Practices for Supporting and Confirming Decision-Making Involved in Kidney and Liver Donation by Related Living Donors in Japan: A Nationwide Survey.

This nationwide survey investigated the actual practices for supporting and confirming the decision-making involved in related living-organ donations in Japan, focusing on organ type and program size differences. Answers to a questionnaire survey were collected from 89 of the 126 (71%) kidney and 30 of the 35 (86%) liver transplantation programs in Japan that were involved in living-donor transplantations in 2013. In 70% of the kidney and 90% of the liver transplantation programs, all donors underwent "third-party" interviews to confirm their voluntariness. The most common third parties were psychiatrists (90% and 83%, respectively). Many programs engaged in practices to support decision-making by donor candidates, including guaranteeing the right to withdraw consent to donate (70% and 100%, respectively) and prescribing a set "cooling-off period" (88% and 100%, respectively). Most donors were offered care by mental health specialists (86% and 93%, respectively). Third parties were designated by more of the larger kidney transplant programs compared with the smaller programs. In conclusion, the actual practices supporting and confirming the decision to donate a living organ varied depending on the organ concerned and the number of patients in the program.

Impact of FDG-PET/CT fused imaging on tumor volume assessment of head-and-neck squamous cell carcinoma: intermethod and interobserver variations.

BACKGROUND: Although gross tumor volume (GTV) at the primary site can predict local control of head-and-neck squamous cell carcinoma (SCC) in patients who are treated with organ-preservation therapy, GTV assessment does not eliminate substantial interobserver variation. PURPOSE: To evaluate whether F-18-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) fused imaging provides additional information for GTV assessment. MATERIAL AND METHODS: We obtained FDG-PET/CT fused images on 20 patients with head-and-neck SCC. All had undergone preoperative conventional workup, including contrast-enhanced CT and magnetic resonance imaging (MRI). The GTV of the primary tumors was designed by two independent observers who used routine clinical data. Observer A was a radiologist and observer B a radiation oncologist. GTV1 and GTV2 were designed without and with FDG-PET/CT, respectively. For geometric interobserver comparison, we calculated the concordance rate as the ratio of the intersection (AxB) of the GTVs to their union (AxB). Intermethod (GTV1 vs. GTV2) and interobserver (A vs. B) differences in the GTVs were assessed by Bland-Altman analysis and the Spearman rank-correlation test. The interobserver concordance rates for GTV1 and GTV2 were compared using a two-tailed paired-samples t test. RESULTS: On FDG-PET/CT, all primary tumors were visualized. There was no systemic trend for a volume difference between GTV1 and GTV2. Although the 95% limits of agreement were wider for interobserver than intermethod differences, the 95% limits of interobserver agreement were narrower for GTV2 than GTV1. The mean interobserver concordance rate for GTV2 was higher than for GTV1 (54.5% vs. 39.1%, P=0.0002). CONCLUSION: FDG-PET/CT is a useful modality for consistent GTV assessment, which should not be used as a single modality but rather to obtain supplemental information in patients with head-and-neck SCC.

An improved computer-aided diagnosis scheme using the nearest neighbor criterion for determining histological classification of clustered microcalcifications.

OBJECTIVES: Our purpose was to evaluate the potential usefulness of the nearest neighbor case which was assumed to be the similar case in a CAD scheme for determining the histological classification of clustered microcalcifications. METHODS: Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after three-month follow-up examination. It included 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 non-invasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. Six objective features on clustered microcalcifications were first extracted from each of the current and the previous images. The nearest neighbor case was then identified by the Euclidean distance in the previous and current feature-space. The histological classification of an unknown new case in question was assumed to be the same as that of the nearest neighbor case which has the shortest Euclidean distance in our database. RESULTS: The classification accuracies were 90.9% for invasive carcinoma, 89.5% for noninvasive carcinoma of the comedo type, 96.0% for noninvasive carcinoma of the noncomedo type, 82.6% for mastopathy, and 93.3% for fibroadenoma. These results were substantially higher than those with our previous CAD scheme. CONCLUSION: The nearest neighbor criterion was useful in a CAD scheme for determining the histological classification.

[Development of computerized method for automated classification of the body parts in digital radiographs].

