&lt;title&gt;Preliminary performance analysis of breast MRI CAD system&lt;/title&gt;

Penn, Alan; Kumar, Nitin A.; Thompson, Scott F.; Schnall, Mitchell D.; Wang, Fei; Gatsonis, Constantine

doi:10.1117/12.431088

Cited by 6 publications

(7 citation statements)

References 5 publications

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“…The focal mass multivariate models are expected to serve as an important teaching and decision-support tool. Penn et al (31) have shown that a computeraided diagnosis system involving the use of some similar variables was able to improve the performance of two of five readers. Further study is needed to assess and document the ability of these models to improve the performance of radiologists of various experience levels.…”

Section: Discussionmentioning

confidence: 99%

Diagnostic Architectural and Dynamic Features at Breast MR Imaging: Multicenter Study

Schnall¹,

et al. 2006

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Section: Discussionmentioning

confidence: 99%

Diagnostic Architectural and Dynamic Features at Breast MR Imaging: Multicenter Study

Schnall¹,

et al. 2006

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“…Previous studies used radiologists to evaluate noncontrast enhanced HiSS MRI (14)(15)(16)(17)(18). Computer-aided diagnosis (CADx) methods for automated lesion segmentation, feature extraction, and classification have been developed for breast MRI (27)(28)(29)(30)(31)(32)(33)(34)(35), and observer studies have demonstrated the potential usefulness of breast MRI CADx in the clinical arena (36,37). The goal of automated computerized analysis of medical images is to obtain quantitative and reproducible indices for lesion characterization (38,39).…”

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Potential of computer‐aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions

Bhooshan

Giger

Medved

et al. 2013

Magnetic Resonance Imaging

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Purpose: To compare the performance of CADx analysis of pre-contrast HiSS MRI to that of clinical DCE-MRI in the diagnostic classification of breast lesions. Materials and Methods: Thirty-four malignant and seven benign lesions were scanned using 2D HiSS and clinical 4D DCE-MRI protocols. Lesions were automatically segmented. Morphological features were calculated for HiSS whereas both morphological and kinetic features were calculated for DCE-MRI. After stepwise feature selection, Bayesian artificial neural networks merged selected features, and ROC analysis evaluated the performance with leave-one-lesion-out validation. Results: AUC values of 0.92 ± 0.06 and 0.90 ± 0.05 were obtained using CADx on HiSS and DCE-MRI, respectively, in the task of classifying benign and malignant lesions. While we failed to show that the higher HiSS performance was significantly better than DCE-MRI, non-inferiority testing confirmed that HiSS was not worse than DCE-MRI. Conclusion: CADx of HiSS (without contrast) performed similarly to CADx on clinical DCE-MRI; thus, computerized analysis of HiSS may provide sufficient information for diagnostic classification. The results are clinically important for patients in whom contrast agent is contra-indicated. Even in the limited acquisition mode of 2D single slice HiSS, by using quantitative image analysis to extract characteristics from the HiSS images, similar performance levels were obtained as compared to those from current clinical 4D DCE-MRI. As HiSS acquisitions become possible in 3D, CADx methods can also be applied. Since HiSS and DCE-MRI are based on different contrast mechanisms, the use of the two protocols in combination may increase diagnostic accuracy.

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“…Penn et al (23) described the first and second revisions of a BMRI CAD system. They used 95 contrast‐enhanced MR cases and had an expert locate the suspicious lesion for each case.…”

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Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer‐aided diagnosis (CAD) system

Meinel

Stolpen

Berbaum

et al. 2006

Magnetic Resonance Imaging

136

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Purpose:To develop and test a computer-aided diagnosis (CAD) system to improve the performance of radiologists in classifying lesions on breast MRI (BMRI). Materials and Methods:A CAD system was developed that uses a semiautomated segmentation method. After segmentation, 42 features based on lesion shape, texture, and enhancement kinetics were computed, and the 13 best features were selected and used as inputs to a backpropagation neural network (BNN). The BNN was trained and tested using the leave-one-out method on 80 BMRI lesions (37 benign, 43 malignant). Lesion histopathology was used as the reference standard. Five human readers classified the 80 lesions first without and then with CAD assistance. The performance of the computer classifier and the human readers was assessed using receiver operating characteristic curves; the performance of the human readers was also evaluated using multireader multicase (MRMC) analysis. Results:The performance of the human readers significantly improved when aided by the CAD system (P Ͻ 0.05). MRMC analysis showed that human reader performance with and without CAD system assistance can be generalized to the population of cases (P Ͻ 0.001). Conclusion:A CAD system based on lesion morphology and enhancement kinetics can improve the performance of human readers in classifying lesions on breast MRI. BREAST CANCER is the most common cancer among women (excluding nonmelanoma skin cancer) and the second leading cause of cancer deaths in women after lung cancer (1). Breast MRI (BMRI) has emerged as a promising technique for detecting, diagnosing, and staging breast cancer. BMRI boasts a high sensitivity for detecting enhancing malignant lesions, but its specificity is less favorable (2). Computer-aided diagnosis (CAD) systems offer an intriguing approach to reducing the high false-positive rate and improving the clinical utility of BMRI.Mammography CAD systems have been developed for detecting breast tumors (3,4) and microcalcifications (5). Many classification methods have been developed and applied to characterize mammographic breast masses as benign or malignant. These methods include wavelets (6), fractals (7), statistical methods (8), visionbased methods (9) and, recently, artificial neural networks (ANN) (3-5,10 -16).ANNs have been used to detect and classify microcalcifications (11,12,17) and breast masses (4,14 -16,18 -22) in mammography. However, CAD systems for BMRI are still limited. Starita et al (2) described a web-based CAD system for the analysis of suspected BMRI lesions. They evaluated the performance of their system on 20 MR data sets, and classified the suspected lesions into five different types: benign lesions, intermediate cases with no clear decision (benign vs. malignant), malignant lesions, veins (usually rejected), and unknown regions (vein or lesion). Starita et al (2) reported 11 true-positives out of 12, and five true-negatives out of seven using their CAD classification system. Penn et al (23) described the first and second revisions of a BMRI CAD sys...

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<title>Preliminary performance analysis of breast MRI CAD system</title>

Cited by 6 publications

References 5 publications

Diagnostic Architectural and Dynamic Features at Breast MR Imaging: Multicenter Study

Diagnostic Architectural and Dynamic Features at Breast MR Imaging: Multicenter Study

Potential of computer‐aided diagnosis of high spectral and spatial resolution (HiSS) MRI in the classification of breast lesions

Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer‐aided diagnosis (CAD) system

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