Detection of microcalcifications in photon-counting dedicated breast-CT using a deep convolutional neural network: Proof of principle

OBJECTIVE In this study, we investigate the feasibility of a deep Convolutional Neural Network (dCNN), trained with mammographic images, to detect and classify microcalcifications (MC) in breast-CT (BCT) images. METHODS This retrospective single-center study was approved by the local ethics committee. 3518 icons generated from 319 mammograms were classified into three classes: "no MC" (1121), "probably benign MC" (1332), and "suspicious MC" (1065). A dCNN was trained (70% of data), validated (20%), and tested on a "real-world" dataset (10%). The diagnostic performance of the dCNN was tested on a subset of 60 icons, generated from 30 mammograms and 30 breast-CT images, and compared to human reading. ROC analysis was used to calculate diagnostic performance. Moreover, colored probability maps for representative BCT images were calculated using a sliding-window approach. RESULTS The dCNN reached an accuracy of 98.8% on the "real-world" dataset. The accuracy on the subset of 60 icons was 100% for mammographic images, 60% for "no MC", 80% for "probably benign MC" and 100% for "suspicious MC". Intra-class correlation between the dCNN and the readers was almost perfect (0.85). Kappa values between the two readers (0.93) and the dCNN were almost perfect (reader 1: 0.85 and reader 2: 0.82). The sliding-window approach successfully detected suspicious MC with high image quality. The diagnostic performance of the dCNN to classify benign and suspicious MC was excellent with an AUC of 93.8% (95% CI 87, 4%-100%). CONCLUSION Deep convolutional networks can be used to detect and classify benign and suspicious MC in breast-CT images

Dedicated Breast Computed Tomography With a Photon-Counting Detector: Initial Results of Clinical In Vivo Imaging

OBJECTIVES: The purpose of this work is to present the data obtained from the first clinical in vivo application of a new dedicated spiral breast computed tomography (B-CT) equipped with a photon-counting detector. MATERIALS AND METHODS: The institutional review board approved this retrospective study. Twelve women referred for breast cancer screening were included and underwent bilateral spiral B-CT acquired in prone position. Additional sonography was performed in case of dense breast tissue or any B-CT findings. In 3 women, previous mammography was available for comparison. Soft tissue (ST) and high-resolution (HR) images were reconstructed. Two independent radiologists performed separately the readout for subjective image quality and for imaging findings detection. Objective image quality evaluation was performed in consensus and included spatial resolution, contrast resolution, signal-to-noise ratio (SNR), and contrast-to-noise ratio. All women were asked to report about positioning comfort and overall comfort during data acquisition. RESULTS: The major pectoral muscle was included in 15 breast CT scans (62.5%); glandular component was partially missing in 2 (8.3%) of the 24 scanned breasts. A thin "ring artifact" was present in all scans but had no influence on image interpretations; no other artifacts were present. Subjective image quality assessment showed excellent agreement between the 2 readers (κ = 1). Three masses were depicted in B-CT and were confirmed as simple cysts in sonography. Additional 5 simple cysts and 2 solid benign lesions were identified only in sonography. A total of 12 calcifications were depicted with a median size of 1.1 mm (interquartile range, 0.7-1.7 mm) on HR and 1.4 mm (interquartile range, 1.1-1.8 mm) on ST images. Median SNRgl, SNRfat, and contrast-to-noise ratio were significantly higher in ST than in HR reconstructions (each, P < 0.001). A mild discomfort due to positioning of the rib cage on the table was reported by 2 women (16.7%); otherwise, no discomfort was reported. CONCLUSIONS: The new dedicated B-CT equipped with a photon-counting detector provides high-quality images with potential for screening of breast cancer along with minor patient discomfort

Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

Imaging Breast Microcalcifications Using Dark-Field Signal in Propagation-Based Phase-Contrast Tomography

Breast microcalcifications are an important primary radiological indicator of breast cancer. However, microcalcification classification and diagnosis may be still challenging for radiologists due to limitations of the standard 2D mammography technique, including spatial and contrast resolution. In this study, we propose an approach to improve the detection of microcalcifications in propagation-based phase-contrast X-ray computed tomography of breast tissues. Five fresh mastectomies containing microcalcifications were scanned at different X-ray energies and radiation doses using synchrotron radiation. Both bright-field (i.e. conventional phase-retrieved images) and dark-field images were extracted from the same data sets using different image processing methods. A quantitative analysis was performed in terms of visibility and contrast-to-noise ratio of microcalcifications. The results show that while the signal-to-noise and the contrast-to-noise ratios are lower, the visibility of the microcalcifications is more than two times higher in the dark-field images compared to the bright-field images. Dark-field images have also provided more accurate information about the size and shape of the microcalcifications