PURPOSE: In picture archiving and communication systems (PACS), the information on the body parts included in radiographs is often not or incorrectly recorded in an image header. In order to apply the computer-aided diagnosis (CAD) system in the PACS environment, the body parts in radiographs need to be recognized correctly by computer. The purpose of this study is to develop a computerized method for correctly classifying the body parts in digital radiographs based on a template matching technique. METHODS/MATERIALS: The image database used in this study was 1032 digital radiographs (14 x 17 inches) obtained with a computed radiography, and included 505 chest of postetroanterior view, 39 chest of lateral view, 241 abdomen, 108 pelvis, 10 upper limbs, 125 lower limbs, and 4 thoracic spine. In this method, test images were classified into four body parts, i.e., (1) chest, (2) abdomen, (3) pelvis, and (4) upper/lower limbs and thoracic spine. This computerized method was tested with 852 images, since 180 images were employed for creation of 98 templates, which represented the average radiographs for various body parts. Our approach was to examine the similarity of a given test image with templates by use of the cross-correlation values as the similarity measures. The body part of the test image was identified as the body part in the template yielding the maximum correlation value. Our method consisted of the following five steps. First, test images were classified into one of three groups; i.e. 1) chest and abdomen, 2) pelvis, and 3) upper/lower limbs and thoracic spine by using the templates obtained from images with the average size and position. Second, the remaining uncertain images were classified by using additional templates in various directions. Third, the chest and abdomen group was separated into two subgroups; i.e.chest and abdomen. Fourth, in order to classify some uncertain images, templates were shifted horizontally and vertically. Fifth, outer pixels of templates were eliminated to avoid the misclassification due to x-ray collimation. RESULTS: Our preliminary results indicated that the body parts for 850 cases (99.8%) were correctly classified with our method. CONCLUSIONS: This method would be useful for automated identification of the body parts in radiographs when various CAD systems would be implemented in the PACS environment.

ROC analysis of detection of metastatic pulmonary nodules on digital chest radiographs with temporal subtraction.

RATIONALE AND OBJECTIVES: The authors' purpose was to evaluate the effect of temporal subtraction on digital chest radiographs in the detection of metastatic pulmonary nodules. MATERIALS AND METHODS: The study included 21 cases with metastatic pulmonary nodule and 21 cases without metastatic nodule. Eleven radiologists, including eight residents and three certified radiologists, provided their confidence levels for the presence or absence of pulmonary nodules without and with temporal subtraction. Their performances without and with temporal subtraction were evaluated by means of receiver operating characteristic analysis with both independent and sequential tests. RESULTS: For the independent test, the radiologists' Az (area under the receiver operating characteristic curve) values were 0.871 without and 0.954 with temporal subtraction, compared with 0.882 and 0.955, respectively, for the sequential test. Diagnosis accuracy was significantly improved with the use of temporal subtraction. There was no significant difference in Az values between the independent and sequential tests. CONCLUSION: Temporal subtraction is useful in the detection of metastatic pulmonary nodules, and this technique augments the value of digital chest radiography.

Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique.

We have been developing a computer-aided diagnostic (CAD) scheme to assist radiologists in improving the detection of pulmonary nodules in chest radiographs, because radiologists can miss as many as 30% of pulmonary nodules in routine clinical practice. A key to the successful clinical application of a CAD scheme is to ensure that there are only a small number of false positives that are incorrectly reported as nodules by the scheme. In order to significantly reduce the number of false positives in our CAD scheme, we developed, in this study, a multiple-template matching technique, in which a test candidate can be identified as a false positive and thus eliminated, if its largest cross-correlation value with non-nodule templates is larger than that with nodule templates. We describe the technique for determination of cross-correlation values for test candidates with nodule templates and non-nodule templates, the technique for creation of a large number of nodule templates and non-nodule templates, and the technique for removal of nodulelike non-nodule templates and non-nodulelike nodule templates, in order to achieve a good performance. In our study, a large number of false positives (44.3%) were removed with reduction of a very small number of true positives (2.3%) by use of the multiple-template matching technique. We believe that this technique can be used to significantly improve the performance of CAD schemes for lung nodule detection in chest radiographs.

An automated patient recognition method based on an image-matching technique using previous chest radiographs in the picture archiving and communication system environment.

