Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real-time diagnosis. Here, we propose a real-time classification scheme that combines US breast imaging reporting and data system (BI-RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI-RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction.

3D Superclusters with Hybrid Bioinks for Early Detection in Breast Cancer

The surface-enhanced Raman scattering (SERS) technique has garnered significant interest due to its ultrahigh sensitivity, making it suitable for addressing the growing demand for disease diagnosis. In addition to its sensitivity and uniformity, an ideal SERS platform should possess characteristics such as simplicity in manufacturing and low analyte consumption, enabling practical applications in complex diagnoses including cancer. Furthermore, the integration of machine learning algorithms with SERS can enhance the practical usability of sensing devices by effectively classifying the subtle vibrational fingerprints produced by molecules such as those found in human blood. In this study, we demonstrate an approach for early detection of breast cancer using a bottom-up strategy to construct a flexible and simple three-dimensional (3D) plasmonic cluster SERS platform integrated with a deep learning algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced Raman intensity through detection limit down to 10-6 M (femtomole-(10-17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects and healthy subjects was used to fabricate the bioink to build 3D-PC structures. The collected SERS successfully classified into two clusters of cancer subjects and healthy subjects with high accuracy of up to 93%. These results highlight the potential of the 3D plasmonic cluster SERS platform for early breast cancer detection and open promising avenues for future research in this field.

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121-each Convolutional Neural Network architecture designed for classification tasks-this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.

Histopathological Image Diagnosis for Breast Cancer Diagnosis Based on Deep Mutual Learning

Every year, millions of women across the globe are diagnosed with breast cancer (BC), an illness that is both common and potentially fatal. To provide effective therapy and enhance patient outcomes, it is essential to make an accurate diagnosis as soon as possible. In recent years, deep-learning (DL) approaches have shown great effectiveness in a variety of medical imaging applications, including the processing of histopathological images. Using DL techniques, the objective of this study is to recover the detection of BC by merging qualitative and quantitative data. Using deep mutual learning (DML), the emphasis of this research was on BC. In addition, a wide variety of breast cancer imaging modalities were investigated to assess the distinction between aggressive and benign BC. Based on this, deep convolutional neural networks (DCNNs) have been established to assess histopathological images of BC. In terms of the Break His-200×, BACH, and PUIH datasets, the results of the trials indicate that the level of accuracy achieved by the DML model is 98.97%, 96.78, and 96.34, respectively. This indicates that the DML model outperforms and has the greatest value among the other methodologies. To be more specific, it improves the results of localization without compromising the performance of the classification, which is an indication of its increased utility. We intend to proceed with the development of the diagnostic model to make it more applicable to clinical settings.

Unveiling the future of breast cancer assessment: a critical review on generative adversarial networks in elastography ultrasound

Elastography Ultrasound provides elasticity information of the tissues, which is crucial for understanding the density and texture, allowing for the diagnosis of different medical conditions such as fibrosis and cancer. In the current medical imaging scenario, elastograms for B-mode Ultrasound are restricted to well-equipped hospitals, making the modality unavailable for pocket ultrasound. To highlight the recent progress in elastogram synthesis, this article performs a critical review of generative adversarial network (GAN) methodology for elastogram generation from B-mode Ultrasound images. Along with a brief overview of cutting-edge medical image synthesis, the article highlights the contribution of the GAN framework in light of its impact and thoroughly analyzes the results to validate whether the existing challenges have been effectively addressed. Specifically, This article highlights that GANs can successfully generate accurate elastograms for deep-seated breast tumors (without having artifacts) and improve diagnostic effectiveness for pocket US. Furthermore, the results of the GAN framework are thoroughly analyzed by considering the quantitative metrics, visual evaluations, and cancer diagnostic accuracy. Finally, essential unaddressed challenges that lie at the intersection of elastography and GANs are presented, and a few future directions are shared for the elastogram synthesis research.

Sonographic Characteristics and Pathology Correlation of Breast Imaging Reporting and Data System (BI-RADS) Category 4 Lesions

INTRODUCTION

The Breast Imaging-Reporting and Database System (BI-RADS) category 4 is designated for breast lumps that do not display the typical features of malignancy but still raise enough suspicion to warrant a recommendation for a biopsy, as malignancy cannot be ruled out through imaging alone. The main objective of this study was to investigate the sonographic characteristics and pathology correlation of BI-RADS 4 breast lesions and determine the positive predictive rate of BI-RADS 4 lesions in diagnosing breast cancer, using histopathology as the gold standard.

METHODS

This was a cross-sectional study conducted at the Department of Radiology, Aga Khan University Hospital in Karachi, spanning from May 2021 to August 2022, with a duration of 15 months. The study focused on female patients over the age of 18 who presented with suspicious breast lesions on ultrasound. Both mammography and ultrasound-guided core needle biopsy were performed on these patients, followed by a detailed histopathological evaluation of the biopsy specimens. To calculate the positive predictive value (PPV), true positive cases were identified through both histopathology and ultrasonography.

RESULTS

A total of 227 cases were categorized as BI-RADS 4 lesions, with the patients' mean age being 47.8 ± 14.3 years (range: 17 - 88). Among the biopsied lesions, 101 cases were confirmed to be true positive for breast malignancies, resulting in a PPV for malignancy of 44.9%. Conversely, there were 124 false positive cases out of the 227 BI-RADS 4 category lesions (54.63%). The primary indication for presentation was a breast lump, and out of the 101 confirmed malignant cases, 70 (69.3%) were associated with malignancy.

CONCLUSION

BI-RADS 4 can be utilized to assess suspicious breast lumps; however, for more reliable results and to avoid false negatives, histopathological confirmation should complement the imaging findings.

SNSVM: SqueezeNet-Guided SVM for Breast Cancer Diagnosis

Breast cancer is a major public health concern that affects women worldwide. It is a leading cause of cancer-related deaths among women, and early detection is crucial for successful treatment. Unfortunately, breast cancer can often go undetected until it has reached advanced stages, making it more difficult to treat. Therefore, there is a pressing need for accurate and efficient diagnostic tools to detect breast cancer at an early stage. The proposed approach utilizes SqueezeNet with fire modules and complex bypass to extract informative features from mammography images. The extracted features are then utilized to train a support vector machine (SVM) for mammography image classification. The SqueezeNet-guided SVM model, known as SNSVM, achieved promising results, with an accuracy of 94.10% and a sensitivity of 94.30%. A 10-fold cross-validation was performed to ensure the robustness of the results, and the mean and standard deviation of various performance indicators were calculated across multiple runs. This model also outperforms state-of-the-art models in all performance indicators, indicating its superior performance. This demonstrates the effectiveness of the proposed approach for breast cancer diagnosis using mammography images. The superior performance of the proposed model across all indicators makes it a promising tool for early breast cancer diagnosis. This may have significant implications for reducing breast cancer mortality rates.

Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning

Significance

Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.

Aim

We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions.

Approach

We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis.

Results

The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features.

Conclusions

The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.

MicroRNA-Based Discovery of Biomarkers, Therapeutic Targets, and Repositioning Drugs for Breast Cancer

Breast cancer treatment can be improved with biomarkers for early detection and individualized therapy. A set of 86 microRNAs (miRNAs) were identified to separate breast cancer tumors from normal breast tissues (n = 52) with an overall accuracy of 90.4%. Six miRNAs had concordant expression in both tumors and breast cancer patient blood samples compared with the normal control samples. Twelve miRNAs showed concordant expression in tumors vs. normal breast tissues and patient survival (n = 1093), with seven as potential tumor suppressors and five as potential oncomiRs. From experimentally validated target genes of these 86 miRNAs, pan-sensitive and pan-resistant genes with concordant mRNA and protein expression associated with in-vitro drug response to 19 NCCN-recommended breast cancer drugs were selected. Combined with in-vitro proliferation assays using CRISPR-Cas9/RNAi and patient survival analysis, MEK inhibitors PD19830 and BRD-K12244279, pilocarpine, and tremorine were discovered as potential new drug options for treating breast cancer. Multi-omics biomarkers of response to the discovered drugs were identified using human breast cancer cell lines. This study presented an artificial intelligence pipeline of miRNA-based discovery of biomarkers, therapeutic targets, and repositioning drugs that can be applied to many cancer types.

Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing

Across all countries, both developing and developed, women face the greatest risk of breast cancer. Patients who have their breast cancer diagnosed and staged early have a better chance of receiving treatment before the disease spreads. The automatic analysis and classification of medical images are made possible by today's technology, allowing for quicker and more accurate data processing. The Internet of Things (IoT) is now crucial for the early and remote diagnosis of chronic diseases. In this study, mammography images from the publicly available online repository The Cancer Imaging Archive (TCIA) were used to train a deep transfer learning (DTL) model for an autonomous breast cancer diagnostic system. The data were pre-processed before being fed into the model. A popular deep learning (DL) technique, i.e., convolutional neural networks (CNNs), was combined with transfer learning (TL) techniques such as ResNet50, InceptionV3, AlexNet, VGG16, and VGG19 to boost prediction accuracy along with a support vector machine (SVM) classifier. Extensive simulations were analyzed by employing a variety of performances and network metrics to demonstrate the viability of the proposed paradigm. Outperforming some current works based on mammogram images, the experimental accuracy, precision, sensitivity, specificity, and f1-scores reached 97.99%, 99.51%, 98.43%, 80.08%, and 98.97%, respectively, on the huge dataset of mammography images categorized as benign and malignant, respectively. Incorporating Fog computing technologies, this model safeguards the privacy and security of patient data, reduces the load on centralized servers, and increases the output.

Deep Learning in Different Ultrasound Methods for Breast Cancer, from Diagnosis to Prognosis: Current Trends, Challenges, and an Analysis

Breast cancer is the second-leading cause of mortality among women around the world. Ultrasound (US) is one of the noninvasive imaging modalities used to diagnose breast lesions and monitor the prognosis of cancer patients. It has the highest sensitivity for diagnosing breast masses, but it shows increased false negativity due to its high operator dependency. Underserved areas do not have sufficient US expertise to diagnose breast lesions, resulting in delayed management of breast lesions. Deep learning neural networks may have the potential to facilitate early decision-making by physicians by rapidly yet accurately diagnosing and monitoring their prognosis. This article reviews the recent research trends on neural networks for breast mass ultrasound, including and beyond diagnosis. We discussed original research recently conducted to analyze which modes of ultrasound and which models have been used for which purposes, and where they show the best performance. Our analysis reveals that lesion classification showed the highest performance compared to those used for other purposes. We also found that fewer studies were performed for prognosis than diagnosis. We also discussed the limitations and future directions of ongoing research on neural networks for breast ultrasound.

A Novel NanoMIP-SPR Sensor for the Point-of-Care Diagnosis of Breast Cancer

Simple, fast, selective, and reliable detection of human epidermal growth factor receptor 2 (HER2) is of utmost importance in the early diagnosis of breast cancer to prevent its high prevalence and mortality. Molecularly imprinted polymers (MIPs), also known as artificial antibodies, have recently been used as a specific tool in cancer diagnosis and therapy. In this study, a miniaturized surface plasmon resonance (SPR)-based sensor was developed using epitope-mediated HER2-nanoMIPs. The nanoMIP receptors were characterized using dynamic light scattering (DLS), zeta potential, Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), and fluorescent microscopy. The average size of the nanoMIPs was determined to be 67.5 ± 12.5 nm. The proposed novel SPR sensor provided superior selectivity to HER2 with a detection limit (LOD) of 11.6 pg mL^-1 in human serum. The high specificity of the sensor was confirmed by cross-reactivity studies using P53, human serum albumin (HSA), transferrin, and glucose. The sensor preparation steps were successfully characterized by employing cyclic and square wave voltammetry. The nanoMIP-SPR sensor demonstrates great potential for use in the early diagnosis of breast cancer as a robust tool with high sensitivity, selectivity, and specificity.

Design of a Low-Cost Diffuse Optical Mammography System for Biomedical Image Processing in Breast Cancer Diagnosis

Worldwide, breast cancer is the most common type of cancer that mainly affects women. Several diagnosis techniques based on optical instrumentation and image analysis have been developed, and these are commonly used in conjunction with conventional diagnostic devices such as mammographs, ultrasound, and magnetic resonance imaging of the breast. The cost of using these instruments is increasing, and developing countries, whose deaths indices due to breast cancer are high, cannot access conventional diagnostic methods and have even less access to newer techniques. Other studies, based on the analysis of images acquired by traditional methods, require high resolutions and knowledge of the origin of the captures in order to avoid errors. For this reason, the design of a low-cost diffuse optical mammography system for biomedical image processing in breast cancer diagnosis is presented. The system combines the acquisition of breast tissue photographs, diffuse optical reflectance (as a biophotonics technique), and the processing of digital images for the study and diagnosis of breast cancer. The system was developed in the form of a medical examination table with a 638 nm red-light source, using light-emitted diode technology (LED) and a low-cost web camera for the acquisition of breast tissue images. The system is automatic, and its control, through a graphical user interface (GUI), saves costs and allows for the subsequent analysis of images using a digital image-processing algorithm. The results obtained allow for the possibility of planning in vivo measurements. In addition, the acquisition of images every 30° around the breast tissue could be used in future research in order to perform a three-dimensional (3D) reconstruction and an analysis of the captures through deep learning techniques. These could be combined with virtual, augmented, or mixed reality environments to predict the position of tumors, increase the likelihood of a correct medical diagnosis, and develop a training system for specialists. Furthermore, the system allows for the possibility to develop analysis of optical characterization for new phantom studies in breast cancer diagnosis through bioimaging techniques.

A Comprehensive Review on Electrochemical Nano Biosensors for Precise Detection of Blood-Based Oncomarkers in Breast Cancer

Breast cancer (BC), one of the most common and life-threatening cancers, has the highest incidence rate among women. Early diagnosis of BC oncomarkers is considered the most effective strategy for detecting and treating BC. Finding the type and stage of BC in women as soon as possible is one of the greatest ways to stop its incidence and negative effects on medical treatment. The development of biosensors for early, sensitive, and selective detection of oncomarkers has recently attracted much attention. An electrochemical nano biosensor (EN) is a very suitable option for a powerful tool for cancer diagnosis. This comprehensive review provides information about the prevalence and pathobiology of BC, recent advances in clinically available BC oncomarkers, and the most common electrochemical nano biosensors for point-of-care (POC) detection of various BC oncomarkers using nanomaterial-based signal amplification techniques.

Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition

This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast cancer classification and by inferring their affected location. For this goal, the image recognition application strategies for the maximal diagnosis accuracy in each medical image data are investigated in terms of various image classification (VGGNet19, ResNet50, DenseNet121, EfficietNet v2), image segmentation (UNet, ResUNet++, DeepLab v3), and related loss functions (binary cross entropy, dice Loss, Tversky loss), and data augmentation. As a result of evaluations through the presented methods, when using filter-based data augmentation, ResNet50 showed the best performance in image classification, and UNet showed the best performance in both X-ray image and ultrasound image as image segmentation. When applying the proposed image recognition strategies for the maximal diagnosis accuracy in each medical image data, the accuracy can be improved by 33.3% in image segmentation in X-ray images, 29.9% in image segmentation in ultrasound images, and 22.8% in image classification in histopathology images.

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.

