Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier

While pap test is the most common diagnosis methods for cervical cancer, their results are highly dependent on the ability of the cytotechnicians to detect abnormal cells on the smears using brightfield microscopy. In this paper, we propose an explainable region classifier in whole slide images that could be used by cyto-pathologists to handle efficiently these big images (100,000x100,000 pixels). We create a dataset that simulates pap smears regions and uses a loss, we call classification under regression constraint, to train an efficient region classifier (about 66.8% accuracy on severity classification, 95.2% accuracy on normal/abnormal classification and 0.870 KAPPA score). We explain how we benefit from this loss to obtain a model focused on sensitivity and, then, we show that it can be used to perform weakly supervised localization (accuracy of 80.4%) of the cell that is mostly responsible for the malignancy of regions of whole slide images. We extend our method to perform a more general detection of abnormal cells (66.1% accuracy) and ensure that at least one abnormal cell will be detected if malignancy is present. Finally, we experiment our solution on a small real clinical slide dataset, highlighting the relevance of our proposed solution, adapting it to be as easily integrated in a pathology laboratory workflow as possible, and extending it to make a slide-level prediction.     

Computerized Quantification of Ultrasonic Heterogeneity in Thyroid Nodules

To test whether computerized quantification of ultrasonic heterogeneity can be of help in the diagnosis of thyroid malignancy, we evaluated ultrasonic heterogeneity with an objective and quantitative computerized method in a prospective setting. A total of 400 nodules including 271 benign thyroid nodules and 129 malignant thyroid nodules were evaluated. Patient clinical data were collected, and the grading of heterogeneity on conventional gray-scale ultrasound images was retrospectively reviewed by a thyroid specialist. Quantification of ultrasonic heterogeneity (heterogeneity index, HI) was performed by a proprietary program implemented with methods proposed in this article. HI values differed significantly between benign and malignant nodules, diagnosed by a combination of fine-needle aspiration and surgical pathology results (p < 0.001, area under the curve = 0.714). The ultrasonic heterogeneity of these samples, as assessed by an experienced clinician, could not significantly differentiate between benign and malignant thyroid nodules. However, nodules with marked ultrasonic heterogeneity had higher HI values than nodules with homogeneous nodules. These results indicate that the new computer-aided diagnosis method for evaluation of the ultrasonic heterogeneity of thyroid nodules is an objective and quantitative method that is correlated with conventional ultrasonic heterogeneity assessment, but can better aid in the diagnosis of thyroid malignancy.     

Computerized detection and quantification of microcalcifications in thyroid nodules

To improve the ultrasonographic detection rates of thyroid cancers with microcalcifications, we propose to enhance the sensitivity of sonographic calcifications detection and to avoid interobserver variation by a computerized quantification method in a prospective setting. A total of 227 participants with 258 nodules were evaluated. Among them, two nodules were excluded for suspicious aspiration cytology results without pathologic proof. Among the remaining 256 nodules, the diagnosis of 181 nodules was verified by surgical pathology and the diagnosis of 75 was based on fine needle aspiration (FNA) biopsy results. There were 173 benign thyroid nodules and 83 malignant thyroid nodules, which included 74 papillary carcinomas. Patient clinical data were collected and the presence of calcifications on conventional gray-scale ultrasound images was retrospectively reviewed by a thyroid specialist. Quantification of cystic components and calcifications was automatically performed by a proprietary program (AmCAD-UT) implemented with methods proposed in this article. The calcification index (CI) was calculated after the cystic component was excluded. The CI between benign and malignant nodules diagnosed by combined FNA biopsy and surgical pathology results (total number, 256) showed a significant difference (p < 0.0001, AUC = 0.746). Furthermore, we excluded patients without surgical pathology results for further validation and the CI between benign and malignant nodules confirmed by pathology results (total number, 181) showed a significant difference (p < 0.0001, AUC = 0.763). To learn whether our computer program increased our diagnostic capabilities, we analyzed human investigators and their abilities to detect and evaluate. In this study, calcifications were noted in 48.19% (40 of 83) of malignant thyroid nodules and in 10.98% (19 of 173) of benign nodules. This new computer-aided diagnosis method to evaluate the sonographic calcifications of thyroid nodules is a more sensitive and more objective method. It can provide better sensitivity than conventional methods in the diagnosis of thyroid malignancies containing microcalcifications.     

