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Computer-Aided Diagnosis Systems for Lung Cancer: Challenges and Methodologies

DOI: 10.1155/2013/942353

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Abstract:

This paper overviews one of the most important, interesting, and challenging problems in oncology, the problem of lung cancer diagnosis. Developing an effective computer-aided diagnosis (CAD) system for lung cancer is of great clinical importance and can increase the patient’s chance of survival. For this reason, CAD systems for lung cancer have been investigated in a huge number of research studies. A typical CAD system for lung cancer diagnosis is composed of four main processing steps: segmentation of the lung fields, detection of nodules inside the lung fields, segmentation of the detected nodules, and diagnosis of the nodules as benign or malignant. This paper overviews the current state-of-the-art techniques that have been developed to implement each of these CAD processing steps. For each technique, various aspects of technical issues, implemented methodologies, training and testing databases, and validation methods, as well as achieved performances, are described. In addition, the paper addresses several challenges that researchers face in each implementation step and outlines the strengths and drawbacks of the existing approaches for lung cancer CAD systems. 1. Introduction Lung cancer remains the leading cause of cancer-related deaths in the US. In 2012, there were approximately 229,447 new cases of lung cancer and 159,124 related deaths [1]. Early diagnosis can improve the effectiveness of treatment and increase the patient’s chance of survival [2]. Positron emission tomography (PET), computed tomography (CT), low-dose computed tomography (LDCT), and contrast-enhanced computed tomography (CE-CT) are the most common noninvasive imaging modalities for detecting and diagnosing lung nodules. PET scans are used to discriminate between malignant and benign lung nodules. Early detection of the nodules can be based on CT and LDCT scans that allow for reconstructing the anatomy of and detecting the anatomic changes in the chest. The CE-CT allows for reconstructing the anatomy of the chest and assessing the detected nodule’s characteristics. A wealth of known publications have investigated the development of computer-aided diagnosis (CAD) systems for lung cancer from a host of different image modalities. The success of a particular CAD system can be measured in terms of accuracy of diagnosis, speed, and automation level. The goal of this paper is to overview different CAD systems for lung cancer proposed in literature. A schematic diagram of a typical CAD system for lung cancer is shown in Figure 1. The segmentation of lung tissues on chest images is a

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