Biomedical subjects

Dar-Ren Chen

Publications and source records attributed to Dar-Ren Chen.

At least 19 recordsLinked to original sources

PubMed2006

2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration.

Tissue elasticity of a lesion is a useful criterion for the diagnosis of breast ultrasound (US). Elastograms are created by comparing ultrasonic radio-frequency waveforms before and after a light-tissue compression. In this study, we evaluate the accuracy of continuous US strain image in the classification of benign from malignant breast tumors. A series of B-mode US images is applied and each case involves 60 continuous images obtained by using the steady artificial pressure of the US probe. In general, after compression by the US probe, a soft benign tumor will become flatter than a stiffened malignant tumor. We proposed a computer-aided diagnostic (CAD) system by utilizing the nonrigid image registration modality on the analysis of tumor deformation. Furthermore, we used some image preprocessing methods, which included the level set segmentation, to improve the performance. One-hundred pathology-proven cases, including 60 benign breast tumors and 40 malignant tumors, were used in the experiments to test the classification accuracy of the proposed method. Four characteristic values--normalized slope of metric value (NSM), normalized area difference (NAD), normalized standard deviation (NSD) and normalized center translation (NCT)--were computed for all cases. By using the support vector machine, the accuracy, sensitivity, specificity and positive and negative predictive values of the classification of continuous US strain images were satisfactory. The A(z) value of the support vector machine based on the four characteristic values used for the classification of solid breast tumors was 0.9358.

Adult↗

PubMed2006

Computer algorithm for analysing breast tumor angiogenesis using 3-D power Doppler ultrasound.

Angiogenesis provides blood supply for tumor expansion and also increases the opportunity for tumor cells to enter the blood or lymph circulation. Several proangiogenic factors as well as the contribution of the microenvironment to tumor-induced angiogenesis have been identified. Among these, vascular endothelial growth factor (VEGF) and the angiopoietin (Ang) family play a predominant role involved in the growth for endothelial cells. Tumor vessels are structurally and functionally abnormal because of an imbalance of these angiogenic regulators. In contrast to normal vessels, tumor vasculature is highly disorganized, tortuous and dilated, with uneven diameter and excessive branching. In other words, the morphologic features are likely to carry additional clues that, when used in conjunction with more established parameters, can improve the present diagnostic approaches. In our study, we present a new method that helps to capture the morphologic features from three-dimensional (3-D) power Doppler ultrasound (PDUS) images. After narrowing down the vessels into their skeletons using a 3-D thinning algorithm, we extracted seven features including vessel-to-volume ratio, number of vascular trees, number of bifurcation, mean of radius and three tortuosity measures, from the skeleton and applied a neural network to classify the tumors by using these features. In investigations into 221 solid breast tumors, including 110 benign and 111 malignant cases, the p values using the Student's t-test for all features were less than 0.05, indicating that the proposed features were deemed statistically significant. The A(Z) values for these seven features were 0.84, 0.87, 0.84, 0.75, 0.77, 0.79 and 0.69, respectively. The accuracy, sensitivity, specificity, and positive and negative predictive values were 80.09% (177 of 221), 80.18% (89 of 111), 80% (88 of 110), 80.18% (89 of 111) and 80% (88 of 110), respectively, with an A(Z) value of 0.89. The preliminary results show that the proposed method is feasible and has a good agreement with the diagnosis of the pathologists.

Adult↗

PubMed2006

Expression of glucose transporter-1 in Taiwanese patients with breast carcinoma--a preliminary report.

Malignant cells show increased glucose uptake in vitro and in vivo. High expression of the glucose transporter-1 gene (GLUT1) has been found in many human tumor tissues. The aim of this study was to investigate the correlation between GLUT1 expression in breast carcinomas of Taiwanese patients and clinical prognostic parameters. Twenty-eight (71.8%) of the 39 breast carcinomas analyzed showed positive GLUT1 expression with different intensities: 1+, 19 cases (48.7%); 2+, 6 cases (15.4%), 3+, 3 cases (7.7%). No significant correlation was seen between GLUT1 expression and clinical prognostic parameters such as tumor size (p = 0.085), age (p = 0.4528), axillary lymph node metastasis (p = 0.9562), nuclear grade (p = 0.6895), estrogen receptor-positive (p = 1.0000), and progesterone receptor-positive (p = 0.9689).

