- Email: [email protected]
2D Shear Wave Elastography in risk prediction of indeterminate thyroid nodules compared to histological findings
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Ultrasound in Medicine & Biology
A small pilot study was performed to assess feasibility of shearwave elastography during treatment process. Results: The Histogram Width Tissue Characterization (HWTC) between 35 and 50 year age group shows significant difference between the epidermal and subcutaneous fat tissue characterization. The epidermis layer of 50 year old female has less gray scale levels with reduced HWTC rate. Conclusion: Further research is encouraged to include large sample populations. Following collagen filler treatment or other aesthetic methods, follow up sonography and elastography methods may prove to provide a measurable outcome in treatment. There is limited knowledge regarding elastography, shear wave elastography and strain elastography, which provides a large field of novel research to be delved upon in future. It is expected in future technology for tissue elasticity imaging there will be sophisticated improvements such as three dimensional and quantitative elasticity images based on tissue elasticity modulus.
Computer-aided diagnosis system for breast ultrasound images using deep learning Hiroki Tanaka,1 Shih-Wei Chiu,1 Takanori Watanabe,2 Setsuko Kaoku,3 Takuhiro Yamaguchi1 1 Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan, 2 National Hospital Organization Sendai Medical Center, Sendai, Miyagi, Japan, 3 National Hospital Organization Osaka National Hospital, Osaka, Japan Introduction: Ultrasonography has been recommended as an adjunctive modality to mammography, which is insufficient for accurate diagnosis of breast cancer in women with dense breast tissue. However, ultrasonography has the disadvantage of being operator dependent, requiring proficiency in reading ultrasound (US) images and increasing the false positive rate. To overcome these problems, we aimed to develop a computer-aided diagnosis (CAD) system for classification of breast malignant and benign masses on ultrasonography based on the convolutional neural network (CNN), a state-of-the-art deep learning technique. Methods: From a large ultrasound (US) image database, managed by Japan Association of Breast and Thyroid Sonology (JABTS), we collected images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations, with each breast mass captured from various angles using the US imaging probe. We constructed an ensemble network by combining two CNN models (VGG192 and ResNe1523) fine-tuned on balanced training data with the data augmentation which is a popular technique for synthetically generating new samples from the original and used the mass level classification method enabling the CNN to classify a given mass using all views. We explored the grounds of classification by generating a heatmap capable of presenting important regions used by the CNN for classification in humans. Results: On an independent 154 test masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9%, a specificity of 87.0%, and an AUC of 0.951 compared with the two CNN models. In addition, our study indicated that not only breast masses but also surrounding tissues are important regions for correct classification. Conclusion: This CNN-based CAD system is expected to assist doctors from the viewpoint of second opinion and improve the diagnosis of breast cancer in clinical practice. Acknowledgments—We would like to thank SAS Institute Japan Ltd for their technical support and offering the development environment for deep learning, which was founded by SAS Institute Inc.
Volume 45, Number S1, 2019 References: 1. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10:257-273. 2. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Int Conf on Learning Representation (ICLR). 2015;1-13. 3. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition Proc of the IEEE Conf on Computer Vision and Pattern Recognition. 2015;770-778.
