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To assess the ability of the pressure gradient between breast lesions and adjacent normal tissue estimated by 3D subharmonic-aided pressure estimation (SHAPE) to characterize indeterminate breast lesions. This prospective study enrolled patients scheduled for ultrasound-guided needle biopsies of a breast lesion. Before the biopsy, 3D SHAPE data were collected from the breast lesion during the infusion of an ultrasound contrast agent (Definity) as well as after clearance of the agent. Direct, invasive pressure measurements in the lesion and adjacent normal tissue were then obtained using an intracompartmental pressure monitoring system (C2DX) before tissue sampling as part of the biopsy procedure. The mean SHAPE gradient and invasive measurement gradient between the lesion and adjacent normal tissue were compared to the biopsy results. The SHAPE gradients were also compared to the invasive pressure gradients. There were 8 malignant and 13 benign lesions studied. The SHAPE gradients and invasive pressure gradients were significantly different between the benign and malignant lesions (2.86 ± 3.24 vs. -0.03 ± 1.72 a.u.; p = 0.03 and 9.9 ± 8.5 vs. 20.9 ± 8.0 mmHg; p = 0.008, respectively). The area under the curves, specificities, and sensitivities for detecting malignancy by SHAPE gradients and invasive pressure gradients were 0.79 and 0.88, 77% and 92%, and 88% and 50%, respectively. A weak negative correlation was found between the SHAPE and invasive pressure gradients (r = -0.2). The pressure gradient between a breast lesion and adjacent normal tissue estimated by 3D SHAPE shows potential for characterizing indeterminate breast lesions. [ABSTRACT FROM AUTHOR]

A portion of detected breast masses might be overrated by using the Breast Imaging-Reporting and Data System ultrasonography (BI-RADS US) lexicon. A principal component regression-based contrast-enhanced ultrasound (PCR-CEUS) evaluation system was built to quantitatively illustrate whether CEUS could help radiologists to differentiate 4A masses. The PCR-CEUS evaluation system, based on principal component analysis (PCA) and logistic regression, was verified by random assignment into training and test sets and shown to reduce the data dimension and avoid collinearity in CEUS variables. This prospective study consecutively collected 238 patients with 238 4A masses confirmed pathologically. All enrolled patients accepted CEUS examination. The diagnostic performance of senior and junior radiologists, PCR-CEUS and combined methods was compared. The PCR-CEUS system had consistent diagnostic performance in both the training and test sets, with an area under the curve (AUC) of 0.831 (0.765-0.897), 0.798 (0.7034-0.892) and 0.854 (0.765-0.943) (all P > 0.05). The AUC of the combined diagnostic model (PCR-CEUS + Senior radiologists) was higher than that of senior radiologists, and the combined model had higher sensitivity (0.875 (0.781-0.969) vs. 0.729 (0.603-0.855)) without compromising specificity. Furthermore, the AUC and specificity of the combined model (PCR-CEUS + Junior radiologists) (0.852 (0.787-0.916)) was higher than that of junior radiologists (0.665 (0.592-0.737) (P < 0.00001)). PCR-CEUS demonstrated good ability in differentiating malignant BI-RADS-US 4A masses and was helpful for both senior and junior radiologists. [ABSTRACT FROM AUTHOR]

To explore the value of hand-held ultrasound (HHUS) for diagnosing complex cystic and solid breast lesions, 472 pathologically proven lesions were analyzed. The lesions were divided into four types based on ultrasound features. Positive predictive values (PPVs) for lesion types and risk factor performances were assessed. Furthermore, HHUS and mammography (MAM) performances were compared: 27 lesions missed in MAM were detected in HHUS. Ultrasound feature analysis revealed higher PPVs for type III and IV lesions than for type I and II lesions. In patients older than 51 y, a type III or IV lesion with a diameter greater than 18 mm, an irregular shape, a non-parallel orientation, an uncircumscribed margin, calcification, vascularity and abnormal axillary lymph nodes were suggestive of malignancy; the area under the curve reached 0.869. Thus, ultrasound is useful in diagnosing complex cystic and solid breast lesions, which should be categorized as Breast Imaging Reporting and Data System (BI-RADS) 4B or 4C. [ABSTRACT FROM AUTHOR]