Yottixel – An Image Search Engine for Large Archives of Histopathology Whole Slide Images

With the emergence of digital pathology, searching for similar images in large archives has gained considerable attention. Image retrieval can provide pathologists with unprecedented access to the evidence embodied in already diagnosed and treated cases from the past. This paper proposes a search engine specialized for digital pathology, called Yottixel, a portmanteau for “one yotta pixel,” alluding to the big-data nature of histopathology images. The most impressive characteristic of Yottixel is its ability to represent whole slide images (WSIs) in a compact manner. Yottixel can perform millions of searches in real-time with a high search accuracy and low storage profile. Yottixel uses an intelligent indexing algorithm capable of representing WSIs with a mosaic of patches which are then converted into barcodes, called “Bunch of Barcodes” (BoB), the most prominent performance enabler of Yottixel. The performance of the prototype platform is qualitatively tested using 300 WSIs from the University of Pittsburgh Medical Center (UPMC) and 2,020 WSIs from The Cancer Genome Atlas Program (TCGA) provided by the National Cancer Institute. Both datasets amount to more than 4,000,000 patches of 1000 × 1000 pixels. We report three sets of experiments that show that Yottixel can accurately retrieve organs and malignancies, and its semantic ordering shows good agreement with the subjective evaluation of human observers.     

Atrial scar quantification via multi-scale CNN in the graph-cuts framework

Late gadolinium enhancement magnetic resonance imaging (LGE MRI) appears to be a promising alternative for scar assessment in patients with atrial fibrillation (AF). Automating the quantification and analysis of atrial scars can be challenging due to the low image quality. In this work, we propose a fully automated method based on the graph-cuts framework, where the potentials of the graph are learned on a surface mesh of the left atrium (LA) using a multi-scale convolutional neural network (MS-CNN). For validation, we have included fifty-eight images with manual delineations. MS-CNN, which can efficiently incorporate both the local and global texture information of the images, has been shown to evidently improve the segmentation accuracy of the proposed graph-cuts based method. The segmentation could be further improved when the contribution between the t-link and n-link weights of the graph is balanced. The proposed method achieves a mean accuracy of 0.856 ± 0.033 and mean Dice score of 0.702 ± 0.071 for LA scar quantification. Compared to the conventional methods, which are based on the manual delineation of LA for initialization, our method is fully automatic and has demonstrated significantly better Dice score and accuracy (p < 0.01). The method is promising and can be potentially useful in diagnosis and prognosis of AF.     

Decision Fusion-Based Fetal Ultrasound Image Plane Classification Using Convolutional Neural Networks

Machine learning for ultrasound image analysis and interpretation can be helpful in automated image classification in large-scale retrospective analyses to objectively derive new indicators of abnormal fetal development that are embedded in ultrasound images. Current approaches to automatic classification are limited to the use of either image patches (cropped images) or the global (whole) image. As many fetal organs have similar visual features, cropped images can misclassify certain structures such as the kidneys and abdomen. Also, the whole image does not encode sufficient local information about structures to identify different structures in different locations. Here we propose a method to automatically classify 14 different fetal structures in 2-D fetal ultrasound images by fusing information from both cropped regions of fetal structures and the whole image. Our method trains two feature extractors by fine-tuning pre-trained convolutional neural networks with the whole ultrasound fetal images and the discriminant regions of the fetal structures found in the whole image. The novelty of our method is in integrating the classification decisions made from the global and local features without relying on priors. In addition, our method can use the classification outcome to localize the fetal structures in the image. Our experiments on a data set of 4074 2-D ultrasound images (training: 3109, test: 965) achieved a mean accuracy of 97.05%, mean precision of 76.47% and mean recall of 75.41%. The Cohen κ of 0.72 revealed the highest agreement between the ground truth and the proposed method. The superiority of the proposed method over the other non-fusion-based methods is statistically significant (p < 0.05). We found that our method is capable of predicting images without ultrasound scanner overlays with a mean accuracy of 92%. The proposed method can be leveraged to retrospectively classify any ultrasound images in clinical research.     

