Breast Study (NBS), wherein multiple histologically normal breast samples were collected from different block regions and different sections at a given region. Intra-individual differences in global gene expression and selected gene expression signatures were quantified and evaluated in association with other patient-level factors. We found that intra-individual reliability was relatively high in global gene expression, but differed by signatures, with composition-related signatures (i.e., stroma) having higher intra-individual variability and tumorigenesis-related signatures (i.e., proliferation) having lower intra-individual variability. Histological stroma composition was the only factor significantly associated with heterogeneous breast tissue (defined as > median intra-individual variation; high nuclear density, odds ratio [OR] = 3.42, 95% confidence interval [CI] = 1.15–10.15; low area, OR = 0.29, 95% CI = 0.10–0.86). Other factors suggestively influencing the variability included age, BMI, and adipose nuclear density. Our results underscore the importance of considering intra-individual variability in tissue-based biomarker development, and have important implications for normal breast research.

TDOA-based microwave imaging algorithm for real-time microwave ablation monitoring

Microwave ablation is widely recognized as a promising minimally invasive tool for treating cancer. Real-time monitoring of the dimensions of the ablation zone is indispensable for ensuring an effective and safe treatment. In this paper, we propose a microwave imaging algorithm for monitoring the evolution of the ablation zone. Our proposed algorithm determines the boundary of the ablation zone by exploiting the time difference of arrival (TDOA) between signals received before and during the ablation at external antennas surrounding the tissue, using the interstitial ablation antenna as the transmitter. A significant advantage of this method is that it requires few assumptions about the dielectric properties of the propagation media. Also the simplicity of the signal processing, wherein the TDOA is determined from a cross-correlation calculation, allows real-time monitoring and provides robust performance in the presence of noise. We investigate the performance of this approach for the application of breast tumor ablation. We use simulated array measurements obtained from finite-difference time-domain simulations of magnetic resonance imaging-derived numerical breast phantoms. The results demonstrate that our proposed method offers the potential to achieve millimeter-order accuracy and real-time operation in estimating the boundary of the ablation zone in heterogeneous and dispersive breast tissue.

Mitigating acoustic heterogeneous effects in microwave-induced breast thermoacoustic tomography using multi-physical K-means clustering

Microwave-induced thermoacoustic tomography shows great potential for early-stage breast tumor detection, but imaging quality usually suffers due to acoustic heterogeneity of breast tissue. To mitigate this problem, conventional methods estimate the distribution of speeds of sound but at a heavy cost of system complexity or computation burden. We propose an imaging reconstruction method that incorporates dielectric and acoustic properties of tissues as a-priori information and reformulates the velocity estimation problem as a data clustering problem. The proposed method is validated by imaging anatomically realistic numerical breast phantoms and real biological tissues. Both simulation and experimental results demonstrate that the proposed method is robust and significantly improves image fidelity with less computational burden than conventional methods. These results make our approach a promising candidate for clinical breast cancer detection.

MicroRNA Expression in Laser Micro-dissected Breast Cancer Tissue Samples – a Pilot Study

Breast cancer continues to represent a significant public health burden despite outstanding research advances regarding the molecular mechanisms of cancer biology, biomarkers for diagnostics and prognostic and therapeutic management of this disease. The studies of micro RNAs in breast cancer have underlined their potential as biomarkers and therapeutic targets; however most of these studies are still done on largely heterogeneous whole breast tissue samples. In this pilot study we have investigated the expression of four micro RNAs (miR-21, 145, 155, 92) known to be involved in breast cancer, in homogenous cell populations collected by laser capture microdissection from breast tissue section slides. Micro RNA expression was assessed by real time PCR, and associations with clinical and pathological characteristics were also explored. Our results have confirmed previous associations of miR-21 expression with poor prognosis characteristics of breast cancers such as high stage, large and highly proliferative tumors. No statistically significant associations were found with the other micro RNAs investigated, possibly due to the small sample size of our study. Our results also suggest that miR-484 could be a suitable endogenous control for data normalization in breast tissues, these results needing further confirmation by future studies. In summary, our pilot study showed the feasibility of detecting micro RNAs expression in homogenous laser captured microdissected invasive breast cancer samples, and confirmed some of the previously reported associations with poor prognostic characteristics of breast tumors.

Heterogeneity of Diffusion-Weighted Imaging in Tumours and the Surrounding Stroma for Prediction of Ki-67 Proliferation Status in Breast Cancer

Breast tissue heterogeneity is related to risk factors that lead to more aggressive tumour growth and worse prognosis, yet such heterogeneity has not been well characterized. The aim of this study is to reveal the heterogeneous signal patterns of the apparent diffusion coefficient (ADC) of a tumour and its surrounding stromal tissue and to predict the Ki-67 proliferation status in oestrogen receptor (ER)-positive breast cancer patients. A dataset of 82 patients who underwent diffusion-weighted imaging (DWI) examination was collected. The ADC map was segmented into regions comprising the tumour and the surrounding stromal shells. To reflect correlations between each region in terms of its mean ADC value, a functional graph was constructed consisting of nodes as regions and edges as interactions between two nodes. Analysis of the graph revealed a higher average degree in samples over-expressing Ki-67 than in samples with low Ki-67 expression. In the low-Ki-67 group, most of the identified edges represented correlations between adjacent regions, whereas additional edges representing correlations between non-adjacent regions were found in the high-Ki-67 group. The ADC signal in various breast stromal regions surrounding the tumour showed a discriminative pattern and would be valuable for estimating the Ki-67 proliferation status by DWI.