Our aim was to prospectively evaluate inter- and intra-observer agreement between Breast Imaging Reporting and Data System (BI-RADS) classifications and Tsukuba elasticity scores (TSs) of breast lesions. The study included 164 breast lesions (63 malignant, 101 benign). The BI-RADS classification and TS of each breast lesion was assessed by the examiner and twice by three reviewers at an interval of 2 months. Weighted κ values for inter-observer agreement ranged from moderate to substantial for BI-RADS classification (κ = 0.585-0.738) and was substantial for TS (κ = 0.608-0.779). Intra-observer agreement was almost perfect for ultrasound (US) BI-RADS (κ = 0.847-0.872) and TS (κ = 0.879-0.914). Overall, individual reviewers are highly self-consistent (almost perfect intra-observer agreement) with respect to BI-RADS classification and TS, whereas inter-observer agreement was moderate to substantial. Comprehensive training is essential for achieving high agreement and minimizing the impact of subjectivity. Our results indicate that breast US and real-time elastography can achieve high diagnostic performance. [ABSTRACT FROM AUTHOR]

Our aim was to compare the inter-observer variability and diagnostic performance of the Breast Imaging Reporting and Data System (BI-RADS) lexicon for breast ultrasound of static and video images. Ninety-nine breast masses visible on ultrasound examination from 95 women 19-81 y of age at five institutions were enrolled in this study. They were scheduled to undergo biopsy or surgery or had been stable for at least 2 y of ultrasound follow-up after benign biopsy results or typically benign findings. For each mass, representative long- and short-axis static ultrasound images were acquired; real-time long- and short-axis B-mode video images through the mass area were separately saved as cine clips. Each image was reviewed independently by five radiologists who were asked to classify ultrasound features according to the fifth edition of the BI-RADS lexicon. Inter-observer variability was assessed using kappa (κ) statistics. Diagnostic performance on static and video images was compared using the area under the receiver operating characteristic curve. No significant difference was found in κ values between static and video images for all descriptors, although κ values of video images were higher than those of static images for shape, orientation, margin and calcifications. After receiver operating characteristic curve analysis, the video images (0.83, range: 0.77-0.87) had higher areas under the curve than the static images (0.80, range: 0.75-0.83; p = 0.08). Inter-observer variability and diagnostic performance of video images was similar to that of static images on breast ultrasonography according to the new edition of BI-RADS. [ABSTRACT FROM AUTHOR]

This work identifies effective computable features from the Breast Imaging Reporting and Data System (BI-RADS), to develop a computer-aided diagnosis (CAD) system for breast ultrasound. Computerized features corresponding to ultrasound BI-RADs categories were designed and tested using a database of 283 pathology-proven benign and malignant lesions. Features were selected based on classification performance using a "bottom-up" approach for different machine learning methods, including decision tree, artificial neural network, random forest and support vector machine. Using 10-fold cross-validation on the database of 283 cases, the highest area under the receiver operating characteristic (ROC) curve (AUC) was 0.84 from a support vector machine with 77.7% overall accuracy; the highest overall accuracy, 78.5%, was from a random forest with the AUC 0.83. Lesion margin and orientation were optimum features common to all of the different machine learning methods. These features can be used in CAD systems to help distinguish benign from worrisome lesions. [ABSTRACT FROM AUTHOR]

Our aim was to compare the diagnostic performance of strain elastography (SE) and shear-wave elastography (SWE), combined with B-mode ultrasonography (US), in breast cancer. For 79 breast lesions that underwent SE and SWE, two radiologists reviewed five data sets (B-mode US, SWE, SE and two combined sets). Qualitative and quantitative elastographic data and Breast Imaging Reporting and Data System (BI-RADS) categories were recorded. The area under the receiver operating characteristic curve (AUC) was evaluated. No significant difference in the AUC between the two elastography methods was noted. After subjective assessment by reviewers, the AUC for the combined sets was improved (SWE, 0.987; SE, 0.982; B-mode US, 0.970; p < 0.05). When SE and SWE were added, 38% and 56% of benign BI-RADS category 4a lesions with a low suspicion of cancer were downgraded without false-negative results, respectively. SE and SWE performed similarly. Therefore, addition of SE or SWE improved the diagnostic performance of B-mode US, potentially reducing unnecessary biopsies. [ABSTRACT FROM AUTHOR]