Adult↗

PubMed2006

Differentiation of serum levels of trace elements in normal and malignant breast patients.

The objective of this study was to determine which elements in serum best differentiated breast cancer in a case-control study. Concentrations of 13 elements in serum of 68 breast tumor patients (25 malignant and 43 benign) and 26 healthy controls were measured. Logistic regression with different variable-selection procedures was used to determine a possible configuration of elements. Sensitivity and specificity of the model were calculated to obtain the optimal cutoff point for discriminating malignant breast cancers vs other individuals (including benign breast disease and normal ones). Acombination of Cd, Mn, and Fe was found to have a specificity and sensitivity of 100% using forward-type logistic regression, when the cutoff value of the combination score was 52.71. Using stepwise-type logistic regression, a combination of Cr and Mn had a sensitivity of 100% and a specificity of 97.1% when the combination score of 17.4 was chosen as the cutoff. Similar analysis could be implemented to compare the malignant and control groups. Specificity and sensitivity were 100% for Mn (forward and stepwise type) with a cutoff point of 6.40. For the backward regression, specificity was 84.6% and sensitivity was 100% for Zn, with a cutoff point of 869.1. In conclusion, there was a significant difference in concentrations of all 13 elements in serum between breast cancer patients and controls. A combination among Cd, Mn, Fe, Cr, and Zn might be important to determine a differentiating reference for breast cancers if a long-term followed-up study is to be conducted.

Breast Neoplasms↗

PubMed2005

Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors.

Ultrasound (US) is a useful diagnostic tool to distinguish benign from malignant masses of the breast. It is a very convenient and safe diagnostic method. However, there is a considerable overlap benignancy and malignancy in ultrasonic images and interpretation is subjective. A high performance breast tumors computer-aided diagnosis (CAD) system can provide an accurate and reliable diagnostic second opinion for physicians to distinguish benign breast lesions from malignant ones. The potential of sonographic texture analysis to improve breast tumor classifications has been demonstrated. However, the texture analysis is system-dependent. The disadvantages of these systems which use texture analysis to classify tumors are they usually perform well only in one specific ultrasound system. While Morphological based US diagnosis of breast tumor will take the advantage of nearly independent to either the setting of US system and different US machines. In this study, the tumors are segmented using the newly developed level set method at first and then six morphologic features are used to distinguish the benign and malignant cases. The support vector machine (SVM) is used to classify the tumors. There are 210 ultrasonic images of pathologically proven benign breast tumors from 120 patients and carcinomas from 90 patients in the ultrasonic image database. The database contains only one image from each patient. The ultrasonic images are captured at the largest diameter of the tumor. The images are collected consecutively from August 1, 1999 to May 31, 2000; the patients' ages ranged from 18 to 64 years. Sonography is performed using an ATL HDI 3000 system with a L10-5 small part transducer. In the experiment, the accuracy of SVM with shape information for classifying malignancies is 90.95% (191/210), the sensitivity is 88.89% (80/90), the specificity is 92.5% (111/120), the positive predictive value is 89.89% (80/89), and the negative predictive value is 91.74% (111/121).

Adolescent↗

PubMed2005

Influences of apolipoprotein E polymorphism on the risk for breast cancer and HER2/neu status in Taiwan.

Apolipoprotein E (APOE) polymorphism plays an important role in lipid metabolism. Preliminary evidence suggests that APOE genotype appears to be a risk factor for not only cardiovascular disease, but also Alzheimer's disease and cancer. We screened the APOE genotype in 290 breast cancer patients and 232 non-cancer controls and determined the relationship between APOE gene polymorphism and breast cancer in Taiwan. We found risk for breast cancer was associated with the APOE genotype (xi(2) = 8.652, p = 0.013). Carriers of the epsilon4 allele were more common in breast cancer cases than carriers of epsilon3 allele (p = 0.004, OR = 1.786, 95% CI: 1.197-2.664). In addition, the epsilon4 allele is also associated with HER2/neu negative status in breast cancer patients (p = 0.006, OR = 0.277, 95% CI: 0.111-0.693). No significant associations between APOE genotype and tumor grade, TN classification, progesterone receptor, estrogen receptor, lymphatic invasion, or recurrence of breast cancer were in evidence. These results suggest that the APOE epsilon4 allele may be a risk factor for breast cancer and correlates with HER2/neu negative status.