Diagnosis of architectural distortion on digital breast tomosynthesis using radiomics and deep learning

Purpose

To implement two Artificial Intelligence (AI) methods, radiomics and deep learning, to build diagnostic models for patients presenting with architectural distortion on Digital Breast Tomosynthesis (DBT) images.

Materials and Methods

A total of 298 patients were identified from a retrospective review, and all of them had confirmed pathological diagnoses, 175 malignant and 123 benign. The BI-RADS scores of DBT were obtained from the radiology reports, classified into 2, 3, 4A, 4B, 4C, and 5. The architectural distortion areas on craniocaudal (CC) and mediolateral oblique (MLO) views were manually outlined as the region of interest (ROI) for the radiomics analysis. Features were extracted using PyRadiomics, and then the support vector machine (SVM) was applied to select important features and build the classification model. Deep learning was performed using the ResNet50 algorithm, with the binary output of malignancy and benignity. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was utilized to localize the suspicious areas. The predicted malignancy probability was used to construct the ROC curves, compared by the DeLong test. The binary diagnosis was made using the threshold of ≥ 0.5 as malignant.

Results

The majority of malignant lesions had BI-RADS scores of 4B, 4C, and 5 (148/175 = 84.6%). In the benign group, a substantial number of patients also had high BI-RADS ≥ 4B (56/123 = 45.5%), and the majority had BI-RADS ≥ 4A (102/123 = 82.9%). The radiomics model built using the combined CC+MLO features yielded an area under curve (AUC) of 0.82, the sensitivity of 0.78, specificity of 0.68, and accuracy of 0.74. If only features from CC were used, the AUC was 0.77, and if only features from MLO were used, the AUC was 0.72. The deep-learning model yielded an AUC of 0.61, significantly lower than all radiomics models (p<0.01), which was presumably due to the use of the entire image as input. The Grad-CAM could localize the architectural distortion areas.

Conclusion

The radiomics model can achieve a satisfactory diagnostic accuracy, and the high specificity in the benign group can be used to avoid unnecessary biopsies. Deep learning can be used to localize the architectural distortion areas, which may provide an automatic method for ROI delineation to facilitate the development of a fully-automatic computer-aided diagnosis system using combined AI strategies.

Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature's data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier's efficiency and training time. The models' diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

Feature Selection Using Correlation Analysis and Principal Component Analysis for Accurate Breast Cancer Diagnosis

Breast cancer is one of the leading causes of death among women, more so than all other cancers. The accurate diagnosis of breast cancer is very difficult due to the complexity of the disease, changing treatment procedures and different patient population samples. Diagnostic techniques with better performance are very important for personalized care and treatment and to reduce and control the recurrence of cancer. The main objective of this research was to select feature selection techniques using correlation analysis and variance of input features before passing these significant features to a classification method. We used an ensemble method to improve the classification of breast cancer. The proposed approach was evaluated using the public WBCD dataset (Wisconsin Breast Cancer Dataset). Correlation analysis and principal component analysis were used for dimensionality reduction. Performance was evaluated for well-known machine learning classifiers, and the best seven classifiers were chosen for the next step. Hyper-parameter tuning was performed to improve the performances of the classifiers. The best performing classification algorithms were combined with two different voting techniques. Hard voting predicts the class that gets the majority vote, whereas soft voting predicts the class based on highest probability. The proposed approach performed better than state-of-the-art work, achieving an accuracy of 98.24%, high precision (99.29%) and a recall value of 95.89%.

[99mTc]Tc-DB15 in GRPR-Targeted Tumor Imaging with SPECT: From Preclinical Evaluation to the First Clinical Outcomes

Diagnostic imaging and radionuclide therapy of prostate (PC) and breast cancer (BC) using radiolabeled gastrin-releasing peptide receptor (GRPR)-antagonists represents a promising approach. We herein propose the GRPR-antagonist based radiotracer [^99mTc]Tc-DB15 ([^99mTc]Tc-N₄-AMA-DGA-_DPhe⁶,Sar¹¹,LeuNHEt¹³]BBN(6-13); N₄: 6-carboxy-1,4,8,11-tetraazaundecane, AMA: aminomethyl-aniline, DGA: diglycolic acid) as a new diagnostic tool for GRPR-positive tumors applying SPECT/CT. The uptake of [^99mTc]Tc-DB15 was tested in vitro in mammary (T-47D) and prostate cancer (PC-3) cells and in vivo in T-47D or PC-3 xenograft-bearing mice as well as in BC patients. DB15 showed high GRPR-affinity (IC₅₀ = 0.37 ± 0.03 nM) and [^99mTc]Tc-DB15 strongly bound to the cell-membrane of T-47D and PC-3 cells, according to a radiolabeled antagonist profile. In mice, the radiotracer showed high and prolonged GRPR-specific uptake in PC-3 (e.g., 25.56 ± 2.78 %IA/g vs. 0.72 ± 0.12 %IA/g in block; 4 h pi) and T-47D (e.g., 15.82 ± 3.20 %IA/g vs. 3.82 ± 0.30 %IA/g in block; 4 h pi) tumors, while rapidly clearing from background. In patients with advanced BC, the tracer could reveal several bone and soft tissue metastases on SPECT/CT. The attractive pharmacokinetic profile of [^99mTc]DB15 in mice and its capability to target GRPR-positive BC lesions in patients highlight its prospects for a broader clinical use, an option currently being explored by ongoing clinical studies.

Effectiveness of ADC Difference Value on Pre-neoadjuvant Chemotherapy MRI for Response Evaluation of Breast Cancer

Background: Neoadjuvant chemotherapy (NAC) is known to be a suitable treatment and first-line defense for locally advanced breast cancer. However, the NAC response may include unexpected outcomes, and it is not easy to predict the NAC response precisely. Especially, early detection of those patients who do not benefit from NAC is needed to reduce unnecessary therapy and side effects. Objective: The purpose of this study was to determine whether the pretreatment apparent diffusion coefficient (ADC) value is effective for predicting the response of breast cancer to NAC. Method: Forty-nine breast cancer cases with pre- and post-NAC breast MRI were enrolled. MRI was performed using a 1.5-T scanner with the basic protocol including diffusion-weighted imaging. ADC difference value (ADC-diff) was calculated in all cases. Results: ADC-diff was high in complete response and partial response cases (p < .05). ADC-diff correlated with the DWI rim sign, with a positive DWI rim sign being associated with a higher ADC-diff (p < .05). Conclusion: High-ADC difference value on the pretreatment MRI can provide information for a better response of NAC on breast cancer.

Effect of x-ray energy on the radiological image quality in propagation-based phase-contrast computed tomography of the breast

Purpose: Breast cancer is the most common cancer in women in developing and developed countries and is responsible for 15% of women's cancer deaths worldwide. Conventional absorption-based breast imaging techniques lack sufficient contrast for comprehensive diagnosis. Propagation-based phase-contrast computed tomography (PB-CT) is a developing technique that exploits a more contrast-sensitive property of x-rays: x-ray refraction. X-ray absorption, refraction, and contrast-to-noise in the corresponding images depend on the x-ray energy used, for the same/fixed radiation dose. The aim of this paper is to explore the relationship between x-ray energy and radiological image quality in PB-CT imaging. Approach: Thirty-nine mastectomy samples were scanned at the imaging and medical beamline at the Australian Synchrotron. Samples were scanned at various x-ray energies of 26, 28, 30, 32, 34, and 60 keV using a Hamamatsu Flat Panel detector at the same object-to-detector distance of 6 m and mean glandular dose of 4 mGy. A total of 132 image sets were produced for analysis. Seven observers rated PB-CT images against absorption-based CT (AB-CT) images of the same samples on a five-point scale. A visual grading characteristics (VGC) study was used to determine the difference in image quality. Results: PB-CT images produced at 28, 30, 32, and 34 keV x-ray energies demonstrated statistically significant higher image quality than reference AB-CT images. The optimum x-ray energy, 30 keV, displayed the largest area under the curve ( AUC VGC ) of 0.754 ( p = 0.009 ). This was followed by 32 keV ( AUC VGC = 0.731 , p ≤ 0.001 ), 34 keV ( AUC VGC = 0.723 , p ≤ 0.001 ), and 28 keV ( AUC VGC = 0.654 , p = 0.015 ). Conclusions: An optimum energy range (around 30 keV) in the PB-CT technique allows for higher image quality at a dose comparable to conventional mammographic techniques. This results in improved radiological image quality compared with conventional techniques, which may ultimately lead to higher diagnostic efficacy and a reduction in breast cancer mortalities.

