Automatic Classification of Simulated Breast Tomosynthesis Whole Images for the Presence of Microcalcification Clusters Using Deep CNNs

Microcalcification clusters (MCs) are among the most important biomarkers for breast cancer, especially in cases of nonpalpable lesions. The vast majority of deep learning studies on digital breast tomosynthesis (DBT) are focused on detecting and classifying lesions, especially soft-tissue lesions, in small regions of interest previously selected. Only about 25% of the studies are specific to MCs, and all of them are based on the classification of small preselected regions. Classifying the whole image according to the presence or absence of MCs is a difficult task due to the size of MCs and all the information present in an entire image. A completely automatic and direct classification, which receives the entire image, without prior identification of any regions, is crucial for the usefulness of these techniques in a real clinical and screening environment. The main purpose of this work is to implement and evaluate the performance of convolutional neural networks (CNNs) regarding an automatic classification of a complete DBT image for the presence or absence of MCs (without any prior identification of regions). In this work, four popular deep CNNs are trained and compared with a new architecture proposed by us. The main task of these trainings was the classification of DBT cases by absence or presence of MCs. A public database of realistic simulated data was used, and the whole DBT image was taken into account as input. DBT data were considered without and with preprocessing (to study the impact of noise reduction and contrast enhancement methods on the evaluation of MCs with CNNs). The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance. Very promising results were achieved with a maximum AUC of 94.19% for the GoogLeNet. The second-best AUC value was obtained with a new implemented network, CNN-a, with 91.17%. This CNN had the particularity of also being the fastest, thus becoming a very interesting model to be considered in other studies. With this work, encouraging outcomes were achieved in this regard, obtaining similar results to other studies for the detection of larger lesions such as masses. Moreover, given the difficulty of visualizing the MCs, which are often spread over several slices, this work may have an important impact on the clinical analysis of DBT images

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Advancing the Clinical Potential of Carbon Nanotube-enabled stationary 3D Mammography

Scope and purpose. 3D imaging has revolutionized medicine. Digital breast tomosynthesis (DBT), also recognized as 3D mammography, is a relatively recent example. stationary DBT (sDBT) is an experimental technology in which the single moving x-ray source of conventional DBT has been replaced by a fixed array of carbon nanotube (CNT)-enabled sources. Given the potential for a higher spatial and temporal resolution compared to commercially-available, moving-source DBT devices, it was hypothesized that sDBT would provide a valuable tool for breast imaging. As such, the purpose of this work was to explore the clinical potential of sDBT. To accomplish this purpose, three broad Aims were set forth: (1) study the challenges of scatter and artifact with sDBT, (2) assess the performance of sDBT relative to standard mammographic screening approaches, and (3) develop a synthetic mammography capability for sDBT. Throughout the work, developing image processing approaches to maximize the diagnostic value of the information presented to readers remained a specific goal. Data sources and methodology. Sitting at the intersection of development and clinical application, this work involved both basic experimentation and human study. Quantitative measures of image quality as well as reader preference and accuracy were used to assess the performance of sDBT. These studies imaged breast-mimicking phantoms, lumpectomy specimens, and human subjects on IRB-approved study protocols, often using standard 2D and conventional 3D mammography for reference. Key findings. Characterizing scatter and artifact allowed the development of new processing approaches to improve image quality. Additionally, comparing the performance of sDBT to standard breast imaging technologies helped identify opportunities for improvement through processing. This line of research culminated in the incorporation of a synthetic mammography capability into sDBT, yielding images that have the potential to improve the diagnostic value of sDBT. Implications. This work advanced the evolution of CNT-enabled sDBT toward a viable clinical tool by incorporating key image processing functionality and characterizing the performance of sDBT relative to standard breast imaging techniques. The findings confirmed the clinical utility of sDBT while also suggesting promising paths for future research and development with this unique approach to breast imaging.Doctor of Philosoph

Enhanced Digital Breast Tomosynthesis diagnosis using 3D visualization and automatic classification of lesions

Breast cancer represents the main cause of cancer-related deaths in women. Nonetheless, the mortality rate of this disease has been decreasing over the last three decades, largely due to the screening programs for early detection. For many years, both screening and clinical diagnosis were mostly done through Digital Mammography (DM). Approved in 2011, Digital Breast Tomosynthesis (DBT) is similar to DM but it allows a 3D reconstruction of the breast tissue, which helps the diagnosis by reducing the tissue overlap. Currently, DBT is firmly established and is approved as a stand-alone modality to replace DM. The main objective of this thesis is to develop computational tools to improve the visualization and interpretation of DBT data. Several methods for an enhanced visualization of DBT data through volume rendering were studied and developed. Firstly, important rendering parameters were considered. A new approach for automatic generation of transfer functions was implemented and two other parameters that highly affect the quality of volume rendered images were explored: voxel size in Z direction and sampling distance. Next, new image processing methods that improve the rendering quality by considering the noise regularization and the reduction of out-of-plane artifacts were developed. The interpretation of DBT data with automatic detection of lesions was approached through artificial intelligence methods. Several deep learning Convolutional Neural Networks (CNNs) were implemented and trained to classify a complete DBT image for the presence or absence of microcalcification clusters (MCs). Then, a faster R-CNN (region-based CNN) was trained to detect and accurately locate the MCs in the DBT images. The detected MCs were rendered with the developed 3D rendering software, which provided an enhanced visualization of the volume of interest. The combination of volume visualization with lesion detection may, in the future, improve both diagnostic accuracy and also reduce analysis time. This thesis promotes the development of new computational imaging methods to increase the diagnostic value of DBT, with the aim of assisting radiologists in their task of analyzing DBT volumes and diagnosing breast cancer