Grading fatty liver and detecting liver pathological features - Is there consensus between sonographers and radiologists? David Sheng-Liang Yang,1 Michal Schneider,2 Paul Lombardo2 1 Department of Ultrasound, Canberra Imaging Group, Canberra, ACT, Australia, 2 Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia Introduction: Hepatic steatosis is the leading cause of chronic liver disease. Reliable detection and staging of liver disease is important to facilitate diagnosis and treatment. The aim of this study was to evaluate the interobserver agreement between trainee sonographers, qualified sonographers and radiologists in grading non-alcoholic fatty liver disease (NAFLD) and detecting common liver pathological features in B-mode images. Methods: 150 B-mode liver ultrasound images from 50 adult patients referred for an abdominal ultrasound were obtained retrospectively from a PACS system. The images were independently graded for the severity of hepatic steatosis (normal, mild, moderate or severe) and the detection of incidental 0findings, focal fatty sparing, liver surface irregularity and rounded liver edge (present or absent) by 17 qualified, six trainee sonographers and six radiologists. Fleiss’ kappa statistics were used to calculate interobserver agreement among participants in grading common liver pathological features. Intraclass correlation coefficient (ICC) were used to calculate the level of absolute agreement among participants in grading NAFLD. Results: The interobserver agreement rates among trainee sonographers for the detection of incidental findings, focal fatty sparing, liver surface irregularity and rounded liver edge were: k = 0.243, 0.486, 0.155 and 0.079 respectively. Among qualified sonographers, the agreement rates were: k = 0.323, 0.428, 0.167 and 0.152 respectively. Among radiologists, the agreement rates were: k = 0.156, 0.266, 0.015 and 0.154 respectively. The intraclass correlation coefficient average scores among trainee sonographers, qualified sonographers, all the sonographers combined and radiologists were: 0.927, 0.978, 0.985 and 0.954 respectively. Conclusion: Visual assessment of common liver pathology in B-mode imaging has low interobserver agreement among sonographers and radiologists, but excellent inter-rater reliability in grading NAFLD. The low agreement levels are likely caused by a lack of standardised assessment criteria. The development of a standardised criteria for staging NAFLD and common liver pathological features are recommended. Key Words: Hepatic steatosis, interobserver agreement, radiologists, sonographers, grading. 2D Shear Wave Elastography in risk prediction of indeterminate thyroid nodules compared to histological findings Pedro P. Moraes, Marcelo M. Straus, Marcelo M. Schelini, Rosa R. Sigrist, Maria Cristina M.C. Chammas Hospital das Clınicas School of Medicine - University of S~ ao Paulo, e PAULO, Brazil SAO
Abstracts We propose a risk prediction for FNAB indeterminate thyroid nodules using 2D-Shear Wave elastography(2D-SWE), to increase sensitivity/specificity of preoperative test and minimize the surgeries currently being carried out. 56 patients/ 62 follicular nodules (atypia/follicular lesion of undetermined significance or follicular/Hu¨rthle cell neoplasm) were scanned by B-mode, color-Doppler and 2D-SWE, compared with histological results after surgery. 36/62(58%) were benign, 26/62(42%) carcinomas. Statistical analysis and ROC curves were performed. B-mode demonstrated significant relationship between malignancy and nodule shape. Anteroposterior dimension/ transverse (centimeters) on axial plan, showed mean value 0.79 for benign nodules (range, 0.65-0.92) and 0.85 for malignant (range, 0.71-1.14; P=0.036). ROC analysis yielded an AUC 0.67 (95%CI: 0.11,1.38). Cutoff>1.25 cm (sens80.8%, spec42.9%). 30/62 nodules (48.3%) were hypoechogenic. 17/30 (57.7%) were malignant. Hypoechogenicity was considered significant evidence of malignancy (P=0.04). No other B-mode characteristics were significantly different between benign and malignant groups. Doppler revealed marked central/ exclusively central blood flow observed on 20/62 nodules (32.2%). 20/20 (100%) were malignant, revealing a significant relationship between these two patterns with malignancy (P=0.001). Four elastography parameters were produced for risk stratification: 1) Elastogram classification was divided in 4 groups - 1 (completely soft tissues- 100% blue) to 4 (completely hard lesions - 100% red). Univariate analysis revealed patterns 3 and 4 have 68.0% and 108.0% higher risk, respectively (sens:80.8%, spec:88.9%, AUC:87.1%, p=0.001); 2) SWE-index nodule mean deformation index in m/s> 3.62 for high risk (sens81.2%, spec82.9%, p<0.0001, AUC:0.86, 95%CI: 0.774, 0.958) and Kilopascal(kPa)>42.0 for higher risk (sens81.5%, spec80.0%, p=0.0001, AUC0.86, 95%CI: 0.771, 0.955); 3) TDR thyroid deformation ratio, calculated dividing nodule SWEindex/thyroid parenchyma adjacent to the nodule SWE-index, calculated using m/s or kPa. With velocity, ROC curve stated a cut-off> 1.36 for higher risk (sens81.5%, spec91.3%, AUC90.4%, p=0.001, 95%CI:0.831, 0.978). Using kPa, a cut-off> 1.81 showed higher risk (sens88.9%, spec91.3%, AUC90.8%, p=0.001, 95%CI: 0.831, 0.986). 4) MDR muscle deformation ratio, calculated dividing nodule SWEindex/pre-thyroid muscles SWE-index, calculated using m/s or kPa. Using velocity, ROC curve stated a cut-off> 1.22 for higher risk (sens88.9%, spec94.3%, AUC96.6%, p=0.0001, 95%CI:0.928, 1.000). Using kPa, a cut-off> 1.53 showed higher risk (sens92.6%, spec94.3%, AUC97.6%, p=0.0001, 95%CI:0.945, 1.000). Multivariate analysis produced a risk score regarding color-Doppler (vascularization pattern) and MDR (in kPa), with elevate acuracy (93,5%). These results indicate that 2D-SWE is a useful tool for the risk stratification of thyroid follicular lesions, implying in a reduction of surgical indications for patients in this groups. References: 1. E. S. Cibas and S. Z. Ali, “The 2017 Bethesda System for Reporting Thyroid Cytopathology,” J. Am. Soc. Cytopathol., vol. 6, no. 6, pp. 217 222, 2017. 2. A. E. Samir et al., “Shear-Wave Elastography for the Preoperative Risk Stratification of Follicular-patterned Lesions of the Thyroid: Diagnostic Accuracy and Optimal Measurement Plane,” Radiology, vol. 277, no. 2, pp. 565 573, 2015. 3. G. H. Tan and H. Gharib, “Thyroid incidentalomas: Management approaches to nonpalpable nodules discovered incidentally on thyroid imaging,” Annals of Internal Medicine, vol. 126, no. 3. pp. 226 231, 1997. 4. M. C. Chammas et al., “Thyroid nodules: evaluation with power Doppler and duplex Doppler ultrasound,” Otolaryngol. Head. Neck Surg., vol. 132, no. 6, pp. 874 882, 2005.