Diffusivity in breast malignancies analyzed for b > 1000 s/mm2 at 1 mm in-plane resolutions: Insight from Gaussian and non-Gaussian behaviors

Diffusion-weighted imaging (DWI) can improve breast cancer characterizations, but often suffers from low image quality -particularly at informative b > 1000 s/mm² values. The aim of this study was to evaluate multishot approaches characterizing Gaussian and non-Gaussian diffusivities in breast cancer. This was a prospective study, in which 15 subjects, including 13 patients with biopsy-confirmed breast cancers, were enrolled. DWI was acquired at 3 T using echo planar imaging (EPI) with and without zoomed excitations, readout-segmented EPI (RESOLVE), and spatiotemporal encoding (SPEN); dynamic contrast-enhanced (DCE) data were collected using three-dimensional gradient-echo T₁ weighting; anatomies were evaluated with T₂ -weighted two-dimensional turbo spin-echo. Congruence between malignancies delineated by DCE was assessed against high-resolution DWI scans with b-values in the 0-1800 s/mm² range, as well as against apparent diffusion coefficient (ADC) and kurtosis maps. Data were evaluated by independent magnetic resonance scientists with 3-20 years of experience, and radiologists with 6 and 20 years of experience in breast MRI. Malignancies were assessed from ADC and kurtosis maps, using paired t tests after confirming that these values had a Gaussian distribution. Agreements between DWI and DCE datasets were also evaluated using Sorensen-Dice similarity coefficients. Cancerous and normal tissues were clearly separable by ADCs: by SPEN their average values were (1.03 ± 0.17) × 10^-3 and (1.69 ± 0.19) × 10^-3  mm² /s (p < 0.0001); by RESOLVE these values were (1.16 ± 0.16) × 10^-3 and (1.52 ± 0.14) × 10^-3 (p = 0.00026). Kurtosis also distinguished lesions (K = 0.64 ± 0.15) from normal tissues (K = 0.45 ± 0.05), but only when measured by SPEN (p = 0.0008). The best statistical agreement with DCE-highlighted regions arose for SPEN-based DWIs recorded with b = 1800 s/mm² (Sorensen-Dice coefficient = 0.67); DWI data recorded with b = 850 and 1200 s/mm² , led to lower coefficients. Both ADC and kurtosis maps highlighted the breast malignancies, with ADCs providing a more significant separation. The most promising alternative for contrast-free delineations of the cancerous lesions arose from b = 1800 s/mm² DWI. LEVEL OF EVIDENCE: 2. TECHNICAL EFFICACY STAGE: 3.

Decision Support for Breast Cancer Detection: Classification Improvement Through Feature Selection

Several statistical-based approaches have been developed to support medical personnel in early breast cancer detection. This article presents a method for feature selection aimed at classifying cases into categories based on patients' breast tissue measures and protein microarray. The effectiveness of this feature selection strategy was evaluated against the commonly used Wisconsin Breast Cancer Database-WBCD (with several patients and fewer features) and a new protein microarray data set (with several features and fewer patients). Features were ranked according to a feature importance index that combines parameters emerging from the unsupervised method of principal component analysis and the supervised method of Bhattacharyya distance. Observations of a training set were iteratively categorized into malignant and benign cases through 3 classification techniques: k-Nearest Neighbor, linear discriminant analysis, and probabilistic neural network. After each classification, the feature with the smallest importance index was removed, and a new categorization was carried out until there was only one feature left. The subset yielding maximum accuracy was used to classify observations in the testing set. Our method yielded average 99.17% accurate classifications in the testing set while retaining average 4.61 out of 9 features in the WBCD, which is comparable to the best results reported by the literature on that data set, with the advantage of relying on simple and widely available multivariate techniques. When applied to the microarray data, the method yielded average accuracy of 98.30% while retaining average 2.17% of the original features. Our results can aid health-care professionals during early diagnosis of breast cancer.

Opto-acoustic imaging of relative blood oxygen saturation and total hemoglobin for breast cancer diagnosis

Opto-acoustic imaging involves using light to produce sound waves for visualizing blood in biological tissue. By using multiple optical wavelengths, diagnostic images of blood oxygen saturation and total hemoglobin are generated using endogenous optical contrast, without injection of any external contrast agent and without using any ionizing radiation. The technology has been used in recent clinical studies for diagnosis of breast cancer to help distinguish benign from malignant lesions, potentially reducing the need for biopsy through improved diagnostic imaging accuracy. To enable this application, techniques for mapping oxygen saturation differences within tissue are necessary. Using biologically relevant opto-acoustic phantoms, we analyze the ability of an opto-acoustic imaging system to display colorized parametric maps that are generated using a statistical mapping approach. To mimic breast tissue, a material with closely matching properties for optical absorption, optical scattering, acoustic attenuation, and speed of sound is used. The phantoms include two vessels filled with whole blood at oxygen saturation levels determined using a sensor-based approach. A flow system with gas-mixer and membrane oxygenator adjusts the oxygen saturation of each vessel independently. Datasets are collected with an investigational Imagio^® breast imaging system. We examine the ability to distinguish vessels as the oxygen saturation level and imaging depth are varied. At depth of 15 mm and hematocrit of 42%, a sufficient level of contrast to distinguish between two 1.6-mm diameter vessels was measured for an oxygen saturation difference of ∼4.6  %  . In addition, an oxygenated vessel was visible at a depth of 48 mm using an optical wavelength of 1064 nm, and a deoxygenated vessel was visible to a depth of 42 mm with 757 nm. The results provide insight toward using color mapped opto-acoustic images for diagnosing breast cancer.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue

BACKGROUND

Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported.

PURPOSE

To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration.

STUDY TYPE

Retrospective.

POPULATION

In all, 133 patients with histologically confirmed 91 malignant and 62 benign mass lesions for training (74 patients with 48 malignant and 26 benign lesions for testing).

FIELD STRENGTH/SEQUENCE

3T, using the volume imaging for breast assessment (VIBRANT) dynamic contrast-enhanced (DCE) sequence.

ASSESSMENT

3D tumor segmentation was done automatically by using fuzzy-C-means algorithm with connected-component labeling. A total of 99 texture and histogram parameters were calculated for each case, and 15 were selected using random forest to build a radiomics model. Deep learning was implemented using ResNet50, evaluated with 10-fold crossvalidation. The tumor alone, smallest bounding box, and 1.2, 1.5, 2.0 times enlarged boxes were used as inputs.

STATISTICAL TESTS

The malignancy probability was calculated using each model, and the threshold of 0.5 was used to make a diagnosis.

