Nonpalpable breast lesions: accuracy of prebiopsy mammographic diagnosis.

Hermann, George; Janus, Cynthia; Schwartz, I S; Krivisky, B; Bier, Simone; Rabinowitz, J G

doi:10.1148/radiology.165.2.3659350

Cited by 54 publications

(14 citation statements)

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“…It is reported that usually less than 30% of all breast biopsies actually show a malignant pathology. [3][4][5][6][7] The high number of unnecessary breast biopsies causes major mental and physical discomfort for the patients as well as unnecessary expenses spent for examinations. Therefore, computer-aided diagnosis ͑CADx͒ systems have been proposed in the past years with the aim to support radiologists in the discrimination of benign and malignant mammographic lesions and to increase the positive predictive value ͑PPV͒ of mammogram interpretation.…”

Section: Introductionmentioning

confidence: 99%

CADx of mammographic masses and clustered microcalcifications: A review

2009

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Breast cancer is the most common type of cancer among women in the western world. While mammography is regarded as the most effective tool for the detection and diagnosis of breast cancer, the interpretation of mammograms is a difficult and error-prone task. Hence, computer aids have been developed that assist the radiologist in the interpretation of mammograms. Computer-aided detection (CADe) systems address the problem that radiologists often miss signs of cancers that are retrospectively visible in mammograms. Furthermore, computer-aided diagnosis (CADx) systems have been proposed that assist the radiologist in the classification of mammographic lesions as benign or malignant. While a broad variety of approaches to both CADe and CADx systems have been published in the past two decades, an extensive survey of the state of the art is only available for CADe approaches. Therefore, a comprehensive review of the state of the art of CADx approaches is presented in this work. Besides providing a summary, the goals for this article are to identify relations, contradictions, and gaps in literature, and to suggest directions for future research. Because of the vast amount of publications on the topic, this survey is restricted to the two most important types of mammographic lesions: masses and clustered microcalcifications. Furthermore, it focuses on articles published in international journals.

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Section: Introductionmentioning

confidence: 99%

CADx of mammographic masses and clustered microcalcifications: A review

2009

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“…At present, the positive biopsy rate for nonpalpable breast lesions as well as for nonpalpable breast masses is between 15%-30%. [1][2][3][4] This means that 70%-85% of breast biopsies are performed for benign lesions. In order to reduce patient anxiety and morbidity, as well as to decrease health care costs, it is desirable to reduce the number of benign biopsies without missing malignancies.…”

Section: Introductionmentioning

confidence: 99%

Computerized characterization of breast masses on three‐dimensional ultrasound volumes

et al. 2004

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We are developing computer vision techniques for the characterization of breast masses as malignant or benign on radiologic examinations. In this study, we investigated the computerized characterization of breast masses on three-dimensional (3-D) ultrasound (US) volumetric images. We developed 2-D and 3-D active contour models for automated segmentation of the mass volumes. The effect of the initialization method of the active contour on the robustness of the iterative segmentation method was studied by varying the contour used for its initialization. For a given segmentation, texture and morphological features were automatically extracted from the segmented masses and their margins. Stepwise discriminant analysis with the leave-one-out method was used to select effective features for the classification task and to combine these features into a malignancy score. The classification accuracy was evaluated using the area Az under the receiver operating characteristic (ROC) curve, as well as the partial area index Az(0.9), defined as the relative area under the ROC curve above a sensitivity threshold of 0.9. For the purpose of comparison with the computer classifier, four experienced breast radiologists provided malignancy ratings for the 3-D US masses. Our dataset consisted of 3-D US volumes of 102 biopsied masses (46 benign, 56 malignant). The classifiers based on 2-D and 3-D segmentation methods achieved test Az values of 0.87+/-0.03 and 0.92+/-0.03, respectively. The difference in the Az values of the two computer classifiers did not achieve statistical significance. The Az values of the four radiologists ranged between 0.84 and 0.92. The difference between the computer's Az value and that of any of the four radiologists did not achieve statistical significance either. However, the computer's Az(0.9) value was significantly higher than that of three of the four radiologists. Our results indicate that an automated and effective computer classifier can be designed for differentiating malignant and benign breast masses on 3-D US volumes. The accuracy of the classifier designed in this study was similar to that of experienced breast radiologists.

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“…In three studies, it was found that only 20%-30% of mammographically suspicious nonpalpable breast masses that underwent biopsy were malignant. [1][2][3] In order to reduce costs and patient discomfort, it is important to reduce the number of benign biopsies without missing any malignant masses. Computer-aided diagnosis has the potential to assist the radiologists in the characterization of mammographic masses.…”

Section: Introductionmentioning

confidence: 99%

Improvement of mammographic mass characterization using spiculation measures and morphological features

et al. 2001

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We are developing new computer vision techniques for characterization of breast masses on mammograms. We had previously developed a characterization method based on texture features. The goal of the present work was to improve our characterization method by making use of morphological features. Toward this goal, we have developed a fully automated, three-stage segmentation method that includes clustering, active contour, and spiculation detection stages. After segmentation, morphological features describing the shape of the mass were extracted. Texture features were also extracted from a band of pixels surrounding the mass. Stepwise feature selection and linear discriminant analysis were employed in the morphological, texture, and combined feature spaces for classifier design. The classification accuracy was evaluated using the area Az under the receiver operating characteristic curve. A data set containing 249 films from 102 patients was used. When the leave-one-case-out method was applied to partition the data set into trainers and testers, the average test Az for the task of classifying the mass on a single mammographic view was 0.83 +/- 0.02, 0.84 +/- 0.02, and 0.87 +/- 0.02 in the morphological, texture, and combined feature spaces, respectively. The improvement obtained by supplementing texture features with morphological features in classification was statistically significant (p = 0.04). For classifying a mass as malignant or benign, we combined the leave-one-case-out discriminant scores from different views of a mass to obtain a summary score. In this task, the test Az value using the combined feature space was 0.91 +/- 0.02. Our results indicate that combining texture features with morphological features extracted from automatically segmented mass boundaries will be an effective approach for computer-aided characterization of mammographic masses.

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Nonpalpable breast lesions: accuracy of prebiopsy mammographic diagnosis.

Cited by 54 publications

References 0 publications

CADx of mammographic masses and clustered microcalcifications: A review

CADx of mammographic masses and clustered microcalcifications: A review

Computerized characterization of breast masses on three‐dimensional ultrasound volumes

Improvement of mammographic mass characterization using spiculation measures and morphological features

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