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5. A. E. Samir et al., “Shear-Wave Elastography for the Preoperative Risk Stratification of Follicular-patterned Lesions of the Thyroid: Diagnostic Accuracy and Optimal Measurement Plane,” Radiology, vol. 277, no. 2, pp. 565 573, 2015. 6. J.-B. Veyrieres et al., “A threshold value in Shear Wave elastography to rule out malignant thyroid nodules: a reality?,” Eur. J. Radiol., 2012. Comparison of the diagsnotic performance of CT angiography and contrast-enhanced US in hepatic artery occlusion after liver transplantation Jin Sil Kim,1 Kyoung Won Kim,2 Sang Hyun Choi,2 So Yeong Jeong,2 Jae Hyun Kwon,3 Gi Won Song,3 Sung Gyu Lee3 1 Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University, Seoul, Korea, 2 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, 3 Division of Liver Transplantation and Hepatobiliary Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea Purpose: To evaluate the diagnostic performance of computed tomographic angiography(CTA) and contrast-enhanced US(CEUS) and compare the two modalities to diagnose significant hepatic artery occlusion (HAO). Materials and Methods: The institutional review board approved this study, with a waiver of informed consent. Among 3117 adult liver transplantations performed over 8 years, 329 recipients were suspected of having HAO using Doppler US. After exclusion of recipients having either CTA or CEUS, 139 recipients were included in the study. CTA and CEUS were retrospectively reviewed using the criterion used in previous studies (CTA, 50% stenosis at the anastomosis; CEUS, no HA enhancement, or delayed and discontinuous enhancement). Accuracies of CTA and CEUS were compared using McNemar test. Results: CEUS showed statistical significant better accuracy and specificity than CTA in patients with Doppler US abnormality after liver transplantation (accuracy 89.2% vs 98.3%, p=0.0005; specificity, 83.2% vs 100%, p=0.0001). CTA had 15 false positive diagnoses, and CEUS had one false negative diagnosis. Conclusion: CEUS showed higher specificity and positive predictive value than CTA and should be the preferred second-line imaging tool for the diagnosis of HAO in patients with Doppler US abnormality.
Improving ultrasound education in the Pacific Region Raymond Keshwan Medical Imaging and Anatomy, Fiji National University, Suva, Fiji Motivation: Ultrasound is becoming an integral part of medical practice in the Pacific despite the lack of qualified sonographers and the absence of specialist education and training. The Problem: Sonography is mostly practiced by radiographers and medical doctors who have acquired skills through an ad hoc system of overseas training attachments, workshops and hands-on training. The modality is highly operator-dependent and the lack of education and training is leading to high levels of variability in accuracy of procedures between personnel. Aims: The purpose of this research is to conduct a training needs analysis (TNA) of personnel performing ultrasound and to propose methods of improving ultrasound education in the Pacific. Methods: This will be an exploratory study to determine the TNA of ultrasound operators and to find out the current system and effectiveness of existing training programs in the Pacific. Data will be acquired through survey questionnaires, interviews, observation of practice nusing checklists and patient feedback.
