Breast Cancer Detection: Evaluation of a Mass-Detection Algorithm for Computer-aided Diagnosis—Experience in 263 Patients

Petrick, Nicholas; Sahiner, Berkman; Chan, Heang Ping; Helvie, Mark A.; Paquerault, Sophie; Hadjiiski, Lubomir M.

doi:10.1148/radiol.2241011062

Cited by 72 publications

(47 citation statements)

References 16 publications

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“…Various researchers have developed CAD methods for breast imaging modalities-including mammography, US, and MR imaging-and for combined modalities for the tasks of automated lesion segmentation, feature extraction, and lesion characterization (11)(12)(13)(30)(31)(32)(33)(34)(35). In addition, several observer studies of all three of these modalities have revealed the potential usefulness of CAD in clinical settings (36)(37)(38)(39)(40).…”

Section: Discussionmentioning

confidence: 99%

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Bhooshan¹,

Giger²,

Jansen³

et al. 2010

Radiology

178

155

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Purpose:To assess the performance of computer-extracted dynamic contrast material-enhanced (DCE) magnetic resonance (MR) imaging kinetic and morphologic features in the differentiation of invasive versus noninvasive breast lesions and metastatic versus nonmetastatic breast lesions. Materials and Methods:In this institutional review board-approved HIPAA-compliant study, in which the requirement for informed patient consent was waived, breast MR images were retrospectively collected. The images had been obtained with a 1.5-T MR unit by using a gadodiamide-enhanced T1-weighted spoiled gradient-recalled acquisition in the steady state sequence. The breast MR imaging database contained 132 benign, 71 ductal carcinoma in situ (DCIS), and 150 invasive ductal carcinoma (IDC) lesions. Fifty-four IDC lesions were associated with metastasis-positive lymph nodes (LNs), and 64 IDC lesions were associated with negative LNs. Lesion segmentation and extraction of morphologic and kinetic features were automatically performed by a laboratory-developed computer workstation. Features were fi rst selected by using stepwise linear discriminant analysis and then merged by using Bayesian neural networks. Lesion classifi cation performance was assessed with receiver operating characteristic analysis. Results: Conclusion:Computer-aided diagnosis of breast DCE MR imagingdepicted lesions was extended from the task of discriminating between malignant and benign lesions to the prognostic tasks of distinguishing between noninvasive and invasive lesions and discriminating between metastatic and nonmetastatic lesions, yielding MR imaging-based prognostic markers.q RSNA, 2010 Supplemental material: http://radiology.rsna.org/lookup/ suppl

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Section: Discussionmentioning

confidence: 99%

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Bhooshan¹,

Giger²,

Jansen³

et al. 2010

Radiology

178

155

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“…The 25% threshold was selected as described in our previous study. 30 To evaluate the performance of our bilateral LDA classifier, the test discriminant scores were analyzed using receiver operating characteristic (ROC) methodology. 31 The accuracy for classification of mass and normal tissue was evaluated as the area under the ROC curve, A z .…”

Section: Evaluation Methods-mentioning

confidence: 99%

Bilateral analysis based false positive reduction for computer‐aided mass detection

Wei

Hadjiiski

et al. 2007

Medical Physics

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We have developed a false positive (FP) reduction method based on analysis of bilateral mammograms for computerized mass detection systems. The mass candidates on each view were first detected by our unilateral computer-aided detection (CAD) system. For each detected object, a regional registration technique was used to define a region of interest (ROI) that is "symmetrical" to the object location on the contralateral mammogram. Texture features derived from the spatial gray level dependence matrices and morphological features were extracted from the ROI containing the detected object on a mammogram and its corresponding ROI on the contralateral mammogram. Bilateral features were then generated from corresponding pairs of unilateral features for each object. Two linear discriminant analysis (LDA) classifiers were trained from the unilateral and the bilateral feature spaces, respectively. Finally, the scores from the unilateral LDA classifier and the bilateral LDA asymmetry classifier were fused with a third LDA whose output score was used to distinguish true mass from FPs. A data set of 341 cases of bilateral two-view mammograms was used in this study, of which 276 cases with 552 bilateral pairs contained 110 malignant and 166 benign biopsy-proven masses and 65 cases with 130 bilateral pairs were normal. The mass data set was divided into two subsets for twofold cross-validation training and testing. The normal data set was used for estimation of FP rates. It was found that our bilateral CAD system achieved a case-based sensitivity of 70%, 80%, and 85% at average FP rates of 0.35, 0.75, and 0.95 FPs/image, respectively, on the test data sets with malignant masses. In comparison to the average FP rates for the unilateral CAD system of 0.58, 1.33, and 1.63, respectively, at the corresponding sensitivities, the FP rates were reduced by 40%, 44%, and 42% with the bilateral symmetry information. The improvement was statistically significance (p<0.05) as estimated by JAFROC analysis.

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“…Feature extraction and FP reduction-FP classification in our mass detection system is accomplished by a three-stage classification scheme. 36,44 For each suspicious object, eleven morphological features are extracted. Rule-based classification and a linear discriminant analysis (LDA) classifier using all 11 morphological features as input predictor variables are trained to remove the detected structures that are substantially different from breast masses.…”

Section: B Methodsmentioning

confidence: 99%

“…34 We have developed a CAD system for the detection of masses on SFMs in our previous studies. 30,35,36 We are developing a mass detection system for mammograms acquired directly by a FFDM system. In this study, we adapted our mass detection system developed for SFMs to FFDMs by optimizing each stage and retraining.…”

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confidence: 99%

Computer‐aided detection of breast masses on full field digital mammograms

et al. 2005

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We are developing a computer-aided detection (CAD) system for breast masses on full field digital mammographic (FFDM) images. To develop a CAD system that is independent of the FFDM manufacturer's proprietary preprocessing methods, we used the raw FFDM image as input and developed a multiresolution preprocessing scheme for image enhancement. A two-stage prescreening method that combines gradient field analysis with gray level information was developed to identify mass candidates on the processed images. The suspicious structure in each identified region was extracted by clustering-based region growing. Morphological and spatial gray-level dependence texture features were extracted for each suspicious object. Stepwise linear discriminant analysis (LDA) with simplex optimization was used to select the most useful features. Finally, rule-based and LDA classifiers were designed to differentiate masses from normal tissues. Two data sets were collected: a mass data set containing 110 cases of two-view mammograms with a total of 220 images, and a no-mass data set containing 90 cases of two-view mammograms with a total of 180 images. All cases were acquired with a GE Senographe 2000D FFDM system. The true locations of the masses were identified by an experienced radiologist. Free-response receiver operating characteristic analysis was used to evaluate the performance of the CAD system. It was found that our CAD system achieved a case-based sensitivity of 70%, 80%, and 90% at 0.72, 1.08, and 1.82 false positive (FP) marks/image on the mass data set. The FP rates on the no-mass data set were 0.85, 1.31, and 2.14 FP marks/image, respectively, at the corresponding sensitivities. This study demonstrated the usefulness of our CAD techniques for automated detection of masses on FFDM images.

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Breast Cancer Detection: Evaluation of a Mass-Detection Algorithm for Computer-aided Diagnosis—Experience in 263 Patients

Abstract: This mass-detection algorithm had a high sensitivity for detection of malignant masses. It may be useful as a second opinion in mammographic interpretation.

Cited by 72 publications

References 16 publications

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Cancerous Breast Lesions on Dynamic Contrast-enhanced MR Images: Computerized Characterization for Image-based Prognostic Markers

Bilateral analysis based false positive reduction for computer‐aided mass detection

Computer‐aided detection of breast masses on full field digital mammograms

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