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The rejection of false-positive marks increases the radiolo-gist's interpretation time, negatively impacting overall reading time and workflow. The term true negative is generally not defined in the assessment of CAD performance, because there is no gold standard that shows all nodules. Consequently, the specificity of CAD is not caluclated. However, it has been suggested that the term true negative be used to describe a case with no lesions for which no CAD marks are generated. This definition would potentially be useful if a first-reader paradigm were ever implemented.


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In this paradigm, the CAD would be applied before the radiologist reading; in cases in which no CAD marks were generated, the radiologist would not have to perform a detailed nodule search. Nevertheless, this would be a promising CAD implementation, particularly for screening databases. While it is already difficult to define the parameters to describe CAD performance, the re-quirements for CAD performance vary with the database under evaluation and with the experience of the user. The database under evaluation determines the need for sensitive detection of small nodules.

On the other hand, any nodule particularly a new nodule in an oncologic patient is primarily suggestive of a metastasis and must be detected regardless of its size; therefore, a high sensitivity is required even for small nodules in these patients. To date, most CAD systems have a detection rate that decreases with decreasing nodule size. Since a radiologist's performance also deteriorates when he or she is looking for small nodules, there is still an overall incremental improvement in diagnostic accuracy even when CAD is used for small nodule detection. Reader experience is another factor that influences the evaluation of CAD software.

If nonexperienced readers interpret a chest CT, they might appreciate any mark pointed out by the CAD, as they are more likely to miss a target lesion than would a more experienced radiologist. They might not mind the additional time required to interpret the study. In a study of the incremental effects of CAD on the performance of readers with different levels of experience, there was a significant difference in detection rates between radiologists and nonradiologists before CAD; but, after CAD, there was no significant difference in detection rates between these readers.

This tendency helps support the use of CAD to assist a relatively inexperienced on-call resident to identify pulmonary arterial filling defects. Given these factors, a good CAD performance can be defined as a high sensitivity de-tection rate combined with a low number of false positives. To date, the use of CAD in chest CT has been focused primarily on the detection of pulmonary nodules.

Most manufacturers develop software for this indication, and many studies have been published in this area. False-positive findings are to be expected from artifacts from respiratory or cardiac motion, vessel bifurcations, hilar vessels, and parenchymal scars. The author has had experience with the ImageChecker software from R2 Technology, which is designed as a second reader to be implemented after the radiologist's initial read. In an analysis of low-dose CT scans 60 mA, kV, 1.

This result was based on noncalcified, solid nodules with a cutoff size of 5 mm. In this study, the results of CAD and the radiologists' results are complementary, since the use of CAD as a second reader tends to find some nodules that are different from those that the radiologist identifies and can indeed improve lung-nodule detection in a screening population.

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The complementary results of a radiologist's read and CAD results have also been found by other studies. All false-positive marks were easily dismissed by glancing at the image or scrolling up and down a few anatomic levels, but this still required a considerable amount of time for the second read with CAD. Clearly, the CAD software cannot yet achieve the differentiating ability of the radiologist's "glance" in these cases.

There is general agreement in the literature that the addition of CAD improves a radiologist's nodule detection rate. However, individual studies are difficult to compare, since they have been performed with different CAD systems, on different databases, using different CT scanning parameters, and with different size thresholds for computing sensitivity and false positives Table 1. Lee et al 23 studied the influence of radiation dose on the use of CAD and reported that a decrease in dose results in a higher false-positive rate.

This database will be available to researchers and is expected to lead to the publication of comparative studies. Most published studies to date have used the second-read model. The challenge with this approach is to limit the number of false-negative marks to an absolute minimum. When CAD is used for joint reading, a radiologist will be more likely to rely on the computer algorithm to point out any potential nodule and, thus, would be expected to decrease their effort to detect additional nodules not pointed out by the software.

A recent study supports this expectation.


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  5. The mean reading time without CAD was seconds, was reduced with concurrent reading seconds , and was longer with a second-read approach seconds. This is likely explained by the decrease in attention of the radiologist during concurrent CAD application, which is supported by the decreased reading time. The authors concluded that CAD could either decrease interpretation time or improve nodule detection, but not both. These results require verification. In addition to the detection of pulmonary nodules, a second area of potential CAD application is for the characterization of nodules as possibly malignant or likely benign lesions.

    With the current use of MDCT, the sensitivity for the detection of lung nodules is high, but the specificity for diagnosing malignant nodules is low. Additional features must be included in CAD tools to detect malignant nodules, the so-called CADx software tools, 14 which would point out malignant nodules only and would allow true-negative values and, hence, specificity to be computed. CADx software, in general, has a variety of indications, including the quantification of microvascular parameters derived from con-trast-enhanced dynamic CT perfusion studies 26 and the quantification of positron-emission tomography PET data.

