Computer-aided diagnostic models in breast cancer screening

Abstract

Mammography is the most common modality for breast cancer detection and diagnosis and is often complemented by ultrasound and MRI. However, similarities between early signs of breast cancer and normal structures in these images make detection and diagnosis of breast cancer a difficult task. To aid physicians in detection and diagnosis, computer-aided detection and computer-aided diagnostic (CADx) models have been proposed. A large number of studies have been published for both computer-aided detection and CADx models in the last 20 years. The purpose of this article is to provide a comprehensive survey of the CADx models that have been proposed to aid in mammography, ultrasound and MRI interpretation. We summarize the noteworthy studies according to the screening modality they consider and describe the type of computer model, input data size, feature selection method, input feature type, reference standard and performance measures for each study. We also list the limitations of the existing CADx models and provide several possible future research directions.Keywords breast cancer; computer-aided detection; computer-aided diagnosis; mammography; MRI; ultrasound Radiological imaging, which often includes mammography, ultrasound (US) and MRI, is the most effective means, to date, for early detection of breast cancer [1]. However, differentiating between benign and malignant findings is difficult.Successful breast cancer diagnosis requires systematic image analysis, characterization and integration of numerous clinical and mammographic variables [2], which is a difficult and error-prone task for physicians. This leads to low positive predictive value of imaging interpretation [3].The integration of computer models into the radiological imaging interpretation process can increase the accuracy of image interpretation. There are two broad categories of computer models in breast cancer diagnosis: computer-aided detection (CADe) and computer-aided The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript. NIH Public Access Author ManuscriptImaging Med. Author manuscript; available in PMC 2011 April 1. Published in final edited form as:Imaging Med. Mammography CADx modelsEarly work involving CADx models in mammography interpretation dates back to 1993. A summary list for primary mammography CADx models is presented in Huo et al. also used ANNs to classify mass lesions detected on screen-film mammograms [32,33]. They automated the feature extraction process to reduce the intra-observer variability [28,34]. In a follow-up study, Huo et al. used different sets of data for training and testing instead of a single database [35]. Their database included 50 biopsy-proven malignant masses, 50 biopsy-proven benign masses and ten cysts proved by fine needle aspirat...

Artificial intelligence in radiology

“…Several systems are already in clinical use, as is the case with screening mammograms 58 . They usually serve as a second opinion in complementing a radiologist’s assessment 59 , and their perceived successes have led to the development of similar systems for other imaging modalities, including ultrasonography and MRI 60 . For instance, traditional CADx systems have been used on ultrasonography images to diagnose cervical cancer in lymph nodes, where they have been found to improve the performance of particularly inexperienced radiologists as well as reduce variability among them 61 .…”

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“…It the 1980s, medical physicists and physicians at the Kurt Rossmann Laboratories (Department of Radiology at the University of Chicago) developed two comprehensive categories of computer models: computer-aided diagnosis (CADx) and computer-assisted detection (CADe) [38]. …”

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