Adult↗

PubMed2005

3-D ultrasound texture classification using run difference matrix.

Ultrasonography is one of the most useful diagnostic tools for human soft tissue and it is in routine use in nearly all hospitals and many physicians' offices and clinics. However, the diagnosis mostly depends upon the personal experiences of the physicians. Moreover, the surface features and internal architecture of a tumor are not easy to be demonstrated simultaneously using the conventional two-dimensional (2-D) ultrasound. Recently, three-dimensional (3-D) ultrasound has been developed and allows the physician to view the 3-D anatomy. 3-D breast US can provide transverse, longitudinal planes as well as in addition simultaneously the coronal plane. This additional information has been proved to be helpful for clinical applications. In this paper, a new approach of texture classification of 3-D ultrasound breast diagnosis using run difference matrix with neural networks is developed. The test 3-D US image database includes 54 malignant and 161 benign tumors. In the experiments, the area index A(z) under the ROC curve of the proposal 3-D RDM method can achieve 0.9680. The accuracy, sensitivity, specificity, positive predictive value and negative predictive value of the proposed 3-D RDM method is 91.9%(148/161), 88.9%(48/54), 93.5%(100/107), 87.3%(48/55), and 94.3%(100/105), respectively.

Area Under Curve↗

PubMed2005

Solid breast masses: classification with computer-aided analysis of continuous US images obtained with probe compression.

PURPOSE: To prospectively evaluate the accuracy of continuous ultrasonographic (US) images obtained during probe compression and computer-aided analysis for classification of biopsy-proved (reference standard) benign and malignant breast tumors. MATERIALS AND METHODS: This study was approved by the local ethics committee, and informed consent was obtained from all included patients. Serial US images of 100 solid breast masses (60 benign and 40 malignant tumors) were obtained with US probe compression in 86 patients (mean age, 45 years; range, 20-67 years). After segmentation of tumor contours with the level-set method, three features of strain on tissue from probe compression--contour difference, shift distance, area difference--and one feature of shape--solidity-were computed. A maximum margin classifier was used to classify the tumors by using these four features. The Student t test and receiver operating characteristic curve analysis were used for statistical analysis. RESULTS: The mean values of contour difference, shift distance, area difference, and solidity were 3.52% +/- 2.12 (standard deviation), 2.62 +/- 1.31, 1.08% +/- 0.85, and 1.70 +/- 1.85 in malignant tumors and 9.72% +/- 4.54, 5.04 +/- 2.79, 3.17% +/- 2.86, and 0.53 +/- 0.63 in benign tumors, respectively. Differences with P < .001 were statistically significant for all four features. Area under the receiver operating characteristic curve (A(Z)) values for contour difference, shift distance, area difference, and solidity were 0.88, 0.85, 0.86, and 0.79, respectively. The A(Z) value of three features of strain was significantly higher than that of the feature of shape (P < .01). The accuracy, sensitivity, specificity, and positive and negative predictive values of US classifications that were based on values for these four features were 87.0% (87 of 100), 85% (34 of 40), 88% (53 of 60), 83% (34 of 41), and 90% (53 of 59), respectively, with an A(Z) value of 0.91. CONCLUSION: Continuous US images obtained with probe compression and computer-aided analysis can aid in classification of benign and malignant breast tumors.

Adult↗

PubMed2004

Three-dimensional ultrasound in margin evaluation for breast tumor excision using Mammotome.