A Novel Ultrasound Methodology for Estimating Spine Mineral Density

We investigated the possible clinical feasibility and accuracy of an innovative ultrasound (US) method for diagnosis of osteoporosis of the spine. A total of 342 female patients (aged 51–60 y) underwent spinal dual X-ray absorptiometry and abdominal echographic scanning of the lumbar spine. Recruited patients were subdivided into a reference database used for US spectral model construction and a study population for repeatability and accuracy evaluation. US images and radiofrequency signals were analyzed via a new fully automatic algorithm that performed a series of spectral and statistical analyses, providing a novel diagnostic parameter called the osteoporosis score (O.S.). If dual X-ray absorptiometry is assumed to be the gold standard reference, the accuracy of O.S.-based diagnoses was 91.1%, with k = 0.859 (p < 0.0001). Significant correlations were also found between O.S.-estimated bone mineral densities and corresponding dual X-ray absorptiometry values, with r² values up to 0.73 and a root mean square error of 6.3%–9.3%. The results obtained suggest that the proposed method has the potential for future routine application in US-based diagnosis of osteoporosis.     

Effect of a novel segmentation algorithm on radiologists' diagnosis of breast masses using ultrasound imaging

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.     

Multi-site cross-organ calibrated deep learning (MuSClD): Automated diagnosis of non-melanoma skin cancer

Although deep learning (DL) has demonstrated impressive diagnostic performance for a variety of computational pathology tasks, this performance often markedly deteriorates on whole slide images (WSI) generated at external test sites. This phenomenon is due in part to domain shift, wherein differences in test-site pre-analytical variables (e.g., slide scanner, staining procedure) result in WSI with notably different visual presentations compared to training data. To ameliorate pre-analytic variances, approaches such as CycleGAN can be used to calibrate visual properties of images between sites, with the intent of improving DL classifier generalizability. In this work, we present a new approach termed Multi-Site Cross-Organ Calibration based Deep Learning (MuSClD) that employs WSIs of an off-target organ for calibration created at the same site as the on-target organ, based off the assumption that cross-organ slides are subjected to a common set of pre-analytical sources of variance. We demonstrate that by using an off-target organ from the test site to calibrate training data, the domain shift between training and testing data can be mitigated. Importantly, this strategy uniquely guards against potential data leakage introduced during calibration, wherein information only available in the testing data is imparted on the training data. We evaluate MuSClD in the context of the automated diagnosis of non-melanoma skin cancer (NMSC). Specifically, we evaluated MuSClD for identifying and distinguishing (a) basal cell carcinoma (BCC), (b) in-situ squamous cell carcinomas (SCC-In Situ), and (c) invasive squamous cell carcinomas (SCC-Invasive), using an Australian (training, n = 85) and a Swiss (held-out testing, n = 352) cohort. Our experiments reveal that MuSCID reduces the Wasserstein distances between sites in terms of color, contrast, and brightness metrics, without imparting noticeable artifacts to training data. The NMSC-subtyping performance is statistically improved as a result of MuSCID in terms of one-vs. rest AUC: BCC (0.92 vs 0.87, p = 0.01), SCC-In Situ (0.87 vs 0.73, p = 0.15) and SCC-Invasive (0.92 vs 0.82, p = 1e-5). Compared to baseline NMSC-subtyping with no calibration, the internal validation results of MuSClD (BCC (0.98), SCC-In Situ (0.92), and SCC-Invasive (0.97)) suggest that while domain shift indeed degrades classification performance, our on-target calibration using off-target tissue can safely compensate for pre-analytical variabilities, while improving the robustness of the model.     

Automated analysis of breast parenchymal patterns in whole breast ultrasound images: preliminary experience

Purpose  A computerized classification scheme to recognize breast parenchymal patterns in whole breast ultrasound (US) images was developed. A preliminary evaluation of the system performance was performed. Methods  Breast parenchymal patterns were classified into three categories: mottled pattern (MP), intermediate pattern (IP), and atrophic pattern (AP). Each classification was defined as proposed by an experienced physician. A total of 281 image features were extracted from a volume of interest which was automatically segmented. Canonical discriminant analysis with stepwise feature selection was employed for the classification of the parenchymal patterns. Results  The classification scheme accuracy was computed to be 83.3% (10/12 cases) in MP cases, 91.7% (22/24 cases) in IP cases, 92.9% (13/14 cases) in AP cases, and 90.0% (45/50 cases) in all the cases. Conclusions  The feasibility of an automated ultrasonography classifier for parenchymal patterns was demonstrated with promising results in whole breast US images.     