Abstract: We investigated the effect of using a novel segmentation algorithm on radiologists’ sensitivity and specificity for discriminating malignant masses from benign masses using ultrasound. Five-hundred ten conventional ultrasound images were processed by a novel segmentation algorithm. Five radiologists were invited to analyze the original and computerized images independently. Performances of radiologists with or without computer aid were evaluated by receiver operating characteristic (ROC) curve analysis. The masses became more obvious after being processed by the segmentation algorithm. Without using the algorithm, the areas under the ROC curve (Az) of the five radiologists ranged from 0.70∼0.84. Using the algorithm, the Az increased significantly (range, 0.79∼0.88; p < 0.001). The proposed segmentation algorithm could improve the radiologists’ diagnosis performance by reducing the image speckles and extracting the mass margin characteristics. [Copyright &y& Elsevier]

Abstract: The objective of this study was to evaluate the positive predictive value (PPV) in ultrasonographically (US)-detected breast lesions of BI-RADS category 4a, 4b and 4c and to find how various clinical factors influenced the PPV of category 4. A total of 2142 women with 2430 breast lesions diagnosed on US as BI-RADS category 4 and underwent biopsy were included. Among them, 452 (18.6%) were pathologically confirmed as malignancy. PPV of each US BI-RADS subcategory was 7.6% (149/1963) for category 4a, 37.8% (68/180) for category 4b and 81.9% (235/287) for category 4c. Several clinical factors were more significantly seen in malignancy of category 4a and 4b, whereas none of the factors showed significance in category 4c. Subcategorization of category 4 is a feasible method in predicting malignancy in which patients’ age, lack of multiplicity and symptoms affected the PPV of category 4 lesions. (E-mail: ekkim@yuhs.ac) [Copyright &y& Elsevier]

Abstract: The primary objective of this study was to review the ultrasonographic features of BI-RADS category 3 (“probably benign”) lesions that eventually proved to be malignant. A second objective was to investigate their clinical and pathologic features according to their palpability and time of biopsy. Thirty-two (0.8%) of 4000 women with lesions that were initially classified as “probably benign” proved to be malignant and formed the study group. The most common reason for a false negative assessment on ultrasound was a failure to recognize suspicious margin characteristics (28 of 32 malignancies, 87.5%). Malignancy was more frequent in palpable (2.4%, 21 of 859) than nonpalpable lesions (0.4%, 11 of 3141, p < 0.001). There was no statistical difference in the mean age, mean size of lesions, or tumor stage between patients who underwent early biopsy (n = 19) or biopsy after 6 months (n = 13). Twenty-nine of the lesions (90.6%) were retrospectively recategorized as BI-RADS 4, which calls for early biopsy. BI-RADS 3 lesions require especially careful assessment. (E-mail: ekkim@yuhs.ac) [Copyright &y& Elsevier]

Abstract: The purpose of this study was to evaluate the accuracy of neural network analysis of elastographic features at sonoelastography for the classification of biopsy-proved benign and malignant breast tumors. Sonoelastography of 181 solid breast masses (113 benign and 68 malignant tumors) was performed for 181 patients (mean age, 47 years; range, 24–75 years). After the manual segmentation of the tumors, five elastographic features (strain difference, strain ratio, mean, median and mode) and six B-mode features (orientation, undulation, angularity, average gradient, gradient variance and intensity variance) were computed. A neural network was used to classify tumors by the use of these features. The Student''s t test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. Area under ROC curve (Az) values of the three elastographic features– mean (0.87), median (0.86) and mode (0.83)–were significantly higher than the Az values for the six B-mode features (0.54–0.69) (p < 0.01). Accuracy, sensitivity, specificity and Az of the neural network for the classification of solid breast tumors were 86.2% (156/181), 83.8% (57/68), 87.6% (99/113) and 0.84 for the elastographic features, respectively, and 82.3% (149/181), 70.6% (48/68), 89.4% (101/113) and 0.78 for the B-mode features, respectively, and 90.6% (164/181), 95.6% (65/68), 87.6% (99/113) and 0.92 for the combination of the elastographic and B-mode features, respectively. We conclude that sonoelastographic images and neural network analysis of features has the potential to increase the accuracy of the use of ultrasound for the classification of benign and malignant breast tumors. (E-mail: rfchang@csie.ntu.edu.tw) [Copyright &y& Elsevier]