An automated patient recognition method for correcting "wrong" chest radiographs being stored in a picture archiving and communication system (PACS) environment has been developed. The method is based on an image-matching technique that uses previous chest radiographs. For identification of a "wrong" patient, the correlation value was determined for a previous image of a patient and a new, current image of the presumed corresponding patient. The current image was shifted horizontally and vertically and rotated, so that we could determine the best match between the two images. The results indicated that the correlation values between the current and previous images for the same, "correct" patients were generally greater than those for different, "wrong" patients. Although the two histograms for the same patient and for different patients overlapped at correlation values greater than 0.80, most parts of the histograms were separated. The correlation value was compared with a threshold value that was determined based on an analysis of the histograms of correlation values obtained for the same patient and for different patients. If the current image is considered potentially to belong to a "wrong" patient, then a warning sign with the probability for a "wrong" patient is provided to alert radiology personnel. Our results indicate that at least half of the "wrong" images in our database can be identified correctly with the method described in this study. The overall performance in terms of a receiver operating characteristic curve showed a high performance of the system. The results also indicate that some readings of "wrong" images for a given patient in the PACS environment can be prevented by use of the method we developed. Therefore an automated warning system for patient recognition would be useful in correcting "wrong" images being stored in the PACS environment.

Improved contralateral subtraction images by use of elastic matching technique.

A contralateral subtraction technique has been developed to assist radiologists in the detection of asymmetric abnormalities such as lung nodules on a single chest radiograph. With this technique, a contralateral subtraction image is obtained by subtracting a right/left reversed "mirror" image from the original one. The lesions in the subtraction image may be enhanced because most of the symmetric skeletal structures, such as peripheral ribs, are eliminated. Although the quality of the previous contralateral subtraction images is relatively good, severe misregistration artifacts, mainly due to serious asymmetry of the ribs in the two lungs of the original image, were observed in some cases, and minor misregistration artifacts were also observed in many cases. In this study, we employed three image warping techniques. An initial global warping technique was applied to reduce severe misregistration artifacts in the subtraction image caused by asymmetric rib structures. Additional two iterative warping techniques based on an elastic matching technique were used for accurate registration of the local structures of ribs, so that minor artifacts present in many subtraction images obtained with the previous technique were greatly reduced. With the new technique, the percentage of chest images, which were rated as being of adequate, good, or excellent quality of subtraction images by use of a subjective evaluation method, was improved from 91% to 97%. In particular, the number of cases with excellent quality was greatly increased from 15% to 42%. The contralateral subtraction technique can be used for detection of asymmetric abnormalities, such as lung nodules, pneumothorax, pneumonia, and emphysema, on peripheral lungs in single chest radiographs, and it therefore has potential utility in a large proportion of abnormal chest images.

[Usefulness of temporal subtraction images of chest computed radiography for detection of metastatic pulmonary nodules].

Temporal subtraction is a technique by which a previous chest radiograph is subtracted from a current radiograph in order to enhance interval changes. Our purpose in this study was to evaluate the usefulness of temporal subtraction for the detection of metastatic pulmonary nodules. We examined 19 cases of metastatic nodules less than 15 mm in diameter (8.4 mm on average). Temporal subtraction images were created based on the matching of local lung areas in pairs of chest radiographs. By using the subtraction images, the detectability of nodules was clearly improved in 5 cases and moderately improved in 8 cases; there was no improvement in 6 cases. The subtraction images were especially useful for nodules superimposed over normal structures, such as rib, mediastinum, and diaphragm, as well as for small nodules. In 6 of the 19 cases, the nodules had been missed clinically; however, the temporal subtraction images clearly demonstrated half of missed small nodules. Temporal subtraction made it possible to enhance subtle interval changes and helped in detecting small pulmonary metastases. This technique seems promising for augmenting the capabilities of computed radiography of the chest.

Computerized analysis of the likelihood of malignancy in solitary pulmonary nodules with use of artificial neural networks.

PURPOSE: To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) to assist radiologists in the distinction of benign and malignant pulmonary nodules. MATERIALS AND METHODS: Fifty-six chest radiographs of 34 primary lung cancers and 22 benign nodules were digitized with a 0.175-mm pixel size and a 10-bit gray scale. Eight subjective image features were evaluated and recorded by radiologists in each case. A computerized method was developed to extract objective features that could be correlated with the subjective features. An ANN was used to distinguish benign from malignant nodules on the basis of subjective or objective features. The performance of the ANN was compared with that of the radiologists by means of receiver operating characteristic (ROC) analysis. RESULTS: Performance of the ANN was considerably greater with objective features (area under the ROC curve, Az = 0.854) than with subjective features (Az = 0.761). Performance of the ANN was also greater than that of the radiologists (Az = 0.752). CONCLUSION: The computerized scheme has the potential to improve the diagnostic accuracy of radiologists in the distinction of benign and malignant solitary pulmonary nodules.

Contralateral subtraction: a novel technique for detection of asymmetric abnormalities on digital chest radiographs.