Rapid evaporative ionisation mass spectrometry of electrosurgical vapours for the identification of breast pathology: towards an intelligent knife for breast cancer surgery

BackgroundRe-operation for positive resection margins following breast-conserving surgery occurs frequently (average = 20–25%), is cost-inefficient, and leads to physical and psychological morbidity. Current margin assessment techniques are slow and labour intensive. Rapid evaporative ionisation mass spectrometry (REIMS) rapidly identifies dissected tissues by determination of tissue structural lipid profiles through on-line chemical analysis of electrosurgical aerosol toward real-time margin assessment.MethodsElectrosurgical aerosol produced from ex-vivo and in-vivo breast samples was aspirated into a mass spectrometer (MS) using a monopolar hand-piece. Tissue identification results obtained by multivariate statistical analysis of MS data were validated by histopathology. Ex-vivo classification models were constructed from a mass spectral database of normal and tumour breast samples. Univariate and tandem MS analysis of significant peaks was conducted to identify biochemical differences between normal and cancerous tissues. An ex-vivo classification model was used in combination with bespoke recognition software, as an intelligent knife (iKnife), to predict the diagnosis for an ex-vivo validation set. Intraoperative REIMS data were acquired during breast surgery and time-synchronized to operative videos.ResultsA classification model using histologically validated spectral data acquired from 932 sampling points in normal tissue and 226 in tumour tissue provided 93.4% sensitivity and 94.9% specificity. Tandem MS identified 63 phospholipids and 6 triglyceride species responsible for 24 spectral differences between tissue types. iKnife recognition accuracy with 260 newly acquired fresh and frozen breast tissue specimens (normal n = 161, tumour n = 99) provided sensitivity of 90.9% and specificity of 98.8%. The ex-vivo and intra-operative method produced visually comparable high intensity spectra. iKnife interpretation of intra-operative electrosurgical vapours, including data acquisition and analysis was possible within a mean of 1.80 seconds (SD ±0.40).ConclusionsThe REIMS method has been optimised for real-time iKnife analysis of heterogeneous breast tissues based on subtle changes in lipid metabolism, and the results suggest spectral analysis is both accurate and rapid. Proof-of-concept data demonstrate the iKnife method is capable of online intraoperative data collection and analysis. Further validation studies are required to determine the accuracy of intra-operative REIMS for oncological margin assessment.

Automated modification and fusion of voxel models to construct body phantoms with heterogeneous breast tissue: Application to MRI simulations.

Human voxel models incorporating detailed anatomical features are vital tools for the computational evaluation of electromagnetic (EM) fields within the body. Besides whole-body human voxel models, phantoms representing smaller heterogeneous anatomical features are often employed; for example, localized breast voxel models incorporating fatty and fibroglandular tissues have been developed for a variety of EM applications including mammography simulation and dosimetry, magnetic resonance imaging (MRI), and ultra-wideband microwave imaging. However, considering wavelength effects, electromagnetic modeling of the breast at sub-microwave frequencies necessitates detailed breast phantoms in conjunction with whole-body voxel models. Heterogeneous breast phantoms are sized to fit within radiofrequency coil hardware, modified by voxel-wise extrusion, and fused to whole-body models using voxel-wise, tissue-dependent logical operators. To illustrate the utility of this method, finite-difference time-domain simulations are performed using a whole-body model integrated with a variety of available breast phantoms spanning the standard four tissue density classifications representing the majority of the population. The software library uses a combination of voxel operations to seamlessly size, modify, and fuse eleven breast phantoms to whole-body voxel models. The software is publicly available on GitHub and is linked to the file exchange at MATLAB® Central. Simulations confirm the proportions of fatty and fibroglandular tissues in breast phantoms have significant yet predictable implications on projected power deposition in tissue. Breast phantoms may be modified and fused to whole-body voxel models using the software presented in this work; user considerations for the open-source software and resultant phantoms are discussed. Furthermore, results indicate simulating breast models as predominantly fatty tissue can considerably underestimate the potential for tissue heating in women with substantial fibroglandular tissue.