RESULTS

In the training dataset, the diagnostic accuracy was 76% using three ROI-based parameters, 84% using the radiomics model, and 86% using ROI + radiomics model. In deep learning using the per-slice basis, the area under the receiver operating characteristic (ROC) was comparable for tumor alone, smallest and 1.2 times box (AUC = 0.97-0.99), which were significantly higher than 1.5 and 2.0 times box (AUC = 0.86 and 0.71, respectively). For per-lesion diagnosis, the highest accuracy of 91% was achieved when using the smallest bounding box, and that decreased to 84% for tumor alone and 1.2 times box, and further to 73% for 1.5 times box and 69% for 2.0 times box. In the independent testing dataset, the per-lesion diagnostic accuracy was also the highest when using the smallest bounding box, 89%.

DATA CONCLUSION

Deep learning using ResNet50 achieved a high diagnostic accuracy. Using the smallest bounding box containing proximal peritumor tissue as input had higher accuracy compared to using tumor alone or larger boxes.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2.

Reducing image artifact in diffuse optical tomography by iterative perturbation correction based on multiwavelength measurements

Ultrasound (US) guided diffuse optical tomography has demonstrated great potential for breast cancer diagnosis, treatment monitoring, and chemotherapy response prediction. Optical measurements of four different wavelengths are used to reconstruct unknown optical absorption maps, which are then used to calculate the hemoglobin concentration distribution of the US visible lesion. Reconstructed absorption maps are prone to image artifacts from outliers in measurement data from tissue heterogeneity, bad coupling between tissue and light guides, and motion by patient or operator. We propose an automated iterative perturbation correction algorithm to reduce image artifacts based on the structural similarity index (SSIM) of absorption maps of four optical wavelengths. The initial image is estimated from the truncated pseudoinverse solution. The SSIM was calculated for each wavelength to assess its similarity with other wavelengths. An absorption map is repeatedly reconstructed and projected back into measurement space to quantify projection error. Outlier measurements with highest projection errors are iteratively removed until all wavelength images are structurally similar with SSIM values greater than a threshold. Clinical data demonstrate statistically significant improvement in image artifact reduction.

Overexpression of TBX3 transcription factor as a potential diagnostic marker for breast cancer

The T-box 3 (TBX3) transcription factor has been shown to serve multiple roles in normal development. Recent findings have revealed that TBX3 is overexpressed in different types of carcinomas, including breast, cervical, ovarian, melanoma, pancreatic, lung, liver, bladder, head and neck. Therefore, the present study investigated the significance of TBX3 as a diagnostic marker of breast cancer. To achieve this aim, breast cancer samples and their adjacent normal tissues were collected from 51 breast cancer patients from the European Gaza hospital during 2015-2016. Sections from each sample were immune-stained by anti-TBX3 and suitable secondary and tertiary antibodies. TBX3 levels were evaluated in cancerous and normal samples. Clinicopathological data for each patient were documented. The correlation between TBX3 levels and the clinicopathological parameters were statistically tested. The results revealed that TBX3 is significantly overexpressed in breast cancer tissues when compared with normal tissues. Furthermore, TBX3 was mainly a cytoplasmic protein in normal and breast cancer tissues. Notably, TBX3 levels exhibited a sensitivity of 78.4%, specificity of 79.6%, accuracy of 79% and area under the curve of 0.791 (0.700-0.882) at a cut-off value=9 as breast cancer marker. However, no significant associations were observed between TBX3 levels and other breast cancer markers including oestrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, cancer antigen 15-3 and breast cancer stages. Altogether, these results suggested that TBX3 overexpression may be a potential biomarker for breast cancer.

Evaluation of a novel monoclonal antibody against tumor-associated MUC1 for diagnosis and prognosis of breast cancer

There is still a great unmet medical need concerning diagnosis and treatment of breast cancer which could be addressed by utilizing specific molecular targets. Tumor-associated MUC1 is expressed on over 90 % of all breast cancer entities and differs strongly from its physiological form on epithelial cells, therefore presenting a unique target for breast cancer diagnosis and antibody-mediated immune therapy. Utilizing an anti-tumor vaccine based on a synthetically prepared glycopeptide, we generated a monoclonal antibody (mAb) GGSK-1/30, selectively recognizing human tumor-associated MUC1. This antibody targets exclusively tumor-associated MUC1 in the absence of any binding to MUC1 on healthy epithelial cells thus enabling the generation of breast tumor-specific radiolabeled immune therapeutic tools. Methods: MAb GGSK-1/30 was used for immunohistochemical analysis of human breast cancer tissue. Its desferrioxamine (Df')-conjugate was synthesized and labelled with ⁸⁹Zr. [⁸⁹Zr]Zr-Df'-GGSK-1/30 was evaluated as a potential PET tracer. Binding and pharmacokinetic properties of [⁸⁹Zr]Zr-Df'-GGSK-1/30 were analyzed in vitro using human and murine cell lines that express tumor-associated MUC1. Self-generated primary murine breast cancer cells expressing human tumor-associated MUC1 were transplanted subcutaneously in wild type and human MUC1-transgenic mice. The pharmacology of [⁸⁹Zr]Zr-Df'-GGSK-1/30 was investigated using breast tumor-bearing mice in vivo by PET/MRT imaging as well as by ex vivo organ biodistribution analysis. Results: The mAb GGSK-1/30 stained specifically human breast tumor tissue and can be possibly used to predict the severity of disease progression based on the expression of the tumor-associated MUC1. For in vivo imaging, the Df'-conjugated mAb was radiolabeled with a radiochemical yield of 60 %, a radiochemical purity of 95 % and an apparent specific activity of 6.1 GBq/µmol. After 7 d, stabilities of 84 % in human serum and of 93 % in saline were observed. In vitro cell studies showed strong binding to human tumor-associated MUC1 expressing breast cancer cells. The breast tumor-bearing mice showed an in vivo tumor uptake of >50 %ID/g and clearly visible specific enrichment of the radioconjugate via PET/MRT. Principal conclusions: Tumor-associated MUC1 is a very important biomarker for breast cancer next to the traditional markers estrogen receptor (ER), progesterone receptor (PR) and HER/2-neu. The mAb GGSK-1/30 can be used for the diagnosis of over 90% of breast cancers, including triple negative breast cancer based on biopsy staining. Its radioimmunoconjugate represents a promising PET-tracer for breast cancer imaging selectively targeting breast cancer cells.

Emotional "Patient-Oriented" Support in Young Patients With I-II Stage Breast Cancer: Pilot Study

Objective: The recent increased survival rate after breast cancer (BC) diagnosis and treatment is mostly related to early screening in younger age. Evidence gained from newly detected assessed psychological needs as well as certain emotional regulatory patterns in younger survivors has been related in the literature to an extremely low rate of adherence to the psychological therapies offered. Tailored psychological support is necessary. The aim of the present study was to verify the preliminary efficacy of supportive psychological intervention with an innovative orientation: the Early BC Psychological Intervention (EBC-Psy).

Methods: A controlled study design was used to investigate the efficacy of EBC-Psy intervention. Preliminary data involved twenty-four patients in the age range of 35–50 years, diagnosed with cancer at the early stage (I–II), who were exposed to the EBC-Psy intervention. To address the effect of intervention, emotional variables were tested before the treatment (Time 1) and then again after 6 months of the treatment (Time 2); evaluated emotional dimensions were anxiety, anger, depression, and psychological distress.

Results: EBC-Psy intervention appears to be effective on both depression (p = 0.02) and psychological distress (p = 0.01), even in a short time, highlighting the strength of a reinforced positive psychological conceptual approach to deal with the “disease condition” in younger patients; on the contrary, the control group evidenced an increase in the same emotional variables in timing.

Conclusion: Our findings, even if limited by this small-scale protocol, seemed to confirm the role of positive psychotherapy after BC diagnosis and treatment through the impact of cognitive processes, coping strategies, and psychological resilience. Future theoretical framework could boost the intervention to design an innovative survivorship model.