    With the use of CADx in MDCT scanning, analysis options are based on a more detailed evaluation of morphology or, if studies from several time-points are available, the evaluation can quantify any nodule growth. Incorporating morphology into decision analysis seems to be the most basic approach. Ideally, morphologic features such as shape, density, and location would be used to rank a nodule into cancer probabilities and display them with different symbols.

    Li et al 27 successfully trained CADx software to determine the likelihood of malignancy of lung nodules based on various objective features, which confirmed this promising approach. The inclusion of nodule growth has been evaluated in more detail. Most vendors offer a "temporal comparison" tool that displays any change in any given nodule, which is commonly reported as growth rate in days and as percent of volume change Figure 5. This information seems to be highly valuable for the characterization of nodules as possibly malignant or likely benign, and, moreover, provides prognostic information in the case of cancer.

    This information may also be useful in monitoring treatment responses. Thin-slice 1 to 1. The volume approach promises to be more sensitive to change than the previously used diameter measurements. An average VDT in lung cancer has been reported to be In addition to the limited definition of a VDT that indicates malignancy, there are several technical problems that can impair a correct volume measurement.

    Lung Imaging and Computer Aided Diagnosis

    Even assuming thin-slice MDCT scanning with consistent parameters, volume measurements are influenced by attached structures such as vessels and pleura that might be included in the volumes to different extents and by different inspiration. In addition to comparing the volume of nodules on studies from different time points, CADx tools offer automated registration and nodule matching to decrease the time required for comparison. According to the author's own unpublished experience, this approach can result in misregistrations that frequently require "unlinking" of the current and former CT scans, which makes automated registration still somewhat impractical HC Roberts, unpublished data, Most CAD studies have been performed with thin slices 1 to 2 mm.

    Some algorithms allow for the processing of thicker 5 mm slices, but others do not. For example, the ImageChecker algorithm will not execute if slices are thicker than 3 mm. Thicker slices 5 to 10 mm allow partial volume effects if the nodules are smaller than the slice thickness, which results in an apparent subsolid density. Although CAD has been used in clinical environments for over 40 years, CAD usually does not substitute the doctor or other professional, but rather plays a supporting role.

    The professional generally a radiologist is generally responsible for the final interpretation of a medical image. CAD is fundamentally based on highly complex pattern recognition. X-ray or other types of images are scanned for suspicious structures. Normally a few thousand images are required to optimize the algorithm. The following procedures are examples of classification algorithms.

    If the detected structures have reached a certain threshold level, they are highlighted in the image for the radiologist. Depending on the CAD system these markings can be permanently or temporary saved. The latter's advantage is that only the markings which are approved by the radiologist are saved. False hits should not be saved, because an examination at a later date becomes more difficult then. CAD systems seek to highlight suspicious structures. The less FPs indicated, the higher the specificity is.

    A low specificity reduces the acceptance of the CAD system because the user has to identify all of these wrong hits. In other segments e. CT lung examinations the FP-rate could be 25 or more. The absolute detection rate of the radiologist is an alternative metric to sensitivity and specificity. Overall, results of clinical trials about sensitivity, specificity, and the absolute detection rate can vary markedly.

    Computer-aided Diagnosis of Lung Cancer in Computed Tomography Scans: A Review | Bentham Science

    Each study result depends on its basic conditions and has to be evaluated on those terms. The following facts have a strong influence:. CAD is used in the diagnosis of breast cancer , lung cancer , colon cancer , prostate cancer , bone metastases , coronary artery disease , congenital heart defect , pathological brain detection, Alzheimer's disease , and diabetic retinopathy. CAD is used in screening mammography X-ray examination of the female breast.

    Screening mammography is used for the early detection of breast cancer. CAD systems are often utilized to help classify a tumor as malignant or benign. CAD is especially established in US and the Netherlands and is used in addition to human evaluation, usually by a radiologist. The first CAD system for mammography was developed in a research project at the University of Chicago. Today it is commercially offered by iCAD and Hologic. A systematic review on computer-aided detection in screening mammography concluded that CAD does not have a significant effect on cancer detection rate, but does undesirably increase recall rate i.

    However, it noted considerable heterogeneity in the impact on recall rate across studies. Procedures to evaluate mammography based on magnetic resonance imaging exist too.

    In the diagnosis of lung cancer, computed tomography with special three-dimensional CAD systems are established and considered as appropriate second opinions. Today all well-known vendors of medical systems offer corresponding solutions. Early detection of lung cancer is valuable.