Ultrasonic Measurement of Temperature Rise in Breast Cyst and in Neighbouring Tissues as a Method of Tissue Differentiation

Abstract Texture of ultrasound images contain information about the properties of examined tissues. The analysis of statistical properties of backscattered ultrasonic echoes has been recently successfully applied to differentiate healthy breast tissue from the benign and malignant lesions. We propose a novel procedure of tissue characterization based on acquiring backscattered echoes from the heated breast. We have proved that the temperature increase inside the breast modifies the intensity, spectrum of the backscattered signals and the probability density function of envelope samples. We discuss the differences in probability density functions in two types of tissue regions, e.g. cysts and the surrounding glandular tissue regions. Independently, Pennes bioheat equation in heterogeneous breast tissue was used to describe the heating process. We applied the finite element method to solve this equation. Results have been compared with the ultrasonic predictions of the temperature distribution. The results confirm the possibility of distinguishing the differences in thermal and acoustical properties of breast cyst and surrounding glandular tissues.

Numerical Modeling of Human Tissues and Scattering Parameters for Microwave Cancer Imaging Systems

In this paper the use of S-parameters (scattering parameters) in the design of microwave Ultra-Wide Band, UWB transceiver system has been investigated. The S-parameters are mainly reflection (S11 or Γ) and transmission coefficients (S12 or Τ). Numerical modeling of them is achieved by means of computational electromagnetic tool called finite difference frequency domain and the sample tissues taken are human breast. The reason for selecting frequency domain simulations is because S-parameters are also analyzed in frequency domain. Numerical modeling helps in simulating the problem much faster than corresponding analytical simulations thus helping in detecting tumors in the tissues quicker. The results are compared with analytical values to find error and accuracy in the numerical computations. Results showed that S11-parameters are very handy for the design of transmitter and receiver filters for a microwave ultra-wide band system and how they can be efficiently used for early detection of cancer (benign or malignant) in normal human tissues for microwave cancer imaging systems. Individuality of this research work is that instead of individual layers analysis (as done in the past) full heterogeneous breast tissue is analytically and numerically modeled here for finding its channel impulse response.

Abstract P2-12-20: Rapid evaporative ionisation mass spectrometry towards real time intraoperative oncological margin status determination in breast conserving surgery

Abstract Introduction: Positive tumour margins following attempted breast conserving surgery (BCS) is an important risk factor for local recurrence. Nationally in the United Kingdom on average approximately 25% of patients undergoing BCS require additional surgery for positive margins. Traditional techniques such as specimen xray, frozen section & imprint cytology to optimise margin clearance have significant limitations. Various research methods under investigation include optical spectroscopy, high resolution imaging and radiofrequency spectroscopy. Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a new method that uses mass spectrometric analysis of the tissue specific ionic content of the surgical diathermy smoke plume for the rapid identification of dissected breast tissues as an intelligent knife (iKnife). We investigate the ability of the "iKnife" to analyze heterogeneous breast tissue intraoperatively using mass spectrometric techniques. Method: The study involved three stages that comprised: method development, tissue specific ex-vivo database construction and intraoperative analysis. Smoke aerosol produced as a result of electrosurgical diathermy from a variety of frozen, fresh and in-vivo breast samples were aspirated into a mass spectrometer via a modified surgical handpiece. Tissue diagnosis was confirmed by subsequent histopathological validation. The data underwent computational analysis using multivariate statistics –predominantly Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), along with leave one patient out cross-validation. A total of 128 patients (n=40 method development, n=66 ex-vivo database, n=22 intraoperative analysis) undergoing breast surgery were enrolled in this study. Ethical approval was obtained from the Research Ethics Committee. Results: 40 patients contributed breast samples (normal and cancerous) for method optimisation to enable analysis of high intensity spectra from heterogeneous breast tissue. Following optimisation an ex-vivo database was constructed from 89 excised fresh breast tissue samples from 66 patients using 330 spectra (246 Normal, 60 Tumour – IDC, ILC, IMC and 24 Benign - fibroadenoma). Multivariate statistical analysis of data revealed classification of tumour compared to normal tissue with sensitivities of 93.0% and specificity of 91.9%. The iKnife was used intraoperatively during the entire operation of 25 surgeries. Spectral data was obtained within 1-2 seconds. Specific margin analysis correctly identified negative margins in 10 cases. Conclusions: The iKnife has been successfully developed for analysis of intraoperative heterogeneous breast tissue. Preliminary data suggests that this technique is suitable with high accuracy for the separation of normal, benign (fibroadenoma) and cancerous (invasive ductal and invasive lobular carcinoma) breast tissues. In comparison to the normal breast, cancerous tissues exhibit statistically different spectral profiles. Further work is aimed at the development of a real time algorithm able to match intraoperative data with the pre-existing database for the rapid interpretation and real time feedback of intraoperative data towards detecting positive margins intraoperatively. Citation Format: St John ER, Al-Khudairi R, Balog J, Rossi M, Gildea L, Speller A, Ramakrishnan R, Shousha S, Takats Z, Leff DR, Darzi A. Rapid evaporative ionisation mass spectrometry towards real time intraoperative oncological margin status determination in breast conserving surgery. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P2-12-20.