A novel contralateral subtraction technique has been developed to assist radiologists in the detection of asymmetric abnormalities on a single chest radiograph. With this method, the lateral inclination is first corrected by rotating and shifting the original chest image so that the midline of the thorax is aligned with the vertical centerline of the original chest image. The rotated image is then flipped laterally to produce a reversed "mirror" image. Finally, the mirror image is warped and subtracted from the original image for derivation of the contralateral subtraction image. The three key techniques which are employed in this study are applied successively to the initial contralateral subtraction technique for acquisition of improved subtraction images. One hundred PA chest radiographs, including 50 normals and 50 abnormals, were used as the database for this study. The percentage of chest images, which were rated as being adequate, good, or excellent quality of subtraction images by employing a subjective evaluation method, was improved from 73% to 91% by use of the three key techniques. The contralateral subtraction technique can be used for detection of any asymmetric abnormalities, such as lung nodules, pneumothorax, pneumonia, and emphysema, on a single chest radiograph, and therefore has potential utility in a high proportion of abnormal cases.

Development of a digital image database for chest radiographs with and without a lung nodule: receiver operating characteristic analysis of radiologists' detection of pulmonary nodules.

OBJECTIVE: We developed a digital image database (www.macnet.or.jp/jsrt2/cdrom_nodules.html ) of 247 chest radiographs with and without a lung nodule. The aim of this study was to investigate the characteristics of image databases for potential use in various digital image research projects. Radiologists' detection of solitary pulmonary nodules included in the database was evaluated using a receiver operating characteristic (ROC) analysis. MATERIALS AND METHODS: One hundred and fifty-four conventional chest radiographs with a lung nodule and 93 radiographs without a nodule were selected from 14 medical centers and were digitized by a laser digitizer with a 2048 x 2048 matrix size (0.175-mm pixels) and a 12-bit gray scale. Lung nodule images were classified into five groups according to the degrees of subtlety shown. The observations of 20 participating radiologists were subjected to ROC analysis for detecting solitary pulmonary nodules. Experimental results (areas under the curve, Az) obtained from observer studies were used for characterization of five groups of lung nodules with different degrees of subtlety. RESULTS: ROC analysis showed that the database included a wide range of various nodules yielding Az values from 0.574 to 0.991 for the five categories of cases for different degrees of subtlety. CONCLUSION: This database can be useful for many purposes, including research, education, quality assurance, and other demonstrations.

Comparison of the quality of temporal subtraction images obtained with manual and automated methods of digital chest radiography.

The authors have been developing a fully automated temporal subtraction scheme to assist radiologists in the detection of interval changes in digital chest radiographs. The temporal subtraction image is obtained by subtraction of a previous image from a current image. The authors' automated method includes not only image shift and rotation techniques but also a nonlinear geometric warping technique for reduction of misregistration artifacts in the subtraction image. However, a manual subtraction method that can be carried out only with image shift and rotation has been employed as a common clinical technique in angiography, and it might be clinically acceptable for detection of interval changes on chest radiographs as well. Therefore, the authors applied both the manual and automated temporal subtraction techniques to 181 digital chest radiographs, and compared the quality of the subtraction images obtained with the two methods. The numbers of clinically acceptable subtraction images were 147 (81.2%) and 176 (97.2%) for the manual and automated subtraction methods, respectively. The image quality of 148 (81.8%) subtraction images was improved by use of the automated method in comparison with the subtraction images obtained with the manual method. These results indicate that the automated method with the nonlinear warping technique can significantly reduce misregistration artifacts in comparison with the manual method. Therefore, the authors believe that the automated subtraction method is more useful for the detection of interval changes in digital chest radiographs.

Computer-aided diagnosis in radiology: potential and pitfalls.

Computer-aided diagnosis (CAD) may be defined as a diagnosis made by a physician who takes into account the computer output as a second opinion. The purpose of CAD is to improve the diagnostic accuracy and the consistency of the radiologists' image interpretation. This article is to provide a brief overview of some of CAD schemes for detection and differential diagnosis of pulmonary nodules and interstitial opacities in chest radiographs as well as clustered micro-calcifications and masses in mammograms. ROC analysis clearly indicated that the radiologists' performances were significantly improved when the computer output was available. An intelligent CAD workstation was developed for detection of breast lesions in mammograms. Results obtained from the first 10,000 cases indicated the potential of CAD in detecting approximately one-half of 'missed' breast cancer.

Iterative image warping technique for temporal subtraction of sequential chest radiographs to detect interval change.