Feasibility and Diagnostic Performance of Voxelwise Computed Diffusion-Weighted Imaging in Breast Cancer

BACKGROUND

Conventional diffusion-weighted imaging (DWI) with high b-values may improve lesion conspicuity, but with a low signal intensity and thus a low signal-to-noise ratio (SNR). The voxelwise computed DWI (vcDWI) may generate high-quality images with a strong lesion signal and low background.

PURPOSE

To evaluate the feasibility and diagnostic performance of vcDWI.

STUDY TYPE

Retrospective.

POPULATION

In all, 67 patients with 72 lesions, 33 malignant and 39 benign.

FIELD STRENGTH/SEQUENCE

3T, including T₂ /T₁ , DWI with two b-values, and dynamic contrast-enhanced MRI (DCE-MRI).

ASSESSMENT

Computed DWI (cDWI) with high b-values of 1500, 2000, 2500 s/mm² (cDWI₁₅₀₀ , cDWI₂₀₀₀ , cDWI₂₅₀₀ ) and vcDWI were generated from measured DWI (mDWI). The mDWI, cDWIs and vcDWI were evaluated by three readers independently to determine lesion conspicuity, background signal suppression, overall image quality using 1-5 rating scales, as well as to give BI-RADS scores. The mean apparent diffusion coefficient (ADC) value for each lesion was measured.

STATISTICAL TESTS

Agreement among the three readers was evaluated by the intraclass correlation coefficient. Receiver operating characteristic (ROC) analysis was performed to compare the diagnostic performance based on reading of mDWI, cDWIs, vcDWI, and the measured ADC values.

RESULTS

vcDWI provided the best lesion conspicuity compared with mDWI and cDWIs (P < 0.005). For overall image quality, vcDWI was significantly better than cDWI (P < 0.005), but not significantly better compared with mDWI for two readers (P = 0.037 and P = 0.013) and significantly worse for the third reader (P < 0.005). Background signal suppression was the best on cDWI₂₅₀₀ , and better on vcDWI than on mDWI, cDWI₁₅₀₀ , and cDWI₂₀₀₀ . The AUC value for differential diagnosis was 0.868 for mDWI, 0.862 for cDWI₁₅₀₀ , 0.781 for cDWI₂₀₀₀ , 0.704 for cDWI₂₅₀₀ , 0.946 for vcDWI, 0.704 for ADC value, and 0.961 for DCE-MRI. DATA CONCLUSION: vcDWI was implemented without increasing scanning time, and it provided excellent lesion conspicuity for detection of breast lesions and assisted in differentiating malignant from benign breast lesions.

LEVEL OF EVIDENCE

4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

On-Site Validation of a Microwave Breast Imaging System, before First Patient Study

This paper presents the Wavelia microwave breast imaging system that has been recently installed at the Galway University Hospital, Ireland, for a first-in-human pilot clinical test. Microwave breast imaging has been extensively investigated over the last two decades as an alternative imaging modality that could potentially bring complementary information to state-of-the-art modalities such as X-ray mammography. Following an overview of the main working principles of this technology, the Wavelia imaging system architecture is presented, as are the radar signal processing algorithms that are used in forming the microwave images in which small tumors could be detectable for disease diagnosis. The methodology and specific quality metrics that have been developed to properly evaluate and validate the performance of the imaging system using complex breast phantoms that are scanned at controlled measurement conditions are also presented in the paper. Indicative results from the application of this methodology to the on-site validation of the imaging system after its installation at the hospital for pilot clinical testing are thoroughly presented and discussed. Given that the imaging system is still at the prototype level of development, a rigorous quality assessment and system validation at nominal operating conditions is very important in order to ensure high-quality clinical data collection.

A fine-needle aspiration-based protein signature discriminates benign from malignant breast lesions

There are increasing demands for informative cancer biomarkers, accessible via minimally invasive procedures, both for initial diagnostics and to follow-up personalized cancer therapy. Fine-needle aspiration (FNA) biopsy provides ready access to relevant tissues; however, the minute sample amounts require sensitive multiplex molecular analysis to achieve clinical utility. We have applied proximity extension assays (PEA) and NanoString (NS) technology for analyses of proteins and of RNA, respectively, in FNA samples. Using samples from patients with breast cancer (BC, n = 25) or benign lesions (n = 33), we demonstrate that these FNA-based molecular analyses (a) can offer high sensitivity and reproducibility, (b) may provide correct diagnosis in shorter time and at a lower cost than current practice, (c) correlate with results from routine analysis (i.e., benchmarking against immunohistochemistry tests for ER, PR, HER2, and Ki67), and (d) may also help identify new markers related to immunotherapy. A specific 11-protein signature, including FGF binding protein 1, decorin, and furin, distinguished all cancer patient samples from all benign lesions in our main cohort and in smaller replication cohort. Due to the minimally traumatic sampling and rich molecular information, this combined proteomics and transcriptomic methodology is promising for diagnostics and evaluation of treatment efficacy in BC.

Breast Cancer During Pregnancy

Breast cancer remains the most common cancer in women. A diagnosis of cancer during pregnancy is uncommon. In recent decades, obstetricians are seeing an increasing number of women who become pregnant or desire to become pregnant after breast cancer treatment because of a delay in childbearing for a variety of reasons, including cultural, educational, and professional. Consequently, breast cancer in young women often occurs before the completion of reproductive plans. A discussion among the patient, the oncologist, and the obstetrician on the relative benefits of early delivery followed by treatment versus commencement of therapy while continuing the pregnancy is of utmost importance in order to reach a consensual decision. The best available evidence suggests that pregnancy after breast cancer increases the risk of recurrence. The birth outcome in women with a history of breast cancer is no different from that in the normal female population; however, increased risks of delivery complications have been reported in the literature. As concurrent pregnancy and breast cancer are uncommon, there are no data from large randomized trials; hence, recommendations are mainly based on retrospective studies.

Quantifying rater variation for ordinal data using a rating scale model

We present a model-based approach to the analysis of agreement between different raters in a situation where all raters have supplied ordinal ratings of the same cases in a sample. It is assumed that no "gold standard" is available. The model is an ordinal regression model with random effects-a so-called rating scale model. The model includes case-specific parameters that allow each case his or hers own level (disease severity). It also allows raters to have different propensities to score a given set of individuals more or less positively-the rater level. Based on the model, we suggest quantifying the rater variation using the median odds ratio. This allows expressing the variation on the same scale as the observed ordinal data. An important example that will serve to motivate and illustrate the proposed model is the study of breast cancer diagnosis based on screening mammograms. The purpose of the assessment is to detect early breast cancer in order to obtain improved cancer survival. In the study, mammograms from 148 women were evaluated by 110 expert radiologists. The experts were asked to rate each mammogram on a 5-point scale ranging from "normal" to "probably malignant."

Performance evaluation of breast cancer diagnosis with mammography, ultrasonography and magnetic resonance imaging

OBJECTIVE

Various imaging modalities have been used to diagnose suspicious breast lesions. Purpose of this study is to compare the diagnostic accuracy for breast cancer using mammography, ultrasonography and magnetic resonance imaging (MRI).

METHODS

Total 107 patients aged from 19 to 62 years are included in this retrospective study. Mammography, ultrasonography and MRI scans were performed for each patient detected with suspected breast tumor within a month. In addition, the tumor diversity (10 types of benign and 5 types of malignant) was confirmed by pathological findings of tumor biopsy. To compare the diagnosis performance of the three imaging modalities, the overall fraction correct (accuracy), positive predict value (PPV), negative predict value (NPV), sensitivity and specificity were calculated. Meanwhile, the receiver operating characteristic (ROC) analysis was also performed.

RESULTS

The diagnostic accuracy ranged from 78.5% to 86.9% among three imaging modalities. All modalities yielded a PPV lower than 77.8% and a NPV higher than 90.0% in identifying the presence of malignant tumors. MRI presented a diagnostic accuracy of 86.9%, as well as a sensitivity of 95.5% and an area under curve (AUC) of 0.948, which are higher than mammography and ultrasonography.

