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.

A fully automated adaptive unsharp masking technique in digital chest radiograph.

The authors are developing a fully automated adaptive unsharp masking technique with parameters that depend on regional image features of a digital chest radiograph. A chest radiograph includes various regions such as lung fields, retrocardiac area, and spine. These areas have very different texture patterns and optical densities. Therefore, for best evaluation, it is necessary to enhance the image contrast of each region by an optimal parameter. In the current study, a chest radiograph was automatically divided into three segments (lung field, retrocardiac area, and spine) by using a histogram analysis of pixel values. The lung fields and retrocardiac area were selectively enhanced with a small mask size and mild weighting factors that had been previously determined to be optimal. The spine was enhanced with a large mask size and adequate weighting factors. An observer performance test indicated that this technique provides excellent diagnostic accuracy for simulated nodules in chest radiographs.

Computer-aided detection of diffuse liver disease in ultrasound images.

The authors are developing an automated method of determining measures of texture in liver ultrasound images to improve the accuracy of diagnosis of diffuse liver abnormalities. In each digitized image, the background trend of pixel values is estimated to isolate the underlying pattern of liver texture. After correcting for background trend, the root mean square (RMS) variation and the first moment of the power spectrum are calculated; these measures have been applied successfully to texture analysis of digital chest radiographs. Background trend-corrected measurements detected statistically significant differences in digitized ultrasound images of 11 normal and 11 abnormal livers. Without correction for background trend, the measures are unable to distinguish normal from abnormal liver texture. The authors also investigated the effect on the texture measures of varying several ultrasound imaging parameters in normal patients and in an ultrasound phantom.

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.

Image feature analysis and computer-aided diagnosis in digital radiography: classification of normal and abnormal lungs with interstitial disease in chest images.

In order to detect and characterize interstitial disease in the lungs, we are developing an automated method for the determination of physical texture measures, which assess the magnitude and coarseness (or fineness) of lung texture in digital chest radiographs. This method is based on an analysis of the power spectrum of lung texture. We now describe an automated classification method for distinction between normal and abnormal lungs with interstitial disease, in which we employ these texture measures and their data base. This computerized method includes three independent tests, one for a definitely abnormal focal pattern, one for a relatively localized abnormal pattern, and one for a diffuse abnormal pattern. The performance of this computerized classification scheme is compared with that of radiologists by means of receiver operating characteristic (ROC) analysis. Our results indicate that this computerized method can be a valuable aid to radiologists in their assessment of interstitial infiltrates.

Image feature analysis and computer-aided diagnosis in digital radiography: detection and characterization of interstitial lung disease in digital chest radiographs.

We are developing an automated method for determining physical measures of lung textures in digital chest radiographs in order to detect and characterize interstitial lung disease. With this method, the underlying background density variations caused by the gross lung and chest wall anatomy are corrected for in order to isolate the fluctuating patterns of the underlying lung texture for subsequent computer analysis. The power spectrum of lung texture, which is obtained from the two-dimensional Fourier transform, is filtered by the visual system response of the human observer. The magnitude and coarseness (or fineness) of the lung textures are then quantified by the root-mean-square (rms) variation and the first moment of the power spectrum, respectively. Preliminary results indicate that the rms variations and/or the first moments of the texture of abnormal lungs with various interstitial diseases are clearly different from those of normal lungs. Our results suggest strongly that quantitative texture measures calculated from digital chest images may be useful to radiologists in their assessment of interstitial disease.

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.

Automated selection of regions of interest for quantitative analysis of lung textures in digital chest radiographs.

In order to implement a computerized scheme for quantitative analysis of interstitial lung disease in chest radiographs in clinical situations, a fully automated method of selecting many square regions of interest (ROIs) in peripheral lung areas are developed. First, the peripheral lung regions are identified, based on the automated detection of lung apices, ribcage edges, and diaphragm. Then a large number of ROIs are selected sequentially by filling in the identified peripheral regions. Finally, those ROIs containing sharp edges are removed based on an edge gradient analysis, for which a gradient-weighted edge orientation histogram is employed. Approximately 200-400 ROIs were automatically selected for each case with this method. The evaluation of using ROC analysis indicated that the automated ROI selection method was effective in quantitative analysis of lung textures in digital chest radiographs.

Image feature analysis and computer-aided diagnosis in digital radiography: effect of digital parameters on the accuracy of computerized analysis of interstitial disease in digital chest radiographs.

We are developing a computerized method for measurement of lung texture in digital chest radiographs for detection and characterization of interstitial disease. Physical texture measures are obtained from analysis of the power spectrum of the lung texture. We have investigated the effect of digital parameters such as pixel size, regions of interest size, the number of quantitation levels, and the peak frequency of the visual system response, as well as the effect of the unsharp masking technique on the performance of this computerized method. We calculated the texture measures by changing digital parameters for 100 normal lungs and 100 abnormal lungs in our database. Receiver operating characteristic (ROC) curves were employed for evaluation of the performance of this computerized method for distinguishing between normal and abnormal lungs. We used the area under the ROC curve to compare the detection accuracy for interstitial infiltrates. We believe that the results of this study may be useful as a guide in the design of computerized schemes for lung texture analysis in digital chest radiographs.