Phase Grouping-Based Needle Segmentation in 3-D Trans-rectal Ultrasound-Guided Prostate Trans-perineal Therapy

A robust and efficient needle segmentation method used to localize and track the needle in 3-D trans-rectal ultrasound (TRUS)-guided prostate therapy is proposed. The algorithmic procedure begins by cropping the 3-D US image containing a needle; then all voxels in the cropped 3-D image are grouped into different line support regions (LSRs) based on the outer product of the adjacent voxels' gradient vector. Two different needle axis extraction methods in the candidate LSR are presented: least-squares fitting and 3-D randomized Hough transform. Subsequent local optimization refines the position of the needle axis. Finally, the needle endpoint is localized by finding an intensity drop along the needle axis. The proposed methods were validated with 3-D TRUS tissue-mimicking agar phantom images, chicken breast phantom images and patient images obtained during prostate cryotherapy. The results of the in vivo test indicate that our method can localize the needle accurately and robustly with a needle endpoint localization accuracy <1.43 mm and detection accuracy >84%, which are favorable for 3-D TRUS-guided prostate trans-perineal therapy.     

Ultrasound versus Computed Tomography Assessment of Focal Lung Aeration in Invasively Ventilated ICU Patients

It is unknown whether and to what extent the penetration depth of lung ultrasound (LUS) influences the accuracy of LUS findings. The current study evaluated and compared the LUS aeration score and two frequently used B-line scores with focal lung aeration assessed by chest computed tomography (CT) at different levels of depth in invasively ventilated intensive care unit (ICU) patients. In this prospective observational study, patients with a clinical indication for chest CT underwent a 12-region LUS examination shortly before CT scanning. LUS images were compared with corresponding regions on the chest CT scan at different subpleural depths. For each LUS image, the LUS aeration score was calculated. LUS images with B-lines were scored as the number of separately spaced B-lines (B-line count score) and the percentage of the screen covered by B-lines divided by 10 (B-line percentage score). The fixed-effect correlation coefficient (β) was presented per 100 Hounsfield units. A total of 40 patients were included, and 372 regions were analyzed. The best association between the LUS aeration score and CT was found at a subpleural depth of 5 cm for all LUS patterns (β = 0.30, p < 0.001), 1 cm for A- and B1-patterns (β = 0.10, p < 0.001), 6 cm for B1- and B2-patterns (β = 0.11, p < 0.001) and 4 cm for B2- and C-patterns (β = 0.07, p = 0.001). The B-line percentage score was associated with CT (β = 0.46, p = 0.001), while the B-line count score was not (β = 0.07, p = 0.305). In conclusion, the subpleural penetration depth of ultrasound increased with decreased aeration reflected by the LUS pattern. The LUS aeration score and the B-line percentage score accurately reflect lung aeration in ICU patients, but should be interpreted while accounting for the subpleural penetration depth of ultrasound.     

Image analysis and machine learning in digital pathology: Challenges and opportunities

With the rise in whole slide scanner technology, large numbers of tissue slides are being scanned and represented and archived digitally. While digital pathology has substantial implications for telepathology, second opinions, and education there are also huge research opportunities in image computing with this new source of “big data”. It is well known that there is fundamental prognostic data embedded in pathology images. The ability to mine “sub-visual” image features from digital pathology slide images, features that may not be visually discernible by a pathologist, offers the opportunity for better quantitative modeling of disease appearance and hence possibly improved prediction of disease aggressiveness and patient outcome. However the compelling opportunities in precision medicine offered by big digital pathology data come with their own set of computational challenges. Image analysis and computer assisted detection and diagnosis tools previously developed in the context of radiographic images are woefully inadequate to deal with the data density in high resolution digitized whole slide images. Additionally there has been recent substantial interest in combining and fusing radiologic imaging and proteomics and genomics based measurements with features extracted from digital pathology images for better prognostic prediction of disease aggressiveness and patient outcome. Again there is a paucity of powerful tools for combining disease specific features that manifest across multiple different length scales.The purpose of this review is to discuss developments in computational image analysis tools for predictive modeling of digital pathology images from a detection, segmentation, feature extraction, and tissue classification perspective. We discuss the emergence of new handcrafted feature approaches for improved predictive modeling of tissue appearance and also review the emergence of deep learning schemes for both object detection and tissue classification. We also briefly review some of the state of the art in fusion of radiology and pathology images and also combining digital pathology derived image measurements with molecular “omics” features for better predictive modeling. The review ends with a brief discussion of some of the technical and computational challenges to be overcome and reflects on future opportunities for the quantitation of histopathology.     