CONCLUSION

By using a diverse dataset and comparing the diagnostic accuracy of three imaging modalities commonly used in breast cancer detection and diagnosis, this study also demonstrated that mammography, ultrasonography and MRI had different diagnostic performance in breast tumor identification. Among them, MRI yielded the highest performance even though the unexpected specificity may lead to over-diagnosis, and ultrosonography is slightly better than mammography.

Reduced Time to Breast Cancer Diagnosis with Coordination of Radiological and Clinical Care

Introduction Diagnostic delays for breast problems is a current concern in British Columbia and diagnostic pathways for breast cancer are currently under review. Breast centres have been introduced in Europe and reported to facilitate diagnosis and treatment. Guidelines for breast centers are outlined by the European Society for Mastology (EUSOMA). A Rapid Access Breast Clinic (RABC) was developed at our hospital applying the concept of triple evaluation for all patients and navigation between clinicians and radiologists. We hypothesize that the Rapid Access Breast Clinic will decrease wait times to diagnosis and minimize duplication of services compared to usual care. Methods A retrospective review was undertaken looking at diagnostic wait times and the number of diagnostic centres involved for consecutive patients seen by breast surgeons with diagnostic workups performed either in the traditional system (TS) or the RABC. Only patients presenting with a new breast problem were included in the study. Results Patients seen at the RABC had a decreased time to surgical consultation (33 vs 86 days, p<0.0001) for both malignant (36 vs 59 days, p=0.0007) and benign diagnoses (31 vs 95 days, p<0.0001). Furthermore, 13% of the patients referred to the surgeon in the TS without a diagnosis were eventually diagnosed with a malignancy and waited a mean of 84 days for initial surgical assessment. Of the patients seen at the RABC, 5% required investigation at more than one institution compared to 39% patients seen in the TS (p<0.0001). Cancer patients had a shorter time from presentation to surgery in the RABC (64 vs 92 days, p=0.009). Conclusion The establishment of the RABC has significantly reduced the time to surgical consultation, time to breast cancer surgery, and duplication of investigations for patients with benign and malignant breast complaints. It is feasible to introduce a EUSOMA-based breast clinic in the Canadian Health Care System and improvements in diagnostic wait times are seen. We recommend the expansion of coordinated care to other sites.

Radiology as the Point of Cancer Patient and Care Team Engagement: Applying the 4R Model at a Patient's Breast Cancer Care Initiation

Radiologists aspire to improve patient experience and engagement, as part of the Triple Aim of health reform. Patient engagement requires active partnerships among health providers and patients, and rigorous teamwork provides a mechanism for this. Patient and care team engagement are crucial at the time of cancer diagnosis and care initiation but are complicated by the necessity to orchestrate many interdependent consultations and care events in a short time. Radiology often serves as the patient entry point into the cancer care system, especially for breast cancer. It is uniquely positioned to play the value-adding role of facilitating patient and team engagement during cancer care initiation. The 4R approach (Right Information and Right Care to the Right Patient at the Right Time), previously proposed for optimizing teamwork and care delivery during cancer treatment, could be applied at the time of diagnosis. The 4R approach considers care for every patient with cancer as a project, using project management to plan and manage care interdependencies, assign clear responsibilities, and designate a quarterback function. The authors propose that radiology assume the quarterback function during breast cancer care initiation, developing the care initiation sequence, as a project care plan for newly diagnosed patients, and engaging patients and their care teams in timely, coordinated activities. After initial consultations and treatment plan development, the quarterback function is transitioned to surgery or medical oncology. This model provides radiologists with opportunities to offer value-added services and solidifies radiology's relevance in the evolving health care environment. To implement 4R at cancer care initiation, it will be necessary to change the radiology practice model to incorporate patient interaction and teamwork, develop 4R content and local adaption approaches, and enrich radiology training with relevant clinical knowledge, patient interaction competence, and teamwork skill set.

Improving breast cancer diagnosis by reducing chest wall effect in diffuse optical tomography

We have developed the ultrasound (US)-guided diffuse optical tomography technique to assist US diagnosis of breast cancer and to predict neoadjuvant chemotherapy response of patients with breast cancer. The technique was implemented using a hand-held hybrid probe consisting of a coregistered US transducer and optical source and detector fibers which couple the light illumination from laser diodes and photon detection to the photomultiplier tube detectors. With the US guidance, diffused light measurements were made at the breast lesion site and the normal contralateral reference site which was used to estimate the background tissue optical properties for imaging reconstruction. However, background optical properties were affected by the chest wall underneath the breast tissue. We have analyzed data from 297 female patients, and results have shown statistically significant correlation between the fitted optical properties ( ? a and ? s ? ) and the chest wall depth. After subtracting the background ? a at each wavelength, the difference of computed total hemoglobin (tHb) between malignant and benign lesion groups has improved. For early stage malignant lesions, the area-under-the-receiver operator characteristic curve (AUC) has improved from 88.5% to 91.5%. For all malignant lesions, the AUC has improved from 85.3% to 88.1%. Statistical test has revealed the significant difference of the AUC improvements after subtracting background tHb values.

Toward a Portable Cancer Diagnostic Tool Using a Disposable MEMS-Based Biochip

GOAL

The objective of this study is to design and develop a portable tool consisting of a disposable biochip for measuring electrothermomechanical (ETM) properties of breast tissue.

METHODS

A biochip integrated with a microheater, force sensors, and electrical sensors is fabricated using microtechnology. The sensor covers the area of 2 mm and the biochip is 10 mm in diameter. A portable tool capable of holding tissue and biochip is fabricated using 3-D printing. Invasive ductal carcinoma and normal tissue blocks are selected from cancer tissue bank in Biospecimen Repository Service at Rutgers Cancer Institute of New Jersey. The ETM properties of the normal and cancerous breast tissues (3-mm thickness and 2-mm diameter) are measured by indenting the tissue placed on the biochip integrated inside the 3-D printed tool.

RESULTS

Integrating microengineered biochip and 3-D printing, we have developed a portable cancer diagnosis device. Using this device, we have shown a statistically significant difference between cancerous and normal breast tissues in mechanical stiffness, electrical resistivity, and thermal conductivity.

CONCLUSION

The developed cancer diagnosis device is capable of simultaneous ETM measurements of breast tissue specimens and can be a potential candidate for delineating normal and cancerous breast tissue cores.

SIGNIFICANCE

The portable cancer diagnosis tool could potentially provide a deterministic and quantitative information about the breast tissue characteristics, as well as the onset and disease progression of the tissues. The tool can be potentially used for other tissue-related cancers.

Awaiting diagnosis of breast cancer: strategies of enduring for preserving self

PURPOSE/OBJECTIVES

To explicate the emotional experiences of women undergoing breast cancer diagnosis who are waiting for the results of breast biopsy.

RESEARCH APPROACH

Glaserian Grounded Theory.

SETTING

Urban area in western Canada.

PARTICIPANTS

37 women aged 32-76 years. The breast cancer diagnosis was positive for 11 women, negative for 24 women, and two results were unclear.

METHODOLOGIC APPROACH

Unstructured, recorded telephone interviews.

FINDINGS

Undergoing breast cancer diagnosis is a profoundly distressing experience dictated by diagnostic processes and procedures. Women rapidly transitioned from wellness to frightening phases of facing cancer to continuing terror during the testing phase. While waiting to hear results, women controlled their emotions, which enabled them to get through the experience and highlighted the protective function of enduring and its necessity for survival. The basic social psychological process, preserving self, is the outcome of enduring.