    However, the random detection of lung cancer in the early stage stage 1 in the X-ray image is difficult. Virtual dual-energy imaging [22] [23] [24] [25] improved the performance of CAD systems in chest radiography. CAD is available for detection of colorectal polyps in the colon in CT colonography.

    Introduction

    CAD detects the polyps by identifying their characteristic "bump-like" shape. To avoid excessive false positives, CAD ignores the normal colon wall, including the haustral folds. Early detection of pathology can be the difference between life and death. CADe can be done by auscultation with a digital stethoscope and specialized software, also known as Computer-aided auscultation.

    Murmurs, irregular heart sounds, caused by blood flowing through a defective heart, can be detected with high sensitivity and specificity. Computer-aided auscultation is sensitive to external noise and bodily sounds and requires an almost silent environment to function accurately.

    Chaplot et al. Their feature vector of each image is created by considering the magnitudes of Slantlet transform outputs corresponding to six spatial positions chosen according to a specific logic. Results over images showed that the classification accuracy was In , Saritha et al. Saritha also suggested to use spider-web plots. Its classification accuracy was reported as In , El-Dahshan et al. In , Zhou et al. CADs can be used to identify subjects with Alzheimer's and mild cognitive impairment from normal elder controls.

    In , Padma et al. In , Signaevsky et al. The measured performance on test data of eight naive WSI across various tauopathies resulted in the recall , precision , and an F1 score of 0. Eigenbrain is a novel brain feature that can help to detect AD, based on Principal Component Analysis [44] or Independent Component Analysis decomposition.

    CADx is available for nuclear medicine images. Commercial CADx systems for the diagnosis of bone metastases in whole-body bone scans and coronary artery disease in myocardial perfusion images exist. With a high sensitivity and an acceptable false lesions detection rate, computer-aided automatic lesion detection system is demonstrated as useful and will probably in the future be able to help nuclear medicine physicians to identify possible bone lesions. Diabetic retinopathy is a disease of the retina that is diagnosed predominantly by fundoscopic images. Diabetic patients in industrialised countries generally undergo regular screening for the condition.

    Imaging is used to recognize early signs of abnormal retinal blood vessels. Manual analysis of these images can be time-consuming and unreliable. The use of some CAD systems to replace human graders can be safe and cost effective. Image pre-processing, and feature extraction and classification are two main stages of these CAD algorithms.

    Computer Aided Diagnosis CAD

    Image normalization is minimizing the variation across the entire image. Intensity variations in areas between periphery and central macular region of the eye have been reported to cause inaccuracy of vessel segmentation. Histogram equalization is useful in enhancing contrast within an image. At the end of the processing, areas that were dark in the input image would be brightened, greatly enhancing the contrast among the features present in the area. On the other hand, brighter areas in the input image would remain bright or be reduced in brightness to equalize with the other areas in the image.

    Besides vessel segmentation, other features related to diabetic retinopathy can be further separated by using this pre-processing technique. Microaneurysm and hemorrhages are red lesions, whereas exudates are yellow spots. Increasing contrast between these two groups allow better visualization of lesions on images. With this technique, review found that 10 out of the 14 recently since published primary research. Green channel filtering is another technique that is useful in differentiating lesions rather than vessels.

    This method is important because it provides the maximal contrast between diabetic retinopathy-related lesions. In contrast, exudates, which appear yellow in normal image, are transformed into bright white spots after green filtering. This technique is mostly used according to the review, with appearance in 27 out of 40 published articles in the past three years.

    Non-uniform illumination correction is a technique that adjusts for non-uniform illumination in fundoscopic image. Non-uniform illumination can be a potential error in automated detection of diabetic retinopathy because of changes in statistical characteristics of image. Morphological operations is the second least used pre-processing method in review.

    After pre-processing of funduscopic image, the image will be further analyzed using different computational methods. However, the current literature agreed that some methods are used more often than others during vessel segmentation analyses. These methods are SVM, multi-scale, vessel-tracking, region growing approach, and model-based approaches. The algorithm works by creating a largest gap between distinct samples in the data. The goal is to create the largest gap between these components that minimize the potential error in classification. Detecting blood vessel from new images can be done through similar manner using support vectors.

    Combination with other pre-processing technique, such as green channel filtering, greatly improves the accuracy of detection of blood vessel abnormalities. Multi-scale approach is a multiple resolution approach in vessel segmentation. At low resolution, large-diameter vessels can first be extracted. By increasing resolution, smaller branches from the large vessels can be easily recognized. Therefore, one advantage of using this technique is the increased analytical speed.