Automated breast cancer detection and classification using ultrasound images: A survey

We present the first demonstration of in vivo collection of Raman spectra of breast tissue. Raman spectroscopy, which analyzes molecular vibrations, is a promising new technique for the diagnosis of breast cancer. We have collected 31 Raman spectra from nine patients undergoing partial mastectomy procedures to show the feasibility of in vivo Raman spectroscopy for intraoperative margin assessment. The data was fit with an established model, resulting in spectral-based tissue characterization in only 1 second. Application of our previously developed diagnostic algorithm resulted in perfect sensitivity and specificity for distinguishing cancerous from normal and benign tissues in our small data set. Significantly, we have detected a grossly invisible cancer that, upon pathologic review, required the patient to undergo a second surgical procedure. Had Raman spectroscopy been used in a real-time fashion to guide tissue excision during the procedure, the additional reexcision surgery might have been avoided. These preliminary findings suggest that Raman spectroscopy has the potential to lessen the need for reexcision surgeries resulting from positive margins and thereby reduce the recurrence rate of breast cancer following partial mastectomy surgeries.

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In vivo Margin Assessment during Partial Mastectomy Breast Surgery Using Raman Spectroscopy

Textbook of Cancer Epidemiology

Article history: Received 9 September 2008 Received in revised form 16 April 2009 Accepted 14 May 2009

Automated breast cancer detection and classification using ultrasound images A survey

Fully automatic and segmentation-robust classification of breast tumors based on local texture analysis of ultrasound images

Objective. To evaluate the role of quantitative margin features in the computer-aided diagnosis of malignant and benign solid breast masses using sonographic imaging. Methods. Sonographic images from 56 patients with 58 biopsy-proven masses were analyzed quantitatively for the following features: margin sharpness, margin echogenicity, and angular variation in margin. Of the 58 masses, 38 were benign and 20 were malignant. Each feature was evaluated individually and in combination with the others to determine its association with malignancy. The combination of features yielding the highest association with malignancy was analyzed by logistic regression to determine the probability of malignancy. The performance of the probability measurements was evaluated by receiver operating characteristic analysis using a round-robin technique. Results. Margin sharpness, margin echogenicity, and angular variation in margin were significantly different for the malignant and benign masses (P < .03, 2-tailed Student t test). According to quantitative measures, tumor-tissue margins of the malignant masses were less distinct than for the benign masses. Although the mean size of the lesions for the two groups was the same, the mean age of the patients was statistically different (P = .000625). After logistic regression analysis, the individual features age, margin sharpness, margin echogenicity, and angular variation in margin were found to be associated with the probability of malignancy (P < .03). The area under the receiver operating characteristic curve ± SD for the 3-feature logistic regression model combining age, margin echogenicity, and angular variation of margin was 0.87 ± 0.05. Conclusions. The proposed quantitative margin features are robust and can reliably measure margin distinctiveness. These features combined with logistic regression analysis can be useful for

Computer-based margin analysis of breast sonography for differentiating malignant and benign masses.

Various features based on qualitative description of shape, contour, margin and echogenicity of solid breast nodules are used clinically to classify them as benign or malignant. However, there continues to be considerable overlap in the sonographic findings for the two types of lesions. This is related to the lack of precise definition of the various features as well as to the lack of agreement among observers, among other factors. The goal of this investigation is to define clinical features quantitatively and evaluate if they differ significantly in malignant and benign cases. Features based on margin sharpness and continuity, shadowing, and attenuation were defined and calculated from the images. These features were tested on digital phantoms. Following the evaluation, the features were measured on 116 breast sonograms of 58 biopsy-proven masses. Biopsy had been recommended for all of these breast lesions based on physical exams and conventional diagnostic imaging of ultrasound and/or mammography. Of the 58 masses, 20 were identified as malignant and 38 as benign histologically. Margin sharpness, margin echogenicity, and angular margin variation were significantly different for the two groups (p<0.03, two-tailed student t-test). Shadowing and attenuation of ultrasound did not show significant difference. The results of this preliminary study show that quantitative margin characteristics measured for the malignant and benign masses from the ultrasound images are different and could potentially be useful in identifying a subgroup of solid breast nodules that have low risk of being malignant.

Sarah A. Kangas

Papers

Computer-based margin analysis of breast sonography for differentiating malignant and benign masses.

Quantitative description of solid breast nodules by ultrasound imaging