CONCLUSIONS

A mid-range theory, Awaiting Diagnosis: Enduring for Preserving Self, was developed. This theory explicates the emotional responses of women who were undergoing diagnosis for breast cancer and provides a theoretical behavioral basis for responding to cues and signals of suffering.

INTERPRETATION

The Praxis Theory of Suffering enables nurses to recognize and respond according to the behaviors of suffering, and to endure with healthy, adaptive, and normalizing behaviors that enable preserving self.

Trends in breast biopsy pathology diagnoses among women undergoing mammography in the United States: a report from the Breast Cancer Surveillance Consortium

BACKGROUND

Current data on the pathologic diagnoses of breast biopsy after mammography can inform patients, clinicians, and researchers about important population trends.

METHODS

Breast Cancer Surveillance Consortium data on 4,020,140 mammograms between 1996 and 2008 were linked to 76,567 pathology specimens. Trends in diagnoses in biopsies by time and risk factors (patient age, breast density, and family history of breast cancer) were examined for screening and diagnostic mammography (performed for a breast symptom or short-interval follow-up).

RESULTS

Of the total mammograms, 88.5% were screening and 11.5% diagnostic; 1.2% of screening and 6.8% of diagnostic mammograms were followed by biopsies. The frequency of biopsies over time was stable after screening mammograms, but increased after diagnostic mammograms. For biopsies obtained after screening, frequencies of invasive carcinoma increased over time for women ages 40-49 and 60-69, Ductal carcinoma in situ (DCIS) increased for those ages 40-69, whereas benign diagnoses decreased for all ages. No trends in pathology diagnoses were found following diagnostic mammograms. Dense breast tissue was associated with high-risk lesions and DCIS relative to nondense breast tissue. Family history of breast cancer was associated with DCIS and invasive cancer.

CONCLUSIONS

Although the frequency of breast biopsy after screening mammography has not changed over time, the percentages of biopsies with DCIS and invasive cancer diagnoses have increased. Among biopsies following mammography, women with dense breasts or family history of breast cancer were more likely to have high-risk lesions or invasive cancer. These findings are relevant to breast cancer screening and diagnostic practices.

When health means suffering: mammograms, pain and compassionate care

The X-ray mammogram remains the cornerstone of most public health programmes aimed at the early diagnosis of breast cancer. Its virtues of safety, reliability and cheapness maintain its established position, and Western social and cultural traditions of ambivalence to pain push any questions concerning the painfulness of the procedure into the background. As part of a larger UK/USA-based empirical study, we undertook a qualitative analysis of women's accounts of pain experienced in mammograms and their reaction to it, comparing their accounts with professional views and advice to patients as reflected in interviews, patient leaflets and practice guidelines. We found considerable variability of experience and reaction to pain among patients, and indications of similar variability in professionals' views and practice, contrasting with a uniformly reassuring message in formal institutional advice. We suggest that in practice professional work-arounds and patients' felt obligation to tolerate pain bridge this gap, but that action to tackle the problems of dropout and the emotional and operational costs of the current system is nonetheless needed. The need is for concerned groups to combine to establish a serious and sustained programme of amelioration and innovative technological development to assure more compassionate patient care and operational efficiency.

Molecular imaging of breast cancer: present and future directions

Medical imaging technologies have undergone explosive growth over the past few decades and now play a central role in clinical oncology. But the truly transformative power of imaging in the clinical management of cancer patients lies ahead. Today, imaging is at a crossroads, with molecularly targeted imaging agents expected to broadly expand the capabilities of conventional anatomical imaging methods. Molecular imaging will allow clinicians to not only see where a tumor is located in the body, but also to visualize the expression and activity of specific molecules (e.g., proteases and protein kinases) and biological processes (e.g., apoptosis, angiogenesis, and metastasis) that influence tumor behavior and/or response to therapy. Breast cancer, the most common cancer among women and a research area where our group is actively involved, is a very heterogeneous disease with diverse patterns of development and response to treatment. Hence, molecular imaging is expected to have a major impact on this type of cancer, leading to important improvements in diagnosis, individualized treatment, and drug development, as well as our understanding of how breast cancer arises.

Optimal Policies for Reducing Unnecessary Follow-up Mammography Exams in Breast Cancer Diagnosis

Mammography is the most effective screening tool for early diagnosis of breast cancer. Based on the mammography findings, radiologists need to choose from one of the following three alternatives: 1) take immediate diagnostic actions including prompt biopsy to confirm breast cancer; 2) recommend a follow-up mammogram; 3) recommend routine annual mammography. There are no validated structured guidelines based on a decision-analytical framework to aid radiologists in making such patient management decisions. Surprisingly, only 15-45% of the breast biopsies and less than 1% of short-interval follow-up recommendations are found to be malignant, resulting in unnecessary tests and patient-anxiety. We develop a finite-horizon discrete-time Markov decision process (MDP) model that may help radiologists make patient-management decisions to maximize a patient's total expected quality-adjusted life years. We use clinical data to find the policies recommended by the MDP model and also compare them to decisions made by radiologists at a large mammography practice. We also derive the structural properties of the MDP model, including sufficiency conditions that ensure the existence of a double control-limit type policy.

Adjuvant chemotherapy for breast cancer in patients with schizophrenia

The outcomes of treatment of physical illnesses are strongly affected by the presence of schizophrenia. We aimed to quantify the clinical course of schizophrenic breast cancer patients who were eligible for adjuvant chemotherapy to determine whether patients with this mental illness receive appropriate treatment for this physical illness. We searched the national Department of Veterans Affairs (DVA) computer database using computer codes for schizophrenia to identify patients who later developed breast cancer and were treated in DVA medical centers. Computer-based data were supplemented with chart-based clinical indicators. There were 55 subjects who appeared to be appropriate candidates for adjuvant systemic therapy. A number of these candidates were not offered postoperative endocrine or cytotoxic chemotherapy, while others refused treatment or were non-compliant. Behaviors typical of schizophrenic subjects, including hostility to caregivers, often disrupt their care. Schizophrenic patients often have advanced-stage cancer at diagnosis, often delay diagnosis and are frequently hostile towards healthcare workers. Many of these patients refuse therapy and/or are non-compliant.

Fracture risk increases after diagnosis of breast or other cancers in postmenopausal women: results from the Women's Health Initiative

SUMMARY

Risk for falls and fractures increases after breast cancer or other cancer diagnosis in postmenopausal women. Factors other than falls may be the major causes for the increased fracture risk.

INTRODUCTION

Cancer treatment and prognosis may have detrimental effects on bone health. However, there is a lack of prospective investigations on fracture risk among incident cancer cases.

METHODS

In this study, postmenopausal women (N = 146,959) from the Women's Health Initiative prospective cohort, who had no cancer history at baseline, were followed for up to 9 years and classified into no cancer, incident breast cancer (BC) and incident other cancer (OC) groups. The main outcomes measured were incident fractures and falls before and after cancer diagnosis. Hazards ratios (HR) and 95% confidence intervals (CI) were computed from Cox proportional hazards model.

RESULTS

While hip fracture risk before a cancer diagnosis was similar between the no cancer and cancer groups, hip fracture risk was significantly higher after BC diagnosis (HR = 1.55, CI = 1.13-2.11) and the elevated risk was even more notable after OC diagnosis (HR = 2.09, CI = 1.65-2.65). Risk of falls also increased after BC (HR = 1.15, CI = 1.06-1.25) or OC diagnosis (HR = 1.27, CI = 1.18-1.36), but could not fully explain the elevated hip fracture risk. Incident clinical vertebral and total fractures were also significantly increased after OC diagnosis (p < 0.05).

CONCLUSIONS

Postmenopausal women have significantly elevated risks for falls and fractures after a cancer diagnosis. The causes for this increased risk remained to be investigated.