Ultrasound in Medicine & Biology
A small pilot study was performed to assess feasibility of shearwave elastography during treatment process. Results: The Histogram Width Tissue Characterization (HWTC) between 35 and 50 year age group shows significant difference between the epidermal and subcutaneous fat tissue characterization. The epidermis layer of 50 year old female has less gray scale levels with reduced HWTC rate. Conclusion: Further research is encouraged to include large sample populations. Following collagen filler treatment or other aesthetic methods, follow up sonography and elastography methods may prove to provide a measurable outcome in treatment. There is limited knowledge regarding elastography, shear wave elastography and strain elastography, which provides a large field of novel research to be delved upon in future. It is expected in future technology for tissue elasticity imaging there will be sophisticated improvements such as three dimensional and quantitative elasticity images based on tissue elasticity modulus.
Computer-aided diagnosis system for breast ultrasound images using deep learning Hiroki Tanaka,1 Shih-Wei Chiu,1 Takanori Watanabe,2 Setsuko Kaoku,3 Takuhiro Yamaguchi1 1 Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan, 2 National Hospital Organization Sendai Medical Center, Sendai, Miyagi, Japan, 3 National Hospital Organization Osaka National Hospital, Osaka, Japan Introduction: Ultrasonography has been recommended as an adjunctive modality to mammography, which is insufficient for accurate diagnosis of breast cancer in women with dense breast tissue. However, ultrasonography has the disadvantage of being operator dependent, requiring proficiency in reading ultrasound (US) images and increasing the false positive rate. To overcome these problems, we aimed to develop a computer-aided diagnosis (CAD) system for classification of breast malignant and benign masses on ultrasonography based on the convolutional neural network (CNN), a state-of-the-art deep learning technique. Methods: From a large ultrasound (US) image database, managed by Japan Association of Breast and Thyroid Sonology (JABTS), we collected images of 1536 breast masses (897 malignant and 639 benign) confirmed by pathological examinations, with each breast mass captured from various angles using the US imaging probe. We constructed an ensemble network by combining two CNN models (VGG192 and ResNe1523) fine-tuned on balanced training data with the data augmentation which is a popular technique for synthetically generating new samples from the original and used the mass level classification method enabling the CNN to classify a given mass using all views. We explored the grounds of classification by generating a heatmap capable of presenting important regions used by the CNN for classification in humans. Results: On an independent 154 test masses (77 malignant and 77 benign), our network showed outstanding classification performance with a sensitivity of 90.9%, a specificity of 87.0%, and an AUC of 0.951 compared with the two CNN models. In addition, our study indicated that not only breast masses but also surrounding tissues are important regions for correct classification. Conclusion: This CNN-based CAD system is expected to assist doctors from the viewpoint of second opinion and improve the diagnosis of breast cancer in clinical practice. Acknowledgments—We would like to thank SAS Institute Japan Ltd for their technical support and offering the development environment for deep learning, which was founded by SAS Institute Inc.
Volume 45, Number S1, 2019 References: 1. Suzuki K. Overview of deep learning in medical imaging. Radiol Phys Technol. 2017;10:257-273. 2. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Int Conf on Learning Representation (ICLR). 2015;1-13. 3. He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition Proc of the IEEE Conf on Computer Vision and Pattern Recognition. 2015;770-778.
Grading fatty liver and detecting liver pathological features - Is there consensus between sonographers and radiologists? David Sheng-Liang Yang,1 Michal Schneider,2 Paul Lombardo2 1 Department of Ultrasound, Canberra Imaging Group, Canberra, ACT, Australia, 2 Department of Medical Imaging and Radiation Sciences, Monash University, Clayton, VIC, Australia Introduction: Hepatic steatosis is the leading cause of chronic liver disease. Reliable detection and staging of liver disease is important to facilitate diagnosis and treatment. The aim of this study was to evaluate the interobserver agreement between trainee sonographers, qualified sonographers and radiologists in grading non-alcoholic fatty liver disease (NAFLD) and detecting common liver pathological features in B-mode images. Methods: 150 B-mode liver ultrasound images from 50 adult patients referred for an abdominal ultrasound were obtained retrospectively from a PACS system. The images were independently graded for the severity of hepatic steatosis (normal, mild, moderate or severe) and the detection of incidental 0findings, focal fatty sparing, liver surface irregularity and rounded liver edge (present or absent) by 17 qualified, six trainee sonographers and six radiologists. Fleiss’ kappa statistics were used to calculate interobserver agreement among participants in grading common liver pathological features. Intraclass