Sonographic evidence of tumor removal by Mammotome excision does not confirm histological clearance. The operator finds it hard to determine if a malignant tumor has been fully removed, leaving a safe margin in the direction of each border; that is, the spatial orientation during tumor retrieval is not well-established by naked eye under sonographic guidance. We propose a computational imaging process to extract reasonable tumor contour in pre- and postoperative data sets for sonographic guidance so that Mammotome excision can help the operator to evaluate the surgical outcome. There were five tumors in the study, including three benign and two malignant. The lesion of interest was delineated after 2-D examination was completed, then it was analyzed with 3-D breast ultrasound (US). To give a reference point for correlations between pre- and postoperative images, we used a marker tape pasted on the skin within the transducer scanning area and then the preoperative 3-D US images were obtained. Subsequently, 2-D breast US was applied during Mammotome operation. After the Mammotome procedures were finished, the postoperative 3-D US images were obtained; thus, we gained two different data sets of 3-D US images that were used for later analysis for evaluating the extension of postoperative margin status. From the results, the safe margin was not satisfactory in all directions, because the minimum differences measured by the proposed algorithm were not large enough in all five cases, and this was proved from two malignant mastectomy specimens. The experimental results representing this inadequate Mammotome excision can be visualized through the computer aid. The comparison of tumor contour and excision margin may possibly be used for small malignant tumors in the future to improve the breast-conserving surgery.

Breast Neoplasms↗

PubMed2004

Watershed segmentation for breast tumor in 2-D sonography.

Automatic contouring for breast tumors using medical ultrasound (US) imaging may assist physicians without relevant experience, in making correct diagnoses. This study integrates the advantages of neural network (NN) classification and morphological watershed segmentation to extract precise contours of breast tumors from US images. Textural analysis is employed to yield inputs to the NN to classify ultrasonic images. Autocovariance coefficients specify texture features to classify breasts imaged by US using a self-organizing map (SOM). After the texture features in sonography have been classified, an adaptive preprocessing procedure is selected by SOM output. Finally, watershed transformation automatically determines the contours of the tumor. In this study, the proposed method was trained and tested using images from 60 patients. The results of computer simulations reveal that the proposed method always identified similar contours and regions-of-interest (ROIs) to those obtained by manual contouring (by an experienced physician) of the breast tumor in ultrasonic images. As US imaging becomes more widespread, a functional automatic contouring method is essential and its clinical application is becoming urgent. Such a method provides robust and fast automatic contouring of US images. This study is not to emphasize that the automatic contouring technique is superior to the one undertaken manually. Both automatic and manual contours did not, after all, necessarily result in the same factual pathologic border. In computer-aided diagnosis (CAD) applications, automatic segmentation can save much of the time required to sketch a precise contour, with very high stability.

Breast Neoplasms↗

PubMed2004

Characterization of spiculation on ultrasound lesions.

Spiculation is a stellate distortion caused by the intrusion of breast cancer into surrounding tissue. Its existence is an important clue to characterizing malignant tumors. Many successful mammographic methods have been proposed to detect tumors with spiculation. Traditional two-dimensional (2-D) ultrasound cannot easily find spiculations because spiculations normally appear parallel to the surface of the skin. Recently, three-dimensional (3-D) ultrasound has been gradually used in clinical applications and it has been proven to be useful in determining the architectural distortion or spiculation that surrounds a breast tumor. This paper aims to identify spiculation from 3-D ultrasonic volume data of a tumor found by a physician. In the proposed method, each coronal slice of volume data is successively extracted and then analyzed as a 2-D ultrasound image by the proposed spiculation detection method. First, in each horizontal slice, the modified rotating structuring element (ROSE) operation is used to find the central region in which spiculation lines converge. Second, the stick algorithm is used to estimate the direction of the edge of each pixel around the central region. A pixel whose edge points toward the central region is marked as a potential spiculation. Finally, the marked pixels are collected around the central region and their distribution is analyzed to determine whether spiculation is present. The 3-D test datasets were obtained using the Voluson 530 or 730, Kretztechnik, Austria. First, the proposed method was tested on 104 2-D typical coronal images (selected by an experienced physician) extracted from 52 3-D ultrasonic datasets. Finally, 225 3-D pathologically proven datasets were tested to evaluate the performance. Spiculations are more easily observed in the coronal view than in the other two views. That is, the 3-D ultrasound is a powerful tool for identifying spiculations. Furthermore, 16% (19/120) of benign cases and 90% (94/105) of malignant cases are detected as spiculations.

Algorithms↗

PubMed2003

Computer-aided diagnosis for 3-dimensional breast ultrasonography.

