[Studies in Computational Intelligence] Computational Intelligence in Healthcare 4 Volume 309 || An Advanced Probabilistic Framework for Assisting Screening Mammogram Interpretation

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<ul><li><p>Chapter 17</p><p>An Advanced Probabilistic Framework forAssisting Screening Mammogram Interpretation</p><p>Marina Velikova1,, Nivea Ferreira1, Maurice Samulski2,Peter J.F. Lucas1, and Nico Karssemeijer2</p><p>1 Institute for Computing and Information Sciences, Radboud University Nijmegen6525 ED, Nijmegen, The Netherlands</p><p>2 Department of Radiology, Radboud University Nijmegen Medical Centre6525 GA, Nijmegen, The Netherlands</p><p>Abstract. Breast cancer is the most common form of cancer amongwomen world-wide. One in nine women will be diagnosed with a form ofbreast cancer in her lifetime. In an eort to diagnose cancer at an earlystage, screening programs have been introduced by using periodic mam-mographic examinations in asymptomatic women. In evaluating screen-ing cases, radiologists are usually presented with two mammographicimages of each breast as a cancerous lesion tends to be observed in dif-ferent breast projections (views). Most computer-aided detection (CAD)systems, on the other hand, only analyse single views independently,and thus fail to account for the interaction between the views and thebreast cancer detection can be obscured due to the lack of consistencyin lesion marking. This limits the usability and the trust in the perfor-mance of such systems. In this chapter, we propose a unied Bayesiannetwork framework for exploiting multi-view dependencies between thesuspicious regions detected by a single-view CAD system. The frameworkis based on a multi-stage scheme, which models the way radiologists in-terpret mammograms, at four dierent levels: region, view, breast andcase. At each level, we combine all available image information for thepatient obtained from a single-view CAD system using a special class ofBayesian networkscausal independence models. The results from exper-iments with actual screening data of 1063 cases, from which 383 werecancerous, show that our approach outperforms the single-view CADsystem in distinguishing between normal and abnormal cases. This isa promising step towards the development of automated systems thatcan provide a valuable second opinion to the screening radiologists forimproved evaluation of breast cancer cases.</p><p>1 Introduction</p><p>According to the International Agency for Research on Cancer (IARC), breastcancer is the most common form of cancer among women world-wide, and evi-dence show that early detection combined to appropriate treatment is currently Corresponding author: marinav@cs.ru.nl</p><p>I. Bichindaritz et al. (Eds.): Computational Intelligence in Healthcare 4, SCI 309, pp. 371395.springerlink.com c Springer-Verlag Berlin Heidelberg 2010</p></li><li><p>372 M. Velikova et al.</p><p> MLO-Right MLO-Left CC-Right CC-Left</p><p>Fig. 1. MLO and CC projections of a left and right breast. The circle represents a lesionand the irregular areas represent regions, true positive and false positive, detected bya single-view CAD system</p><p>the most eective strategy on reducing mortality rates. This is the reason whymany countries have introduced breast cancer screening programs which targetat women of a certain age, usually starting at age of 50 years, aiming to detectthe occurrence of breast cancer as early as possible. Breast cancer screening iswidely seen as one of the keystone of breast cancer management, and an im-pressive decrease in breast cancer mortality in the age group invited for breastcancer screening has been shown [1].</p><p>The screening process involves carrying out an X-ray examination of thebreasts and the reading of the resulting images, or mammograms, by trainedradiologists. A screening mammographic exam usually consists of four images,corresponding to each breast scanned in two views mediolateral (MLO) viewand craniocaudal (CC) view; see Figure 1. The MLO projection is taken un-der (approximately) 45 angle and shows part of the pectoral muscles. The CCprojection is a top-down view of the breast.</p><p>Despite the high level of expertise of radiologists involved in breast cancerscreening, the number of misinterpretations in the screening process is still high.For instance, it is not unusual to encounter cases of women which have breastcancer, but have their lesions missed during the reading process. This is due tothe fact that diagnosis is subject to ones interpretation: either a lesion repre-senting the cancer is not detected by the radiologist or the lesion is identiedbut incorrectly evaluated. In other words, mammogram reading is not a straight-forward task: image quality, breast density and a vague denition of the char-acteristics of breast cancer lesions are some of the factors which inuence suchreading.</p><p>In order to support radiologists on their observations, and interpretation,computer-aided detection (CAD) systems are used. Most CAD systems havethe purpose of helping avoiding detection errors. However, CAD systems canalso be exploited to increase the radiologists performance by providing someinterpretation to mammograms [2].