Computer-aided Diagnosis of Breast Elastography

Ultrasonography has been an important imaging technique for detecting breast tumors. As opposed tothe conventional B-mode image, the real-time tissue elastography by ultrasound is a new technique for imagingthe elasticity and applied to detect the stiffness of tissues. The red region of color elastography indicatesthe soft tissue and the blue one indicates the hard tissue. The harder tissue usually is classified as malignancy.In this paper, the authors proposed a computer-aided diagnosis( CAD) system on elastography tomeasure whether this system is effective and accurate to classify the tumor into benign and malignant. Accordingto the features of elasticity, the color elastography was transferred to hue, saturation, and value(HSV) color space and extracted meaningful features from hue images. Then the neural network was utilizedin multiple features to distinguish tumors. In this experiment, there are 180 pathology-proven cases including113 benign and 67 malignant cases used to examine the classification. The results of the proposedsystem showed an accuracy of 83.89 %, a sensitivity of 82.09 % and a specificity of 84.96 %. Compared withthe physician\u27s diagnosis, an accuracy of 78.33 %, a sensitivity of 53.73 % and a specificity of 92.92 %, theproposed CAD system had better performance. Moreover, the agreement of the proposed CAD system andthe physician\u27s diagnosis was calculated by kappa statistics, the kappa 0.64 indicated there is a fair agreementof observers

Biomechanical Modeling and Inverse Problem Based Elasticity Imaging for Prostate Cancer Diagnosis

Early detection of prostate cancer plays an important role in successful prostate cancer treatment. This requires screening the prostate periodically after the age of 50. If screening tests lead to prostate cancer suspicion, prostate needle biopsy is administered which is still considered as the clinical gold standard for prostate cancer diagnosis. Given that needle biopsy is invasive and is associated with issues including discomfort and infection, it is desirable to develop a prostate cancer diagnosis system that has high sensitivity and specificity for early detection with a potential to improve needle biopsy outcome. Given the complexity and variability of prostate cancer pathologies, many research groups have been pursuing multi-parametric imaging approach as no single modality imaging technique has proven to be adequate. While imaging additional tissue properties increases the chance of reliable prostate cancer detection and diagnosis, selecting an additional property needs to be done carefully by considering clinical acceptability and cost. Clinical acceptability entails ease with respect to both operating by the radiologist and patient comfort. In this work, effective tissue biomechanics based diagnostic techniques are proposed for prostate cancer assessment with the aim of early detection and minimizing the numbers of prostate biopsies. The techniques take advantage of the low cost, widely available and well established TRUS imaging method. The proposed techniques include novel elastography methods which were formulated based on an inverse finite element frame work. Conventional finite element analysis is known to have high computational complexity, hence computation time demanding. This renders the proposed elastography methods not suitable for real-time applications. To address this issue, an accelerated finite element method was proposed which proved to be suitable for prostate elasticity reconstruction. In this method, accurate finite element analysis of a large number of prostates undergoing TRUS probe loadings was performed. Geometry input and displacement and stress fields output obtained from the analysis were used to train a neural network mapping function to be used for elastopgraphy imaging of prostate cancer patients. The last part of the research presented in this thesis tackles an issue with the current 3D TRUS prostate needle biopsy. Current 3D TRUS prostate needle biopsy systems require registering preoperative 3D TRUS to intra-operative 2D TRUS images. Such image registration is time-consuming while its real-time implementation is yet to be developed. To bypass this registration step, concept of a robotic system was proposed which can reliably determine the preoperative TRUS probe position relative to the prostate to place at the same position relative to the prostate intra-operatively. For this purpose, a contact pressure feedback system is proposed to ensure similar prostate deformation during 3D and 2D image acquisition in order to bypass the registration step

Breast Ultrasound Past, Present, and Future

This chapter will review the utilization of breast ultrasound for screening and diagnostic purposes. Currently, ultrasound is primarily used to investigate palpable lesions in women less than 30 years old, to provide further characterization of abnormal mammographic findings, and to guide invasive breast interventions. Innovations in ultrasound technology have improved the detection and diagnosis of breast cancer. Computer-aided detection (CAD), elastography, quantitative breast ultrasound technology, and ultrasound contrast agents (microbubbles) were developed to improve diagnostic accuracy. These advancements have the potential to impact overall survival by detecting cancers that are smaller and less aggressive

Evaluation of a Computer-Aided Diagnosis System in the Classification of Lesions in Breast Strain Elastography Imaging