correlation coefficient (ICC) were used to calculate the level of absolute agreement among participants in grading NAFLD. Results: The interobserver agreement rates among trainee sonographers for the detection of incidental findings, focal fatty sparing, liver surface irregularity and rounded liver edge were: k = 0.243, 0.486, 0.155 and 0.079 respectively. Among qualified sonographers, the agreement rates were: k = 0.323, 0.428, 0.167 and 0.152 respectively. Among radiologists, the agreement rates were: k = 0.156, 0.266, 0.015 and 0.154 respectively. The intraclass correlation coefficient average scores among trainee sonographers, qualified sonographers, all the sonographers combined and radiologists were: 0.927, 0.978, 0.985 and 0.954 respectively. Conclusion: Visual assessment of common liver pathology in B-mode imaging has low interobserver agreement among sonographers and radiologists, but excellent inter-rater reliability in grading NAFLD. The low agreement levels are likely caused by a lack of standardised assessment criteria. The development of a standardised criteria for staging NAFLD and common liver pathological features are recommended. Key Words: Hepatic steatosis, interobserver agreement, radiologists, sonographers, grading. 2D Shear Wave Elastography in risk prediction of indeterminate thyroid nodules compared to histological findings Pedro P. Moraes, Marcelo M. Straus, Marcelo M. Schelini, Rosa R. Sigrist, Maria Cristina M.C. Chammas Hospital das Clınicas School of Medicine - University of S~ ao Paulo, e PAULO, Brazil SAO
Abstracts We propose a risk prediction for FNAB indeterminate thyroid nodules using 2D-Shear Wave elastography(2D-SWE), to increase sensitivity/specificity of preoperative test and minimize the surgeries currently being carried out. 56 patients/ 62 follicular nodules (atypia/follicular lesion of undetermined significance or follicular/Hu¨rthle cell neoplasm) were scanned by B-mode, color-Doppler and 2D-SWE, compared with histological results after surgery. 36/62(58%) were benign, 26/62(42%) carcinomas. Statistical analysis and ROC curves were performed. B-mode demonstrated significant relationship between malignancy and nodule shape. Anteroposterior dimension/ transverse (centimeters) on axial plan, showed mean value 0.79 for benign nodules (range, 0.65-0.92) and 0.85 for malignant (range, 0.71-1.14; P=0.036). ROC analysis yielded an AUC 0.67 (95%CI: 0.11,1.38). Cutoff>1.25 cm (sens80.8%, spec42.9%). 30/62 nodules (48.3%) were hypoechogenic. 17/30 (57.7%) were malignant. Hypoechogenicity was considered significant evidence of malignancy (P=0.04). No other B-mode characteristics were significantly different between benign and malignant groups. Doppler revealed marked central/ exclusively central blood flow observed on 20/62 nodules (32.2%). 20/20 (100%) were malignant, revealing a significant relationship between these two patterns with malignancy (P=0.001). Four elastography parameters were produced for risk stratification: 1) Elastogram classification was divided in 4 groups - 1 (completely soft tissues- 100% blue) to 4 (completely hard lesions - 100% red). Univariate analysis revealed patterns 3 and 4 have 68.0% and 108.0% higher risk, respectively (sens:80.8%, spec:88.9%, AUC:87.1%, p=0.001); 2) SWE-index nodule mean deformation index in m/s> 3.62 for high risk (sens81.2%, spec82.9%, p<0.0001, AUC:0.86, 95%CI: 0.774, 0.958) and Kilopascal(kPa)>42.0 for higher risk (sens81.5%, spec80.0%, p=0.0001, AUC0.86, 95%CI: 0.771, 0.955); 3) TDR thyroid deformation ratio, calculated dividing nodule SWEindex/thyroid parenchyma adjacent to the nodule SWE-index, calculated using m/s or kPa. With velocity, ROC curve stated a cut-off> 1.36 for higher risk (sens81.5%, spec91.3%, AUC90.4%, p=0.001, 95%CI:0.831, 0.978). Using kPa, a cut-off> 1.81 showed higher risk (sens88.9%, spec91.3%, AUC90.8%, p=0.001, 95%CI: 0.831, 0.986). 4) MDR muscle deformation ratio, calculated dividing nodule SWEindex/pre-thyroid muscles SWE-index, calculated using m/s or kPa. Using velocity, ROC curve stated a cut-off> 1.22 for higher risk (sens88.9%, spec94.3%, AUC96.6%, p=0.0001, 95%CI:0.928, 1.000). Using kPa, a cut-off> 1.53 showed higher risk (sens92.6%, spec94.3%, AUC97.6%, p=0.0001, 95%CI:0.945, 1.000). Multivariate analysis produced a risk score regarding color-Doppler (vascularization pattern) and MDR (in kPa), with elevate acuracy (93,5%). These results indicate that 2D-SWE is a useful tool for the risk stratification of thyroid follicular lesions, implying in a reduction of surgical indications for patients in this groups. References: 1. E. S. Cibas and S. Z. Ali, “The 2017 Bethesda System for Reporting Thyroid Cytopathology,” J. Am. Soc. Cytopathol., vol. 6, no. 6, pp. 217 222, 2017. 2. A. E. Samir et al., “Shear-Wave Elastography for the Preoperative Risk Stratification of Follicular-patterned Lesions of the Thyroid: Diagnostic Accuracy and Optimal Measurement Plane,” Radiology, vol. 277, no. 2, pp. 565 573, 2015. 3. G. H. Tan and H. Gharib, “Thyroid incidentalomas: Management approaches to nonpalpable nodules discovered incidentally on thyroid imaging,” Annals of Internal Medicine, vol. 126, no. 3. pp. 226 231, 1997. 4. M. C. Chammas et al., “Thyroid nodules: evaluation with power Doppler and duplex Doppler ultrasound,” Otolaryngol. Head. Neck Surg., vol. 132, no. 6, pp. 874 882, 2005.