</p></li><li><p>An Advanced Probabilistic Framework 373</p><p>In reading mammograms, radiologists judge for the presence of a lesion bycomparing views. The general rule is that a lesion is to be observed in bothviews (whenever available). CAD systems, on the other hand, have been mostlydeveloped to analyse each view independently. Hence, the correlations in the le-sion characteristics are ignored and the breast cancer detection can be obscureddue to the lack of consistency in lesion marking. This limits the usability andthe trust in the performance of such systems. That is the reason why we developa Bayesian network model that combines the information from all the regionsdetected by a single-view CAD system in MLO and CC to obtain a single proba-bilistic measure for suspiciousness of a case. We explore multi-view dependenciesin order to improve the breast cancer detection rate, not in terms of individualregions of interest but, at a case level.</p><p>In detail, the power of our model relies on</p><p> Incorporating background knowledge with respect to mammogram readingdone by experts: multiple views are not analysed separately, and the cor-relation among corresponding regions in dierent views are preserved as aconsequence;</p><p> Analysing beyond the features of given regions: the interpretation is extendedup to case level. In the end, an interpretation to whether a given case can,or cannot, be considered as cancerous is our ultimate goal;</p><p> The design itself as representing a step forward on a better understanding ofthe domain, establishing clearly the qualitative and quantitative informationof the relevant concepts in such domain.</p><p>Our design of a Bayesian network model in the context of image analysis hasimportant implications with respect to the structure and content of that network,i.e., the understanding of the variables being used, and how those relate to oneanother. This is seen as one of the major contributions of the present work.</p><p>The remainder of the chapter is organised as follows. In the next section wegive some introduction on terms of the domain, which will be used through-out this chapter. In Section 3 we briey review previous research in multi-viewbreast cancer detection. In Section 4 we introduce basic denitions related toBayesian networks and in Section 5 we then describe a general Bayesian networkframework for multi-view detection. The proposed approach is evaluated on anapplication of breast cancer detection using actual screening data. The evalua-tion procedure and the results are presented in Section 6. Finally, conclusionsand directions for extension of our model are given in Section 7.</p><p>2 Terminology</p><p>To get the reader acquainted with the terminology used in the domain of breastcancer and throughout this chapter, we introduce next a number of concepts.By lesion we refer to a physical cancerous object detected in a patient. We calla contoured area on a mammogram a region. A region can be true positive (forexample, a lesion marked manually by a radiologist or detected automatically</p></li><li><p>374 M. Velikova et al.</p><p>by a CAD system) or false positive; see Figure 1. A region detected by a CADsystem is described by a number of continuous (real-valued) features describing,for example, size, density and location of the region. By link we denote matching(established correspondence) between two regions in MLO and CC views. Theterm case refers to a patient who has undergone a mammographic exam.</p><p>In addition, two main types of mammographic abnormalities are distinguished:microcalcications and masses. Microcalcifications are tiny deposits of calciumand are associated with extra cell activity in breast tissue. They can be scat-tered throughout the mammary gland, which usually is a benign sign, or occurin clusters, which might indicate breast cancer at an early stage.</p><p>According to the BI-RADS [3] denition, a mass is a space occupying le-sion seen in two dierent projections. When visible in only one projection, itis referred as a mammographic density. However, the density may be a mass,perhaps obscured by overlying glandular tissue on the other view, and if it ischaracterised by enough malignant features then the patient would be referredfor further examination. For instance, many cancerous tumours may be pre-sented as relatively circumscribed masses. Some might be sharply delineated ormay be partly irregular in outline, while other cancers (which induce a reactivebrosis) have an irregular or stellated appearance resulting in a characteristicradiographic feature, so-called spiculation. Masses might vary in size, usuallynot exceeding 5 cm. In terms of density, they appear denser (brighter on themammogram) when compared to breast fat tissue.