A temporal subtraction technique has been developed to assist radiologists in the detection of interval changes on chest radiographs. Although the overall performance of the current temporal subtraction technique is relatively good, severe misregistration errors, mainly due to AP inclination and/or rotation, are observed in some cases. In order to reduce these errors, we attempted to improve the subtraction scheme by applying an iterative image warping technique. In cases obtained with the new temporal subtraction technique 177 (97.8%) of 181 showed adequate, good, or excellent quality. We also found that 156 (86.2%) of cases obtained with the new scheme showed improvements in the quality of the subtraction images compared with the previous scheme. The results indicate that the performance of the temporal subtraction technique was greatly improved by use of the iterative image warping technique.

Effect of an artificial neural network on radiologists' performance in the differential diagnosis of interstitial lung disease using chest radiographs.

OBJECTIVE: We developed a new method to distinguish between various interstitial lung diseases that uses an artificial neural network. This network is based on features extracted from chest radiographs and clinical parameters. The aim of our study was to evaluate the effect of the output from the artificial neural network on radiologists' diagnostic accuracy. MATERIALS AND METHODS: The artificial neural network was designed to differentiate among 11 interstitial lung diseases using 10 clinical parameters and 16 radiologic findings. Thirty-three clinical cases (three cases for each lung disease) were selected. In the observer test, chest radiographs were viewed by eight radiologists (four attending physicians and four residents) with and without network output, which indicated the likelihood of each of the 11 possible diagnoses in each case. The radiologists' performance in distinguishing among the 11 interstitial lung diseases was evaluated by receiver operating characteristic (ROC) analysis with a continuous rating scale. RESULTS: When chest radiographs were viewed in conjunction with network output, a statistically significant improvement in diagnostic accuracy was achieved (p < .0001). The average area under the ROC curve was .826 without network output and .911 with network output. CONCLUSION: An artificial neural network can provide a useful "second opinion" to assist radiologists in the differential diagnosis of interstitial lung disease using chest radiographs.

Application of temporal subtraction for detection of interval changes on chest radiographs: improvement of subtraction images using automated initial image matching.

The authors developed a temporal subtraction scheme based on a nonlinear geometric warping technique to assist radiologists in the detection of interval changes in chest radiographs obtained on different occasions. The performance of the current temporal subtraction scheme is reasonably good; however, severe misregistration can occur in some cases. The authors evaluated the quality of 100 chest temporal subtraction images selected from their clinical image database. Severe misregistration was mainly attributable to initial incorrect global matching. Therefore, they attempted to improve the quality of the subtraction images by applying a new initial image matching technique to determine the global shift value between the current and the previous chest images. A cross-correlation method was employed for the initial image matching by use of blurred low-resolution chest images. Nineteen cases (40.4%) among 47 poor registered subtraction images were improved. These results show that the new initial image matching technique is very effective for improving the quality of chest temporal subtraction images, which can greatly enhance subtle changes in chest radiographs.

Artificial neural networks in chest radiography: application to the differential diagnosis of interstitial lung disease.

RATIONALE AND OBJECTIVES: The authors evaluated the usefulness of artificial neural networks (ANNs) in the differential diagnosis of interstitial lung disease. MATERIALS AND METHODS: The authors used three-layer, feed-forward ANNs with a back-propagation algorithm. The ANNs were designed to distinguish between 11 interstitial lung diseases on the basis of 10 clinical parameters and 16 radiologic findings extracted by chest radiologists. Thus, the ANNs consisted of 26 input units and 11 output units. One hundred fifty actual clinical cases, 110 cases from previously published articles, and 110 hypothetical cases were used for training and testing the ANNs by using a round-robin (or leave-one-out) technique. ANN performance was evaluated with receiver operating characteristic (ROC) analysis. RESULTS: The Az (area under the ROC curve) obtained with actual clinical cases was 0.947, and both the sensitivity and specificity of the ANNs were approximately 90% in terms of indicating the correct diagnosis with the two largest output values among the 11 diseases. CONCLUSION: ANNs using clinical parameters and radiologic findings may be useful for making the differential diagnosis of interstitial lung disease on chest radiographs.

Influence of clinical information on the detection of wrist fractures in children.

The purpose of this investigation is to assess the importance of clinical information for the detection of non-displaced wrist fractures in children. Twenty non-displaced fractures of the distal radius in children younger than 15 years of age and twenty age-matched controls were evaluated by five blinded observers before and after giving clinical data, and a receiver operating characteristic (ROC) analysis using a continuous rating scale with a line-marking method was performed. The detection of the fractures was significantly improved with clinical information, and the main reason for this was an increase in true positive fraction. Availability of adequate clinical data should be emphasized for interpreting radiography.