Comparison of diagnostic quality and accuracy in color-coded versus gray-scale DCE-MR imaging display

Purpose  The purpose of this study was to evaluate the diagnostic value and tumor-vascular display properties (microcirculation) of two different functional MRI post-processing and display (color and gray-scale display) techniques used in oncology. Materials and methods  The study protocol was approved by the IRB and written informed consent was obtained from all patients. 38 dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data sets of patients with malignant pleural-mesothelioma were acquired and post-processed. DCE-MRI was performed at 1.5 tesla with a T1-weighted 2D gradient-echo-sequence (TR 7.0 ms, TE 3.9 ms, 15 axial slices, 22 sequential repetitions), prior and during chemotherapy. Subtracting first image of contrast-enhanced-dynamic series from the last, produced gray-scale images. Color images were produced using a pharmacokinetic two-compartment model. Eight raters, blinded to diagnosis, by visual assessment of post-processed images evaluated both diagnostic quality of the images and vasculature of the tumor using a rating scale ranging from −5 to +5. The scores for vasculature were assessed by correlating with the maximum amplitude of the total-tumor-ROI for accuracy. Results  Color coded images were rated as significantly higher in diagnostic quality and tumor vascular score than gray-scale images (p <  0.001, 0.005). ROI signal amplitude analysis and vascular ratings on color coded images were better correlated compared to gray-scale images rating (p <  0.05). Conclusion  Color coded images were shown to have higher diagnostic quality and accuracy with respect to tumor vasculature in DCE-MRI, therefore their implementation in clinical assessment and follow-up should be considered for wider application.     

Value of Contrast-Enhanced Ultrasound Using Perflubutane Microbubbles for Diagnosing Liver Fibrosis and Cirrhosis in Rats

We determined the ability of contrast-enhanced ultrasound (CEUS) using perflubutane microbubbles to diagnose liver fibrosis and cirrhosis in rats using histology as the reference standard. Fibrosis was induced by oral administration of carbon tetrachloride to 32 Wistar rats. Features with baseline ultrasound (US) and enhancement level of liver and spleen with CEUS were obtained. In the post-vascular phase of CEUS, images of normal livers (n = 5) were significantly brighter than images of fibrotic (n = 6) and cirrhotic livers (n = 13) by quantitative analysis (all p < 0.05). The contrast between livers and spleens in rats with cirrhosis was quantitatively greater than that in normal rats and rats with fibrosis (all p < 0.05). Compared with US, CEUS improved sensitivity from 63% to 84% and accuracy from 71% to 88%. Specificity was 100% for both. The increased value of CEUS in diagnosing liver fibrosis and cirrhosis in rats supports its evaluation in clinical trials.     

Quantification of Acoustic Radiation Force Impulse in Differentiating Between Malignant and Benign Breast Lesions

The aim of this study was to evaluate the use of gray-level quantification (GLQ) in virtual touch tissue imaging (VTI) in the differential diagnosis of breast lesions. GLQ values of 153 lesions (101 benign, 52 malignant) were analyzed with matrix laboratory software (MATLAB, The MathWorks, Natick, MA, USA), with gray levels ranging from 0 (pure black) to 255 (pure white). The diagnostic performance of GLQ was also evaluated using receiver operating characteristic curve analysis. The mean GLQ value for benign lesions (103.27 ± 39.44) differed significantly from that for malignant lesions (44.57 ± 13.61) (p < 0.001). At a cutoff value of 52.31, the sensitivity, specificity, accuracy, positive predictive value and negative predictive value were 86.5%, 93.1%, 90.8%, 86.5% and 93.1%, respectively. In conclusion, we have proposed a method for quantification of gray levels in VTI for the differential diagnosis of breast lesions. Our results indicate that this method has the potential to aid in the classification of benign and malignant breast masses.     

Using Critical Care Chest Ultrasonic Examination in Emergency Consultation: A Pilot Study

The purpose of this study was to investigate the effects of critical care chest ultrasonic examination (CCUE) by intensivist on the diagnosis and treatment decisions in emergent consultation for patients who may have a problem-need transfer to an intensive care unit (ICU). A total of 130 patients who required emergent consultation in the ordinary wards were included in this study. Patients were randomly divided into conventional group (n = 63) and CCUE group (n = 67, added CCUE). The two groups showed no significant differences in general clinical information or final diagnosis (p > 0.05). The CCUE group had a shorter time to preliminary diagnosis, final diagnosis, treatment response and X-ray/computed tomography examination; a delay in ICU transfer and ICU stay days (3.9 ± 1.2 vs. 5.4 ± 1.9 d, p < 0.05) and a higher diagnostic accuracy than the conventional group (p < 0.001). In conclusion, CCUE could help early diagnosis and therapy for the patient who may need to transfer to the ICU and reduce the ICU stay for in-hospital patients in emergent consultation.     