Purpose: Evaluation of the performance of a computer-aided diagnosis (CAD) system based on the quantified color distribution in strain elastography imaging to evaluate the malignancy of breast tumors. Methods: The database consisted of 31 malignant and 52 benign lesions. A radiologist who was blinded to the diagnosis performed the visual analysis of the lesions. After six months with no eye contact on the breast images, the same radiologist and other two radiologists manually drew the contour of the lesions in B-mode ultrasound, which was masked in the elastography image. In order to measure the amount of hard tissue in a lesion, we developed a CAD system able to identify the amount of hard tissue, represented by red color, and quantify its predominance in a lesion, allowing classification as soft, intermediate, or hard. The data obtained with the CAD system were compared with the visual analysis. We calculated the sensitivity, specificity, and area under the curve (AUC) for the classification using the CAD system from the manual delineation of the contour by each radiologist. Results: The performance of the CAD system for the most experienced radiologist achieved sensitivity of 70.97%, specificity of 88.46%, and AUC of 0.853. The system presented better performance compared with his visual diagnosis, whose sensitivity, specificity, and AUC were 61.29%, 88.46%, and 0.829, respectively. The system obtained sensitivity, specificity, and AUC of 67.70%, 84.60%, and 0.783, respectively, for images segmented by Radiologist 2, and 51.60%, 92.30%, and 0.771, respectively, for those segmented by the Resident. The intra-class correlation coefficient was 0.748. The inter-observer agreement of the CAD system with the different contours was good in all comparisons. Conclusions: The proposed CAD system can improve the radiologist performance for classifying breast masses, with excellent inter-observer agreement. It could be a promising tool for clinical use

Which supplementary imaging modality should be used for breast ultrasonography? Comparison of the diagnostic performance of elastography and computer-aided diagnosis

PURPOSE: The aim of this study was to evaluate and compare the diagnostic performance of grayscale ultrasonography (US), US elastography, and US computer-aided diagnosis (US-CAD) in the differential diagnosis of breast masses. METHODS: A total of 193 breast masses in 175 consecutive women (mean age, 46.4 years) from June to August 2015 were included. US and elastography images were obtained and recorded. A US-CAD system was applied to the grayscale sonograms, which were automatically analyzed and visualized in order to generate a final assessment. The final assessments of breast masses were based on the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) categories, while elasticity scores were assigned using a 5-point scoring system. The diagnostic performance of grayscale US, elastography, and US-CAD was calculated and compared. RESULTS: Of the 193 breast masses, 120 (62.2%) were benign and 73 (37.8%) were malignant. Breast masses had significantly higher rates of malignancy in BI-RADS categories 4c and 5, elastography patterns 4 and 5, and when the US-CAD assessment was possibly malignant (all P<0.001). Elastography had higher specificity (40.8%, P=0.042) than grayscale US. US-CAD showed the highest specificity (67.5%), positive predictive value (PPV) (61.4%), accuracy (74.1%), and area under the curve (AUC) (0.762, all P<0.05) among the three diagnostic tools. CONCLUSION: US-CAD had higher values for specificity, PPV, accuracy, and AUC than grayscale US or elastography. Computer-based analysis based on the morphologic features of US may be very useful in improving the diagnostic performance of breast US.ope

Can New Ultrasound Imaging Techniques Improve Breast Lesion Characterization? Prospective Comparison between Ultrasound BI-RADS and Semi-Automatic Software “SmartBreast”, Strain Elastography, and Shear Wave Elastography

Background: Ultrasound plays a crucial role in early diagnosis of breast cancer. The aim of this research is to evaluate the diagnostic performance of BI-RADS classification in comparison with new semi-automatic software Resona R9, Mindray, “SmartBreast” and strain elastography (SE), point shear wave (pSWE), and 2D shear wave (2D SWE) Elastography for breast lesion differentiation. Methods: Ninety-two breast nodules classified according to BI-RADS lexicon by an expert radiologist were evaluated by a second investigator with B-mode ultrasound, color Doppler, “SmartBreast”, and elastography. Histopathology was considered the gold standard. Results: The agreement between software and investigator was excellent in the identification of the posterior features of breast masses (Cohen’s k = 0.94), good for shape and vascular signal (Cohen’s k, respectively, of 0.6 and 0.65), poor for orientation, margins, and echo pattern (Cohen’s k, respectively, of 0.28, 0.33 and 0.48), moderate for dimensions (Lin’s correlation coefficient of 0.90, p = 0.07). SE showed a greater area under curve (AUC) than pSWE and 2D SWE (0.84, 0.64, and 0.61, respectively), with a greater specificity and a comparable sensitivity to pSWE (respectively, of 0.86 and 0.55, 0.81 and 0.84). Conclusions: SE improved the diagnostic performance of BI-RADS classification more than pSWE and 2D SWE; “SmartBreast” showed good agreement only for shape and vascularization but not for the other ultrasound features of breast lesions