Abstract: We evaluated whether real-time ultrasound elastography (USE) performed in addition to conventional ultrasound (US) can improve the differentiation of benign from malignant breast lesions. Both conventional US and real-time USE were performed in 112 consecutive patients with 139 breast lesions using a Hitachi EUB-8500 US system. Each lesion was assigned an elasticity score according to the degree and distribution of strain induced manually by mild compression. The USE scores (1 to 5) were compared with the BI-RADS assessment categories (1 to 5) obtained with conventional US. Sensitivity, specificity and overall accuracy of each method were determined with surgical pathology as the gold standard. There were 70 benign and 69 malignant lesions. The mean elasticity score was significantly higher for malignant lesions than for benign lesions (4.33 ± 0.11 vs. 2.10 ± 0.13, p < 0.01). When a cutoff point of 4 was used, the sensitivity, specificity and accuracy were 85.5, 88.6 and 87% for USE and 94.2, 87.1 and 90.6% for conventional US, respectively. Of the 64 lesions assessed as BI-RADS 2 or 3(i.e., benign) based on conventional US, two were scored as 4 and 5 (i.e., malignant) using USE and were subsequently proven to be malignant. Of the 75 lesions with BI-RADS 4 or 5 category from conventional US, one was scored as a category 1 (benign) with USE and found to be benign by pathology. Our study results suggest that the addition of USE imaging to conventional US could be helpful in the detection and characterization of breast masses. (E-mail: yuxinjiangxh@yahoo.com.cn) [Copyright &y& Elsevier]

Abstract: Clinically, the ultrasound findings are evaluated by its sonographic characteristics and then assigned to assessment categories according to the definitions of Breast Imaging Reporting and Data System (BI-RADS) developed by the American College of Radiology. In this study, a computer-aided classification (CAC) system was proposed to classify the masses into assessment categories 3, 4 and 5, which simulated the clinical diagnosis of radiologists. Compared with current computer-aided diagnosis systems, the proposed CAC system classifies the indeterminate cases into BI-RADS category 4 for further diagnosis. Six hundred twenty-six cases were collected from three ultrasound systems and confirmed by pathology and retrospectively classified into categories 3, 4 and 5 by radiologists. The multinomial logistic regression model was trained as the CAC system for predicting the assessment category from the computerized BI-RADS features and from a set of machine-dependent factors. By using the machine-dependent factors to indicate the adopted ultrasound systems, the same regression model could be applied for the cases acquired from different ultrasound systems. A basic CAC system was trained by using the classification result of radiologists. A weighted CAC system, to improve the capacity of the basic CAC system in differentiating benign from malignant lesions, was trained by adding the pathologic result. Between the radiologists and the basic CAC system, a substantial agreement was indicated by Cohen’s kappa statistic and the differences in either the performance indices or the AZ of receiver operating characteristic (ROC) analysis were not statistically significant. For the weighted CAC system, the performance indices accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 73.00% (457 of 626), 98.17% (215 of 219), 59.46% (242 of 407), 56.58% (215 of 380) and 98.37% (242 of 246), respectively; the AZ was 0.94; and the correlation with the radiologists was also substantial agreement. The indices accuracy and specificity of weighted CAC system, compared with those of the radiologists, were improved by 5.91% and 8.85%, respectively and the indices of sensitivity and NPV, compared with those of a conventional CAD system, were improved by 10.5% and 5.21%, respectively; all improvements were statistically significant. To classify the mass into BI-RADS assessment categories by the CAC system is feasible. Moreover, the proposed CAC system is flexible because it can be used to diagnose the cases acquired from different ultrasound systems. (E-mail: rfchang@csie.ntu.edu.tw) [Copyright &y& Elsevier]