</p><p>Masses are the more frequently occurring mammographic type of breast can-cer. In the context of screening mammography, there is strong evidence thatmisinterpretation of masses is a more common cause of missing cancer than per-ceptual oversight. Furthermore, applications have shown that CAD tends to per-form better in identifying malignant microcalcications compared with masses[4]. Masses are more dicult to detect due to the great variability in their phys-ical features and similarity to the breast tissue, especially at early stages ofdevelopment. Hence, the prompt of the current CAD comprises not only thecancer but also a large number of false positive (FP) locations-undesired resultin screening where the reading time is crucial. Given the challenge in true massidentication, in this work, we focus only on the detection of malignant masses.</p><p>3 Previous Research</p><p>The rapid development of computer technology in the last three decades hasbrought unique opportunities for building up and employing numerous auto-mated intelligent systems in order to facilitate human experts in decision making,to increase humans performance and eciency, and to allow cost saving. Oneof the domains where intelligent systems have been applied with great success ishealthcare [5]. Healthcare practitioners usually deal with large amounts of infor-mation ranging from patient and demographic to clinical and billing data. Thesedata need to be processed eciently and correctly, which often creates enormouspressure for the human experts. Hence, healthcare systems have emerged to</p></li><li><p>An Advanced Probabilistic Framework 375</p><p>facilitate clinicians as intelligent assistants in diagnostic and prognostic pro-cesses, laboratory analysis, treatment protocol, and teaching of medical students.</p><p>The initial research eorts in this area were devoted to the development ofmedical expert systems. Their main components comprise knowledge base, con-taining specic facts from the application area, rule-based system to extract theknowledge, explanation module to support and explain the decisions made tothe end user, and user interface to provide natural communication between thesystem and the human expert. One of the early prominent examples is MYCIN,developed in mid-1970s to diagnose and treat patients with infectious blood dis-eases caused by bacteria in the blood [6]. Up to date, the number of developedmedical expert systems has grown up enormously as discussed in [7].</p><p>With the extensive use of diagnostics imaging techniques such as X-ray, Mag-netic resonance imaging (MRI) and Ultrasound, recently, another promising typeof medical intelligent paradigm has emerged, namely computer-aided detection,or shortly CAD. As its name implies, the primary goal is to assist, rather thanto substitute, the human expert in image analysis tasks, allowing him compre-hensive evaluation in a short time. One typical application area of CAD systemsis the breast cancer detection and diagnosis. In [8], it is presented a generaloverview of various tasks such as image feature extraction, classication of mi-crocalcications and masses, and prognosis of breast cancer as well as the relatedtechniques to tackle these tasks, including Bayesian networks, neural networks,statistical and Cox prognostic models. Next, we review some of the recent tech-niques used for the development of breast cancer CAD systems, and then wediscuss a number of previous studies dealing with multi-view dependencies tofacilitate mammographic analysis.</p><p>3.1 Computer-Aided Detection for Breast Cancer</p><p>In some previous research, Bayesian network technology has been used in or-der to model the intrinsic uncertainty of the breast cancer domain [9,10]. Suchmodels incorporate background knowledge in terms of (causal) relations amongvariables. However, they use BI-RADS terms to describe a lesion, rather thannumerical features automatically extracted from images. This requires the hu-man expert to dene and provide the input features a priori, which limits theautomatic support of the system. Other research attempt is the expert systemfor breast cancer detection, proposed in [11]. The main techniques used are as-sociation rules for reducing the feature space and neural networks for classifyingthe cases. Application on the Wisconsin breast cancer dataset [12] showed thatthe proposed synthesis approach has the advantage of reducing the feature space,allowing for better discrimination between cancerous and normal cases. A limita-tion of this study, however, is the lack of any clinically relevant application of theproposed method with a larger dataset and an extensive validation procedure.</p><p>Automatic extraction of features is applied in [13], where the authors proposea CAD system for automatic detection of miscrocalcications on mammograms.The method uses least-squares support vector machines to t the intensity sur-face of the digital X-ray image and then convolution of the tted image with</p></li><li><p>376 M. Velikova et al.</p><p>mixture of kernels to detect maximum extremum points of microcalcications.Experimental results with benchmark mammographic data of 22 mammogramshave demonstrated computational eciency (detection per image in less than 1second) and the potential reliability of the classication outcome. However, theseresults required a better validation with a larger dataset in order to provide aninsight for the clinical application of the method.</p><p>In [14...</p></li></ul>