HYPOTHESIS: Using 3-dimensional (3-D) over 2-dimensional (2-D) ultrasonographic (US) images of the breast represents a potentially significant advantage for computer-aided diagnosis (CAD). BACKGROUND: Although conventional 2-D US images of the breast are increasingly used in surgical clinical practice, 3-D US imaging of the breast, a newly introduced technique, can offer more information than 2-D US images do. DESIGN: This study deals with a CAD method for use with the proposed 3-D US images of the breast and compares its performance with conventional 2-D US versions. METHODS: The test databases included 3-D US images of 107 benign and 54 malignant breast tumors for a total of 161 US images. All solid nodules at US belong to categories above C3 (ie, probably benign). The 3-D US imaging was performed using a scanner (Voluson 530; Kretz Technik, Zipf, Austria). New 3-D autocorrelation coefficients extended from the traditional 2-D autocorrelations were developed to extract the texture characteristics of the 3-D US images. The extracted texture features of the 3-D US images were used to classify the tumor as benign or malignant using the neural network. RESULTS: At the receiver operating characteristic analysis, 3-D and 2-D autocorrelation calculating schemes yielded Az values (ie, area under the receiver operating characteristic curve) of 0.97 and 0.85 in distinguishing between benign and malignant lesions, respectively. Accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are statistically significantly improved using 3-D instead of 2-D US images for CAD. CONCLUSIONS: The proposed system (for 3-D and 2-D CAD) is expected to be a useful computer-aided diagnostic tool for classifying benign and malignant tumors on ultrasonograms and can provide a second reading to help reduce misdiagnosis. Findings from this study suggest that using 3-D over 2-D US images for CAD represents a potentially significant advantage.

Breast Neoplasms↗

PubMed2003

Improvement in breast tumor discrimination by support vector machines and speckle-emphasis texture analysis.

Recent statistics show that breast cancer is a major cause of death among women in developed countries. Hence, finding an accurate and effective diagnostic method is very important. In this paper, we propose a high precision computer-aided diagnosis (CAD) system for sonography. We utilize a support vector machine (SVM) to classify breast tumors according to their texture information surrounding speckle pixels. We test our system with 250 pathologically-proven breast tumors including 140 benign and 110 malignant ones. Also we compare the diagnostic performances of three texture features, i.e., speckle-emphasis texture feature, nonspeckle-emphasis texture feature and conventional all pixels texture feature, applied to breast sonography using SVM. In our experiment, the accuracy of SVM with speckle information for classifying malignancies is 93.2% (233/250), the sensitivity is 95.45% (105/110), the specificity is 91.43% (128/140), the positive predictive value is 89.74% (105/117) and the negative predictive value is 96.24% (128/133). Based on the experimental results, speckle phenomenon is a useful tool to be used in computer-aided diagnosis; its performance is better than those of the other two features. Speckle phenomenon, which is considered as noise in sonography, can intrude into judgments of a physician using naked eyes but it is another story for application in a computer-aided diagnosis algorithm.

Algorithms↗

PubMed2003

3-D US frame positioning using speckle decorrelation and image registration.

In this paper, a new positioning system is proposed for the 3-D ultrasound (US). This system combines the image registration technique and speckle decorrelation algorithm to accurately position sequential ultrasonic images without any additional positioning hardware. The speckle decorrelation algorithm estimates the relative distance of two neighboring frames and the image registration technique gets the range of the whole 3-D ultrasonic data set and makes slight modification on each frame's position. The image registration technique is based on the reference image, which is perpendicular to the 3-D ultrasonic data set. This reference image intersects each frame of the 3-D ultrasonic data set in a line. For each frame, the intersectional line is first found and then the location in the reference image can be used to estimate the position of this frame. This system uses the data set of consecutive 2-D freehand-scanned US B-mode images to construct the 3-D US volume data, and it can be integrated into the 3-D US volume rendering system.

Algorithms↗

PubMed2003

Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis.