S5
5. A. E. Samir et al., “Shear-Wave Elastography for the Preoperative Risk Stratification of Follicular-patterned Lesions of the Thyroid: Diagnostic Accuracy and Optimal Measurement Plane,” Radiology, vol. 277, no. 2, pp. 565 573, 2015. 6. J.-B. Veyrieres et al., “A threshold value in Shear Wave elastography to rule out malignant thyroid nodules: a reality?,” Eur. J. Radiol., 2012. Comparison of the diagsnotic performance of CT angiography and contrast-enhanced US in hepatic artery occlusion after liver transplantation Jin Sil Kim,1 Kyoung Won Kim,2 Sang Hyun Choi,2 So Yeong Jeong,2 Jae Hyun Kwon,3 Gi Won Song,3 Sung Gyu Lee3 1 Department of Radiology and Medical Research Institute, College of Medicine, Ewha Womans University, Seoul, Korea, 2 Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea, 3 Division of Liver Transplantation and Hepatobiliary Surgery, Department of Surgery, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea Purpose: To evaluate the diagnostic performance of computed tomographic angiography(CTA) and contrast-enhanced US(CEUS) and compare the two modalities to diagnose significant hepatic artery occlusion (HAO). Materials and Methods: The institutional review board approved this study, with a waiver of informed consent. Among 3117 adult liver transplantations performed over 8 years, 329 recipients were suspected of having HAO using Doppler US. After exclusion of recipients having either CTA or CEUS, 139 recipients were included in the study. CTA and CEUS were retrospectively reviewed using the criterion used in previous studies (CTA, 50% stenosis at the anastomosis; CEUS, no HA enhancement, or delayed and discontinuous enhancement). Accuracies of CTA and CEUS were compared using McNemar test. Results: CEUS showed statistical significant better accuracy and specificity than CTA in patients with Doppler US abnormality after liver transplantation (accuracy 89.2% vs 98.3%, p=0.0005; specificity, 83.2% vs 100%, p=0.0001). CTA had 15 false positive diagnoses, and CEUS had one false negative diagnosis. Conclusion: CEUS showed higher specificity and positive predictive value than CTA and should be the preferred second-line imaging tool for the diagnosis of HAO in patients with Doppler US abnormality.
Improving ultrasound education in the Pacific Region Raymond Keshwan Medical Imaging and Anatomy, Fiji National University, Suva, Fiji Motivation: Ultrasound is becoming an integral part of medical practice in the Pacific despite the lack of qualified sonographers and the absence of specialist education and training. The Problem: Sonography is mostly practiced by radiographers and medical doctors who have acquired skills through an ad hoc system of overseas training attachments, workshops and hands-on training. The modality is highly operator-dependent and the lack of education and training is leading to high levels of variability in accuracy of procedures between personnel. Aims: The purpose of this research is to conduct a training needs analysis (TNA) of personnel performing ultrasound and to propose methods of improving ultrasound education in the Pacific. Methods: This will be an exploratory study to determine the TNA of ultrasound operators and to find out the current system and effectiveness of existing training programs in the Pacific. Data will be acquired through survey questionnaires, interviews, observation of practice nusing checklists and patient feedback.