CAD Algorithms for Solid Breast Masses Discrimination: Evaluation of the Accuracy and Interobserver Variability

For a successful computer-aided diagnosis (CAD) approach, investigating the benefit of the output for radiologist diagnosis is as important as developing the computer algorithm itself. To evaluate the accuracy and the interobserver variability of two newly developed CAD algorithms for breast mass discrimination, eight radiologists with varied experience in breast ultrasonography (US) independently reviewed the lesions according to Breast Imaging Reporting and Data System (BI-RADS)-US. They interpreted the original ultrasound images, provided a final assessment category to indicate the probability of malignancy and then made a further diagnosis using the images processed by the proposed CAD algorithms. The receiver operating characteristic (ROC) curve and Cohen's κ statistics were employed to evaluate the effect of the CAD algorithms on radiologist diagnoses. By using the proposed CAD approach, the quality of the images was improved and more information was provided to the observers. With the processed images, the areas under the ROC (Az) of each reader (0.86∼0.89) were greater than those with the original ultrasound images (0.81∼0.86) and all the radiologists improved their performance significantly (p < 0.05) except two senior radiologists (p > 0.05). The Az values of the junior radiologists with CAD were comparable to those of the senior radiologists. Cohen's κ statistics showed that better interobserver agreement was obtained by using the processed images. We conclude that the proposed CAD method is more helpful for the junior radiologists than for the senior ones and it also showed the advantage of decreasing interobserver variability. (E-mail: jwtian2004@yahoo.com.cn)     

Age-Related Ultrasonic Properties of Breast Tissue In Vivo

The aim of the current work was to quantify the ultrasonic properties of the whole breast in vivo as a function of age. Forty-four women were scanned using a computerized ultrasonic scanner developed in our laboratory. Raster scans in two orthogonal views, mediolateral and craniocaudal, were obtained using the ultrasonic through-transmission method. By combining the information from the two views, we estimated two acoustic properties: speed of sound and attenuation coefficient. On the basis of the results, both the attenuation coefficient and the speed of sound follow a three-phase age-related pattern. During the first phase, which corresponds to ages 20 to 35 y, both properties decrease with time and then remain roughly unchanged until about 55 y. During the third phase corresponding to ages >55 y, values decrease again with time. The mean speed of sound decreases from 1504 ± 35 m/s at <30 y to 1452 ± 9 m/s at >60 y (p < 0.01), and the attenuation coefficient decreases from 1.27 ± 0.32 to 0.96 ± 0.13 dB/cm/MHz (p < 0.03), respectively. In conclusion, both the ultrasonic speed of sound and the attenuation coefficient of breast tissue are age related. Both parameters decrease during life, markedly during the first and third phases. These changes may be attributed to anatomic and physiologic changes associated with reproductivity and menopause.     

Serum amyloid A1 can be a novel biomarker for evaluating the presence and severity of acute coronary syndrome

BackgroundSerum amyloid A (SAA) is an acute phase protein and a novel inflammatory biomarker of cardiovascular diseases. Of the four subtypes, SAA1 is the most representative biomarker. In this study, we aimed to assess the value of SAA1 as a novel biomarker for evaluating the presence and severity of acute coronary syndrome (ACS) in Chinese patients.Methods and resultsA total of 140 ACS patients and 88 non-ACS patients (including 36 stable coronary artery disease (SCAD) patients and 52 healthy controls) who underwent coronary angiography were enrolled. The SAA1 level was significantly higher in ACS patients compared with the SCAD and healthy control subgroups (P < 0.001, respectively), and was significantly higher in the high SYNTAX Score II (SS II) group compared with the medium SS II group and low SS II group (P < 0.001, respectively) in ACS patients. The cutoff level of SAA1 for indicating the presence of ACS was 324.65 ng/mL (sensitivity of 77.9%, specificity of 60.2% and an area under the curve of 0.717). The increased SAA1 levels were positively associated with the presence (OR = 1.013, P < 0.001) and severity (OR = 1.023, P < 0.001) of ACS. Furthermore, there was a positive correlation between SAA1 levels and SS II (r = 0.467, P < 0.001).ConclusionsOur results suggest that elevated SAA1 levels may be a novel biomarker for evaluating the presence of ACS and the severity of CAD in ACS patients. Measuring SAA1 levels makes it possible to evaluate the presence of ACS and severity of CAD in ACS patients.