http://ieeexplore.ieee.org/iel5/5/31047/01444179.pdf

Fundamentals of acoustics

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

A review of the development and implementation of a 4-year medical student integrated ultrasound curriculum is presented. Multiple teaching and assessment modalities are discussed as well as results from testing and student surveys. Lessons learned while establishing the curriculum are summarized. It is concluded that ultrasound is a well received, valuable teaching tool across all 4 years of medical school, and students learn ultrasound well, and they feel their ultrasound experience enhances their medical education.

/pdf/an-integrated-ultrasound-curriculum-iusc-for-medical-g6vtaudgd0.pdf

An integrated ultrasound curriculum (iUSC) for medical students: 4-year experience.

Objective The purpose of this pilot project was to train medical students in sonography. Methods Thirty-three medical students participated in a pilot sonography course, which included exposure to ultrasound physics, knobology of a compact ultrasound scanner, training in scanning and anatomy of the aorta and right kidney, and reading assignments in these areas. Pretraining and posttraining examinations were given in these areas to analyze the degree of knowledge gained by these methods. Results Nearly all of the medical students increased their basic knowledge of sonography and improved their scanning skills. The improvement was statistically significant in all areas. Conclusions Training in sonography for medical students could be used as a foundation for later, more specialty-specific training to improve the overall medical sonography skills for all physicians.

Teaching medical students diagnostic sonography.

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.

Early diagnosis of triple-negative (TN) breast cancer is important due to its aggressive biological characteristics, poor clinical outcomes, and limited options for therapy. The goal of this study is to evaluate the potential of machine learning with quantitative ultrasound image features for the diagnosis of TN breast cancer. Ultrasonic and clinical data of 140 surgically confirmed breast cancer cases were analyzed retrospectively for the diagnosis of TN and non-TN (NTN) subtypes. The subtypes were classified based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2). Ultrasound image features were measured from the grayscale and color Doppler images and used with logistic regression for classification by machine learning. Leave-one-out cross validation was used to train and test the differentiation. Diagnostic performance was measured by the area under receiver operating characteristic (ROC) curve, and sensitivity and specificity determined at the Youdons index. Of the twelve grayscale and Doppler features measured, eight were found to be statistically different for the TN and NTN subtypes (p < 0.05). The area under the ROC curve (AUC) of the statistically significant grayscale (GS) and color Doppler (CD) features was 0.85 and 0.65, respectively. The AUC increased to 0.88 when the GS and CD features were used together, with sensitivity of 86.96% and specificity of 82.91%. Consideration of patient age in the analysis did not improve discrimination of TN and NTN. The analysis of breast ultrasound images by machine learning achieves high level of differentiation between the TN and NTN subtypes, exceeding the diagnostic performance by standard visual assessments of the images.

Machine learning for diagnostic ultrasound of triple-negative breast cancer

Objective. This study investigated the use of ultrasound image analysis in quantifying temperature changes in tissue, both ex vivo and in vivo, undergoing local hyperthermia. Methods. Temperature estimation is based on the thermal dependence of the acoustic speed in a heated medium. Because standard beam-forming algorithms on clinical ultrasound scanners assume a constant acoustic speed, temperature-induced changes in acoustic speed produce apparent scatterer displacements in B-mode images. A cross-correlation algorithm computes axial speckle pattern displacement in B-mode images of heated tissue, and a theoretically derived temperature-displacement relationship is used to generate maps of temperature changes within the tissue. Validation experiments were performed on excised tissue and in murine subjects, wherein low-intensity ultrasound was used to thermally treat tissue for several minutes. Diagnostic temperature estimation was performed using a linear array ultrasound transducer, while a fine-wire thermocouple invasively measured the temperature change. Results. Pearson correlations ± SDs between the image-derived and thermocouple-measured temperature changes were R 2 = 0.923 ± 0.066 for 4 thermal treatments of excised bovine muscle tissue and R 2 = 0.917 ± 0.036 for 4 treatments of in vivo murine tumor tissue. The average differences between the two temperature measurements were 0.87°C ± 0.72°C for ex vivo studies and 0.97°C ± 0.55°C for in vivo studies. Maps of the temperature change distribution in tissue were generated for each experiment. Conclusions. This study demonstrates that velocimetric measurement on B-mode images has potential to assess temperature changes noninvasively in clinical applications.

In Vivo Noninvasive Temperature Measurement by B-Mode Ultrasound Imaging

Color Doppler is used in the clinic for visually assessing the vascularity of breast masses on ultrasound, to aid in determining the likelihood of malignancy. In this study, quantitative color Doppler radiomics features were algorithmically extracted from breast sonograms for machine learning, producing a diagnostic model for breast cancer with higher performance than models based on grayscale and clinical category from the Breast Imaging Reporting and Data System for ultrasound (BI-RADSUS). Ultrasound images of 159 solid masses were analyzed. Algorithms extracted nine grayscale features and two color Doppler features. These features, along with patient age and BI-RADSUS category, were used to train an AdaBoost ensemble classifier. Though training on computer-extracted grayscale features and color Doppler features each significantly increased performance over that of models trained on clinical features, as measured by the area under the receiver operating characteristic (ROC) curve, training on both color Doppler and grayscale further increased the ROC area, from 0.925 ± 0.022 to 0.958 ± 0.013. Pruning low-confidence cases at 20% improved this to 0.986 ± 0.007 with 100% sensitivity, whereas 64% of the cases had to be pruned to reach this performance without color Doppler. Fewer borderline diagnoses and higher ROC performance were both achieved for diagnostic models of breast cancer on ultrasound by machine learning on color Doppler features.

https://www.mdpi.com/2075-4418/10/9/631/pdf

Theodore W. Cary

Papers

Teaching medical students diagnostic sonography.

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

Machine learning for diagnostic ultrasound of triple-negative breast cancer

In Vivo Noninvasive Temperature Measurement by B-Mode Ultrasound Imaging

Color Doppler Ultrasound Improves Machine Learning Diagnosis of Breast Cancer.