Computer-aided lesion diagnosis in B-mode ultrasound by border irregularity and multiple sonographic features

Lee, Jong-Ha; Seong, Yeong Kyeong; Chang, Chu-Ho; Ko, Eun Young; Cho, Baek Hwan; Ku, Jeonghun; Woo, Kyoung-Gu

doi:10.1117/12.2007452

Cited by 11 publications

(8 citation statements)

References 11 publications

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“…S-Detect applies a novel feature extraction technique and support vector machine classifier that classifies breast masses into benign or malignant according to the proposed feature combinations integrated according to the US BI-RADS [8]. Features used for US feature analysis in S-Detect are as follows: shape differences, echo and texture features using spatial grey-level dependence matrices, intensity in the mass area, gradient magnitude in the mass area, orientation, depth-width ratio, distance between mass shape and best fit ellipse, average gray changes or histogram changes between tissue/mass area, comparison of gray value of left, posterior, and right under the lesion, the number of lobulated areas/protuberances/depressions, lobulation index, and elliptic-normalized circumference [8].…”

Section: Methodsmentioning

confidence: 99%

Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist

et al. 2017

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PurposeThe purpose of this study was to evaluate the diagnostic performance of S-Detect when applied to breast ultrasonography (US), and the agreement with an experienced radiologist specializing in breast imaging.MethodsFrom June to August 2015, 192 breast masses in 175 women were included. US features of the breast masses were retrospectively analyzed by a radiologist who specializes in breast imaging and S-Detect, according to the fourth edition of the American College of Radiology Breast Imaging Reporting and Data System lexicon and final assessment categories. Final assessments from S-Detect were in dichotomized form: possibly benign and possibly malignant. Kappa statistics were used to analyze the agreement between the radiologist and S-Detect. Diagnostic performance of the radiologist and S-Detect was calculated, including sensitivity, specificity, positive predictive value (PPV), negative predictive value, accuracy, and area under the receiving operator characteristics curve.ResultsOf the 192 breast masses, 72 (37.5%) were malignant, and 120 (62.5%) were benign. Benign masses among category 4a had higher rates of possibly benign assessment on S-Detect for the radiologist, 63.5% to 36.5%, respectively (P=0.797). When the cutoff was set at category 4a, the specificity, PPV, and accuracy was significantly higher in S-Detect compared to the radiologist (all P<0.05), with a higher area under the receiver operator characteristics curve of 0.725 compared to 0.653 (P=0.038). Moderate agreement (k=0.58) was seen in the final assessment between the radiologist and S-Detect.ConclusionS-Detect may be used as an additional diagnostic tool to improve the specificity of breast US in clinical practice, and guide in decision making for breast masses detected on US.

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Section: Methodsmentioning

confidence: 99%

Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist

et al. 2017

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“…In this system (S-Detect), the final assessment classification was divided into 'possibly benign' and 'possibly malignant'. The CAD program applies a novel feature extraction technique and support vector machine classifier that classifies breast lesions as benign or malignant according to the US BI-RADS lexicons (15). The result of this analysis was defined as CAD.…”

Section: Imaging Analyses and Management Planningmentioning

confidence: 99%

Comparison of the Diagnostic Performance of Breast Ultrasound and CAD Using BI-RADS Descriptors and Quantitative Variables

et al. 2018

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Background: Computer-aided diagnosis (CAD) as an adjunct to breast ultrasonography (US) has been extensively developed using different breast imaging techniques to help improve radiologists' diagnostic performance, particularly in distinguishing malignant from benign breast lesions. Additionally, quantitative variables can be obtained using CAD. Objectives: The aim of this study was to evaluate the usefulness of CAD as an adjunct to breast US by exploring the diagnostic performance of US and CAD using the breast imaging reporting and data system (BI-RADS) descriptors and quantitative variables. Patients and Methods: Between October 2015 and December 2016, we performed breast US with CAD (S-Detect) for screening or diagnostic purposes. We assessed the diagnostic performance according to the BI-RADS descriptors, quantitative variables (width, height, height/width (H/W) ratio, area, and depth), and combined results. We analyzed the agreement in the descriptors between US and CAD. Results: Four hundred thirty-two women with 521 breast lesions were enrolled. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of US vs. CAD were 0.82 vs. 0.78, 95% vs. 78%, and 69% vs. 78%, respectively. When CAD was combined with breast US, the specificity was significantly improved from 69% to 72% (P < 0.05). Among all descriptors and quantitative variables, the height and H/W ratio exhibited the greatest AUC (0.76 and 0.75), and the H/W ratio had the highest sensitivity (90%). According to the concordance analysis, the orientation exhibited the greatest agreement (k = 0.57). Conclusion: Using CAD, we could accurately determine the BI-RADS descriptors, quantitative variables and improve the specificity. Additionally, the orientation and H/W ratio are consistent key factors for both US and CAD.

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“…This form of the disease is the leading killer of women between 40 and 55 years old and is the second leading cause of death overall in women [1]. Due to this, screening techniques allowing early detection and diagnosis have been studied in order to increase the chances of survival using less aggressive treatment [2, 3].…”

Section: Introductionmentioning

confidence: 99%

“…However, this procedure is less effective when investigating dense breasts due to relatively high false negative rates [1]. Moreover, the number of unnecessary biopsies is very large and can lead to changes in the parenchyma making it difficult to read subsequent mammographic images [2].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, ultrasonography has proven a valuable technique used as an adjunct to conventional mammography for the detection and classification of breast lesions [1]. This procedure has been used to obtain additional diagnostic information, in order to reduce the number of unnecessary biopsies and assist with more accuracy the diagnosis of simple cysts (around 96–100% of efficacy when both of these techniques are used together) [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images

Marcomini

Carneiro

Schiabel

2016

International Journal of Biomedical Imaging

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This research presents a methodology for the automatic detection and characterization of breast sonographic findings. We performed the tests in ultrasound images obtained from breast phantoms made of tissue mimicking material. When the results were considerable, we applied the same techniques to clinical examinations. The process was started employing preprocessing (Wiener filter, equalization, and median filter) to minimize noise. Then, five segmentation techniques were investigated to determine the most concise representation of the lesion contour, enabling us to consider the neural network SOM as the most relevant. After the delimitation of the object, the most expressive features were defined to the morphological description of the finding, generating the input data to the neural Multilayer Perceptron (MLP) classifier. The accuracy achieved during training with simulated images was 94.2%, producing an AUC of 0.92. To evaluating the data generalization, the classification was performed with a group of unknown images to the system, both to simulators and to clinical trials, resulting in an accuracy of 90% and 81%, respectively. The proposed classifier proved to be an important tool for the diagnosis in breast ultrasound.

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Computer-aided lesion diagnosis in B-mode ultrasound by border irregularity and multiple sonographic features

Cited by 11 publications

References 11 publications

Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist

Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist

Comparison of the Diagnostic Performance of Breast Ultrasound and CAD Using BI-RADS Descriptors and Quantitative Variables

Application of Artificial Neural Network Models in Segmentation and Classification of Nodules in Breast Ultrasound Digital Images

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