Because ultrasound (US) imaging offers benefits compared with other medical imaging techniques, it is used routinely in nearly all hospitals and many clinics. However, the surface features and internal structure of a tumor are not easily demonstrated simultaneously using the traditional 2-D US. The newly developed three-dimensional (3-D) US can capture the morphology of a breast tumor and overcome the limitations of the traditional 2-D US. This study deals with pixel relation analysis techniques for use with 3-D breast US images and compares its performance to 2-D versions of the images. The 3-D US imaging was performed using a Voluson 530 scanner. The rectangular subimages of the volume-of-interest (VOI) were manually selected and the selected VOIs were outlined to include the entire extent of the tumor margin. The databases in this study included 54 malignant and 161 benign tumors. All solid nodules at US belong over C3 (probably benign) according to ACR BI-RADS category. All or some selected 2-D slices were used separately to calculate the diagnosis features for a 3-D US data set. We have proposed and compared several different methods to extract the characteristics of these consecutive 2-D images. As shown in our experiments, the diagnostic results were better than those of the conventional 2-D US. In the experiments, the area index Az under ROC curve of the proposed 3-D US method can achieve 0.9700 +/- 0.0118, but Az of the 2-D US is only 0.8461 +/- 0.0315. The p value of these two Az differences using z test is smaller than 0.01. Furthermore, we can find that the features from only several slices are enough to provide good diagnostic results if the adopted features are modified from the 2-D features.

Breast Neoplasms↗

PubMed2003

3-D breast ultrasound segmentation using active contour model.

In this study, we made use of the discrete active contour model to overcome the natural properties of ultrasound (US) images, speckle, noise and tissue-related textures, to segment the breast tumors precisely. Determination of the real tumor boundary with the snake-deformation process requires an initial contour estimate. However, the manual way to sketch an initial contour is very time-consuming. Thus, we propose an automatic initial contour-finding method that not only maintains the tumor shape, but also is close to the tumor boundary and inside the tumor. During the deformation process, to prevent the snake trapping into the false position caused by tissue-related texture or speckle, we added the edge information as an image feature to define the external force. In addition, because the 3-D volume of a tumor is essentially constructed by a sequence of 2-D images, our method for finding boundaries of a tumor can be extended to 3-D cases. By precisely counting the volume of the 3-D images, we can get the volume of tumor. Finally, we will show that the proposed techniques have rather good performance and lead to a satisfactory result in comparison with the estimated volume and physician's estimate.

Breast Neoplasms↗

PubMed2003

Support vector machines for diagnosis of breast tumors on US images.

RATIONALE AND OBJECTIVES: Breast cancer has become the leading cause of cancer deaths among women in developed countries. To decrease the related mortality, disease must be treated as early as possible, but it is hard to detect and diagnose tumors at an early stage. A well-designed computer-aided diagnostic system can help physicians avoid misdiagnosis and avoid unnecessary biopsy without missing cancers. In this study, the authors tested one such system to determine its effectiveness. MATERIALS AND METHODS: Many computer-aided diagnostic systems for ultrasonography are based on the neural network model and classify breast tumors according to texture features. The authors tested a refinement of this model, an advanced support vector machine (SVM), in 250 cases of pathologically proved breast tumors (140 benign and 110 malignant), and compared its performance with that of a multilayer propagation neural network. RESULTS: The accuracy of the SVM for classifying malignancies was 85.6% (214 of 250); the sensitivity, 95.45% (105 of 110); the specificity, 77.86% (109 of 140); the positive predictive value, 77.21% (105 of 136); and the negative predictive value, 95.61% (109 of 114). CONCLUSION: The SVM proved helpful in the imaging diagnosis of breast cancer. The classification ability of the SVM is nearly equal to that of the neural network model, and the SVM has a much shorter training time (1 vs 189 seconds). Given the increasing size and complexity of data sets, the SVM is therefore preferable for computer-aided diagnosis.

Breast Neoplasms↗

PubMed2003

3-D snake for US in margin evaluation for malignant breast tumor excision using Mammotome.

The goal of this paper is to use the three-dimensional (3-D) snake technique in 3-D ultrasound to obtain the tumor contour for the pre- and the post-operative malignant breast excision by the vacuum assisted biopsy instrument Mammotome. This technique of assessing the margin of two can help the physician to evaluate the effect of the surgery. By using the anisotropic diffusion filter, the noise and speckles can be reduced. Then the stick detection is adopted for enhancing the edge. Finally, the gradient vector flow (GVF) snake is used to obtain the tumor contour. These techniques are extended to the 3-D techniques to increase the accuracy and robust of segmentation results. We hope that this study can help physicians to improve the minimal invasive operation for a breast tumor.

Anatomy, Cross-Sectional↗