Localization of inter-rib spaces for lung texture analysis and computer-aided diagnosis in digital chest images.

An automated method for sampling lung textures in digital posterior/anterior chest images is being developed for use in computer-aided diagnosis of interstitial pulmonary diseases. In our present approach, two vertical profiles in the periphery of both lungs are fitted with a shift-variant sinusoidal function from which we estimate locations of posterior ribs and inter-rib spaces. Regions of interest (ROI's) for sampling lung textures are then automatically centered on the calculated locations of inter-rib spaces. In tests with 66 chest images, the overall success rate in placing 6.4 mm X 6.4 mm ROI's within inter-rib spaces with this method was 71%, with an average of 18 ROI's selected in 4-5 s/image by a VAX11/750 computer. When four additional alternative ROI's were selected on the sides of each original ROI, the success rate in having at least one ROI correctly located in an inter-rib space increased to 94%. Since we are still developing a fully automated sampling method, the present approach has been incorporated into a semiautomated method that is currently being used to sample lung textures from a large number of clinical cases.

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.

Computer-aided diagnosis for interstitial infiltrates in chest radiographs: optical-density dependence of texture measures.

We have been developing a computerized scheme for automated detection and characterization of interstitial infiltrates based on the Fourier transform of lung texture. To improve the performance of the scheme, which was developed using digitized screen-film radiographs, optical-density dependence of both the gradient of the film used and the system noise associated with the laser scanner were investigated. Two hundred chest radiographs, including 100 abnormal cases with interstitial infiltrates, were digitized using a laser scanner. The root-mean-square (RMS) variations and the first moments of the power spectra, which correspond to the magnitude and coarseness of lung texture, were determined by Fourier transform of lung textures in numerous regions of interest (ROIs). The RMS variation was dependent upon the average optical density in the ROI, though no obvious trend existed for the first moment of the power spectrum. Dependence of the RMS variations on optical density was corrected for using the gradient curve of the film. Also, system noise associated with the laser scanner was corrected. Results indicated that the specificity was improved from 81% (without correction) to 89% (with corrections), without any loss of sensitivity (90%). Thus, the correspondence between the computer output and consensus interpretation of radiologists was improved with the new scheme compared to the previous one. This improved computerized scheme may be useful to radiologists in detecting interstitial infiltrates in chest radiographs.

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.

Computerized analysis of interstitial disease in chest radiographs: improvement of geometric-pattern feature analysis.

We have been developing automated computerized schemes to assist radiologists in interpreting chest radiographs for interstitial disease based on texture analysis and geometric-pattern feature analysis. In this study, we attempted to improve the performance of the geometric-pattern feature analysis, because the current classification performance with geometric-pattern feature analysis is considerably lower than that of texture analysis. In order to improve the performance in distinguishing between normal lungs and abnormal lungs with interstitial disease, we attempted to remove rib edges in regions of interest (ROIs) by using an edge detection technique, and also to reduce false positives by using feature analysis techniques. In addition, the effects of many parameters on classification performance were investigated to identify proper threshold levels, and subsequently the specificity of the geometric-pattern feature analysis was improved from 69.5% to 86.1% at a sensitivity of 95.0%. Using a combined rule-based method with texture analysis and geometric-pattern feature analysis plus the artificial neural network (ANN) method for classification, a high specificity of 96.1% was obtained at a sensitivity of 95.0%.

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.

Computerized analysis of interstitial infiltrates on chest radiographs: a new scheme based on geometric pattern features and Fourier analysis.

RATIONALE AND OBJECTIVES: Detection of interstitial infiltrates on chest radiographs is difficult and subjective. Therefore, we developed a computerized method to provide quantitative analysis of lung texture to increase diagnostic accuracy. METHODS: Two hundred chest radiographs--100 healthy and 100 abnormal with interstitial infiltrates--were digitized using a laser scanner. They were analyzed by an automated computerized scheme that uses a combination of two methods for detection of interstitial infiltrates: a lung texture analysis based on the Fourier transform and a geometric pattern feature analysis based on filtering techniques. RESULTS: The overall sensitivity and specificity of the computerized scheme were 92% and 90%, respectively. The scheme achieved a sensitivity of 80% in subtle cases (n = 15) and 88% in cases with localized interstitial disease (n = 26), whereas the specificity remained unchanged. There was good correlation between the computer output and the radiologists' severity rating. CONCLUSION: This enhanced computerized scheme exhibits high sensitivity and specificity with a large database.

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.

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.