Modelling the interpretation of digital mammography using high order statistics and deep machine learning

Visual search is an inhomogeneous, yet efficient sampling process accomplished by the saccades and the central (foveal) vision. Areas that attract the central vision have been studied for errors in interpretation of medical images. In this study, we extend existing visual search studies to understand features of areas that receive direct visual attention and elicit a mark by the radiologist (True and False Positive decisions) from those that elicit a mark but were captured by the peripheral vision. We also investigate if there are any differences between these areas and those that are never fixated by radiologists. Extending these investigations, we further explore the possibility of modelling radiologists’ search behavior and their interpretation of mammograms using deep machine learning techniques. We demonstrated that energy profiles of foveated (FC), peripherally fixated (PC), and never fixated (NFC) areas are distinct. It was shown that FCs are selected on the basis of being most informative. Never fixated regions were found to be least informative. Evidences that energy profiles and dwell time of these areas influence radiologists’ decisions (and confidence in such decisions) were also shown. High-order features provided additional information to the radiologists, however their effect on decision (and confidence in such decision) was not significant. We also showed that deep-convolution neural network can successfully be used to model radiologists’ attentional level, decisions and confidence in their decisions. High accuracy and high agreement (between true and predicted values) in such predictions can be achieved in modelling attentional level (accuracy: 0.90, kappa: 0.82) and decisions (accuracy: 0.92, kappa: 0.86) of radiologists. Our results indicated that an ensembled model for radiologist’s search behavior and decision can successfully be built. Convolution networks failed to model missed cancers however

Deep learning in breast cancer screening

Breast cancer is the most common cancer form among women worldwide and the incidence is rising. When mammography was introduced in the 1980s, mortality rates decreased by 30% to 40%. Today all women in Sweden between 40 to 74 years are invited to screening every 18 to 24 months. All women attending screening are examined with mammography, using two views, the mediolateral oblique (MLO) view and the craniocaudal (CC) view, producing four images in total. The screening process is the same for all women and based purely on age, and not on other risk factors for developing breast cancer. Although the introduction of population-based breast cancer screening is a great success, there are still problems with interval cancer (IC) and large screen detected cancers (SDC), which are connected to an increased morbidity and mortality. To have a good prognosis, it is important to detect a breast cancer early while it has not spread to the lymph nodes, which usually means that the primary tumor is small. To improve this, we need to individualize the screening program, and be flexible on screening intervals and modalities depending on the individual breast cancer risk and mammographic sensitivity. In Sweden, at present, the only modality in the screening process is mammography, which is excellent for a majority of women but not for all. The major lack of breast radiologists is another problem that is pressing and important to address. As their expertise is in such demand, it is important to use their time as efficiently as possible. This means that they should primarily spend time on difficult cases and less time on easily assessed mammograms and healthy women. One challenge is to determine which women are at high risk of being diagnosed with aggressive breast cancer, to delineate the low-risk group, and to take care of these different groups of women appropriately. In studies II to IV we have analysed how we can address these challenges by using deep learning techniques. In study I, we described the cohort from which the study populations for study II to IV were derived (as well as study populations in other publications from our research group). This cohort was called the Cohort of Screen Aged Women (CSAW) and contains all 499,807 women invited to breast cancer screening within the Stockholm County between 2008 to 2015. We also described the future potentials of the dataset, as well as the case control subset of annotated breast tumors and healthy mammograms. This study was presented orally at the annual meeting of the Radiological Society of North America in 2019. In study II, we analysed how a deep learning risk score (DLrisk score) performs compared with breast density measurements for predicting future breast cancer risk. We found that the odds ratios (OR) and areas under the receiver operating characteristic curve (AUC) were higher for age-adjusted DLrisk score than for dense area and percentage density. The numbers for DLrisk score were: OR 1.56, AUC, 0.65; dense area: OR 1.31, AUC 0.60, percent density: OR 1.18, AUC, 0.57; with P < .001 for differences between all AUCs). Also, the false-negative rates, in terms of missed future cancer, was lower for the DLrisk score: 31%, 36%, and 39% respectively. This difference was most distinct for more aggressive cancers. In study III, we analyzed the potential cancer yield when using a commercial deep learning software for triaging screening examinations into two work streams – a ‘no radiologist’ work stream and an ‘enhanced assessment’ work stream, depending on the output score of the AI tumor detection algorithm. We found that the deep learning algorithm was able to independently declare 60% of all mammograms with the lowest scores as “healthy” without missing any cancer. In the enhanced assessment work stream when including the top 5% of women with the highest AI scores, the potential additional cancer detection rate was 53 (27%) of 200 subsequent IC, and 121 (35%) of 347 next-round screen-detected cancers. In study IV, we analyzed different principles for choosing the threshold for the continuous abnormality score when introducing a deep learning algorithm for assessment of mammograms in a clinical prospective breast cancer screening study. The deep learning algorithm was supposed to act as a third independent reader making binary decisions in a double-reading environment (ScreenTrust CAD). We found that the choice of abnormality threshold will have important consequences. If the aim is to have the algorithm work at the same sensitivity as a single radiologist, a marked increase in abnormal assessments must be accepted (abnormal interpretation rate 12.6%). If the aim is to have the combined readers work at the same sensitivity as before, a lower sensitivity of AI compared to radiologists is the consequence (abnormal interpretation rate 7.0%). This study was presented as a poster at the annual meeting of the Radiological Society of North America in 2021. In conclusion, we have addressed some challenges and possibilities by using deep learning techniques to make breast cancer screening programs more individual and efficient. Given the limitations of retrospective studies, there is a now a need for prospective clinical studies of deep learning in mammography screening