Diagnostic and Interventional Radiology in a breast centre

Topic of the thesis is the analysis of three key aspects of diagnostic and interventional radiology in a Breast Center: 1 - monitoring of the radiation dose delivered by mammography; 2 – integrated diagnostic approach conducted together by radiologists and surgeons (joint CORD; 3 - interdisciplinary meetings. Radiation dose monitoring Mammography is still considered the most effective imaging technique for the early detection of breast cancer and for mortality reduction. The parameter for estimating the absorbed dose is the average glandular dose. The purpose of this section of the thesis is to present the data collected from three mammography units in the period from January 1 to May 31. A dose monitoring software (TQM) was used that was able to automatically detect relevant indices from X-ray equipments and to analyze the data in terms of variability of dosimetric behaviours. The “joint CORD” In the period from January 16 to April 11, a weekly session handled by a junior breast surgeon and a senior Radiology resident (joint CORD) was established. The aim of this session was to optimize the path within the Breast Centre of the patients with urgent referral and nonspecific symptoms. In fact, for these patients it is likely that the final diagnosis can be already reached with a clinical breast examination and a breast ultrasonography. Twelve sessions of joint CORD were performed, that included 95 patients (average: 7.9 patients for each session). Of the 95 patients who had access to this service, 33 had an examination performed elsewhere with detection of suspicious nodules or were controls at 6 months of multiple fibroadenomatosis; 20 came for palpable lumps; 16 for unilateral or bilateral breast pain; 5 for mastitis; 5 for swelling / hyperemia or collection after QUART; 4 for secretion (milky); and 12 for various reasons (axillary swelling, screening prior hormonal therapies, skin nodule, adenoma of the nipple). Of the 95 patients, besides ultrasound and clinical breast examination, 24 (25.2%) underwent mammography, 6 (6.3%) underwent MRI, and 2 (2, 1%) underwent stereotactic biopsy. Twentythree US-guided cytological examinations were performed (24.2%): in 21 cases of nodules and in 2 cases of mammary secretions. The results of cytology were: 15 C2 (benign findings) with the conclusion of the diagnostic iter; 2 C3 (probably benign findings); 4 C1 (inadequate sampling). The joint CORD allowed patients to finish their diagnostic workup in a single access, thus dramatically reducing the time they spent in the breast imaging center. Interdisciplinary meetings Were conducted interdisciplinary meetings (with breast radiologists and surgeons) on a weekly basis starting from January 17. This section of the thesis analyzes the period from January 17 to March 27. The cases discussed were tabulated to analyze the most frequent causes of problems, possible solutions and improvements for clinical practice. Eleven meetings were held, discussing a total of 48 cases (average: 4.36 cases discussed per meeting). Of the 48 cases discussed, 11 (22.9%) did not reach a cyto-histological conclusive diagnosis, 9 (18.7%) had an underlying lack of communication between radiologists and surgeons, 8 (16.6%) required a further biopsy, 6 (12.5%) had an improper use of MRI , 5 (10.4%) required additional MRI, 3 (6.25%) required a shared decision between radiologists and surgeons, 2 (4.1%) had a PET inappropriately performed, 2 (4.1%) were considered inappropriate for surgical evaluation, and 1 (2.07%) required a new mammography

Detecting Abnormal Axillary Lymph Nodes on Mammograms Using a Deep Convolutional Neural Network

The purpose of this study was to determine the feasibility of a deep convolutional neural network (dCNN) to accurately detect abnormal axillary lymph nodes on mammograms. In this retrospective study, 107 mammographic images in mediolateral oblique projection from 74 patients were labeled to three classes: (1) "breast tissue", (2) "benign lymph nodes", and (3) "suspicious lymph nodes". Following data preprocessing, a dCNN model was trained and validated with 5385 images. Subsequently, the trained dCNN was tested on a "real-world" dataset and the performance compared to human readers. For visualization, colored probability maps of the classification were calculated using a sliding window approach. The accuracy was 98% for the training and 99% for the validation set. Confusion matrices of the "real-world" dataset for the three classes with radiological reports as ground truth yielded an accuracy of 98.51% for breast tissue, 98.63% for benign lymph nodes, and 95.96% for suspicious lymph nodes. Intraclass correlation of the dCNN and the readers was excellent (0.98), and Kappa values were nearly perfect (0.93-0.97). The colormaps successfully detected abnormal lymph nodes with excellent image quality. In this proof-of-principle study in a small patient cohort from a single institution, we found that deep convolutional networks can be trained with high accuracy and reliability to detect abnormal axillary lymph nodes on mammograms

Automatic and standardized quality assurance of digital mammography and tomosynthesis with deep convolutional neural networks

OBJECTIVES The aim of this study was to develop and validate a commercially available AI platform for the automatic determination of image quality in mammography and tomosynthesis considering a standardized set of features. MATERIALS AND METHODS In this retrospective study, 11,733 mammograms and synthetic 2D reconstructions from tomosynthesis of 4200 patients from two institutions were analyzed by assessing the presence of seven features which impact image quality in regard to breast positioning. Deep learning was applied to train five dCNN models on features detecting the presence of anatomical landmarks and three dCNN models for localization features. The validity of models was assessed by the calculation of the mean squared error in a test dataset and was compared to the reading by experienced radiologists. RESULTS Accuracies of the dCNN models ranged between 93.0% for the nipple visualization and 98.5% for the depiction of the pectoralis muscle in the CC view. Calculations based on regression models allow for precise measurements of distances and angles of breast positioning on mammograms and synthetic 2D reconstructions from tomosynthesis. All models showed almost perfect agreement compared to human reading with Cohen's kappa scores above 0.9. CONCLUSIONS An AI-based quality assessment system using a dCNN allows for precise, consistent and observer-independent rating of digital mammography and synthetic 2D reconstructions from tomosynthesis. Automation and standardization of quality assessment enable real-time feedback to technicians and radiologists that shall reduce a number of inadequate examinations according to PGMI (Perfect, Good, Moderate, Inadequate) criteria, reduce a number of recalls and provide a dependable training platform for inexperienced technicians