Computer-aided diagnosis of lung nodule classification between benign nodule, primary lung cancer, and metastatic lung cancer at different image size using deep convolutional neural network with transfer learning

We developed a computer-aided diagnosis (CADx) method for classification between benign nodule, primary lung cancer, and metastatic lung cancer and evaluated the following: (i) the usefulness of the deep convolutional neural network (DCNN) for CADx of the ternary classification, compared with a conventional method (hand-crafted imaging feature plus machine learning), (ii) the effectiveness of transfer learning, and (iii) the effect of image size as the DCNN input. Among 1240 patients of previously-built database, computed tomography images and clinical information of 1236 patients were included. For the conventional method, CADx was performed by using rotation-invariant uniform-pattern local binary pattern on three orthogonal planes with a support vector machine. For the DCNN method, CADx was evaluated using the VGG-16 convolutional neural network with and without transfer learning, and hyperparameter optimization of the DCNN method was performed by random search. The best averaged validation accuracies of CADx were 55.9%, 68.0%, and 62.4% for the conventional method, the DCNN method with transfer learning, and the DCNN method without transfer learning, respectively. For image size of 56, 112, and 224, the best averaged validation accuracy for the DCNN with transfer learning were 60.7%, 64.7%, and 68.0%, respectively. DCNN was better than the conventional method for CADx, and the accuracy of DCNN improved when using transfer learning. Also, we found that larger image sizes as inputs to DCNN improved the accuracy of lung nodule classification.

Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization

We aimed to evaluate a computer-aided diagnosis (CADx) system for lung nodule classification focussing on (i) usefulness of the conventional CADx system (hand-crafted imaging feature + machine learning algorithm), (ii) comparison between support vector machine (SVM) and gradient tree boosting (XGBoost) as machine learning algorithms, and (iii) effectiveness of parameter optimization using Bayesian optimization and random search. Data on 99 lung nodules (62 lung cancers and 37 benign lung nodules) were included from public databases of CT images. A variant of the local binary pattern was used for calculating a feature vector. SVM or XGBoost was trained using the feature vector and its corresponding label. Tree Parzen Estimator (TPE) was used as Bayesian optimization for parameters of SVM and XGBoost. Random search was done for comparison with TPE. Leave-one-out cross-validation was used for optimizing and evaluating the performance of our CADx system. Performance was evaluated using area under the curve (AUC) of receiver operating characteristic analysis. AUC was calculated 10 times, and its average was obtained. The best averaged AUC of SVM and XGBoost was 0.850 and 0.896, respectively; both were obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters for achieving high AUC were obtained with fewer numbers of trials when using TPE, compared with random search. Bayesian optimization of SVM and XGBoost parameters was more efficient than random search. Based on observer study, AUC values of two board-certified radiologists were 0.898 and 0.822. The results show that diagnostic accuracy of our CADx system was comparable to that of radiologists with respect to classifying lung nodules.

Automatic segmentation of the uterus on MRI using a convolutional neural network

BACKGROUND

This study was performed to evaluate the clinical feasibility of a U-net for fully automatic uterine segmentation on MRI by using images of major uterine disorders.

METHODS

This study included 122 female patients (14 with uterine endometrial cancer, 15 with uterine cervical cancer, and 55 with uterine leiomyoma). U-net architecture optimized for our research was used for automatic segmentation. Three-fold cross-validation was performed for validation. The results of manual segmentation of the uterus by a radiologist on T2-weighted sagittal images were used as the gold standard. Dice similarity coefficient (DSC) and mean absolute distance (MAD) were used for quantitative evaluation of the automatic segmentation. Visual evaluation using a 4-point scale was performed by two radiologists. DSC, MAD, and the score of the visual evaluation were compared between uteruses with and without uterine disorders.

RESULTS

The mean DSC of our model for all patients was 0.82. The mean DSCs for patients with and without uterine disorders were 0.84 and 0.78, respectively (p = 0.19). The mean MADs for patients with and without uterine disorders were 18.5 and 21.4 [pixels], respectively (p = 0.39). The scores of the visual evaluation were not significantly different between uteruses with and without uterine disorders.

CONCLUSIONS

Fully automatic uterine segmentation with our modified U-net was clinically feasible. The performance of the segmentation of our model was not influenced by the presence of uterine disorders.

Development of severe complications caused by stent placement followed by definitive radiation therapy for T4 esophageal cancer

Esophageal stenting in previously irradiated patients is known to cause more severe complications than those in patients who were not irradiated. But there are few reports regarding the results of stent placement before radiation therapy. Three patients with stage T4 esophageal cancer with direct invasion to the trachea and/or aorta underwent radiation therapy after stent placement. Two of the three patients had received systemic chemotherapy before radiation therapy. Fifty-one to 66 Gy of radiation therapy was administrated 15 to 66 days after the stent placement. The initial response to radiation therapy was no change (NC) or progressive disease (PD). All patients died of bleeding or pneumonia caused by perforation at the site of the stents 17 to 79 days after the radiation therapy. It is strongly suggested that even in patients with locally advanced esophageal cancer with severe dysphagia, radiation therapy should precede stent placement, because the consequences of radiation therapy after stent placement are devastating, and radiation therapy alone can, potentially, resolve the symptoms.

Temporal Subtraction of Serial CT Images with Large Deformation Diffeomorphic Metric Mapping in the Identification of Bone Metastases

Purpose To determine the improvement of radiologist efficiency and performance in the detection of bone metastases at serial follow-up computed tomography (CT) by using a temporal subtraction (TS) technique based on an advanced nonrigid image registration algorithm. Materials and Methods This retrospective study was approved by the institutional review board, and informed consent was waived. CT image pairs (previous and current scans of the torso) in 60 patients with cancer (primary lesion location: prostate, n = 14; breast, n = 16; lung, n = 20; liver, n = 10) were included. These consisted of 30 positive cases with a total of 65 bone metastases depicted only on current images and confirmed by two radiologists who had access to additional imaging examinations and clinical courses and 30 matched negative control cases (no bone metastases). Previous CT images were semiautomatically registered to current CT images by the algorithm, and TS images were created. Seven radiologists independently interpreted CT image pairs to identify newly developed bone metastases without and with TS images with an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Reading time was recorded, and usefulness was evaluated with subjective scores of 1-5, with 5 being extremely useful and 1 being useless. Significance of these values was tested with the Wilcoxon signed-rank test. Results The subtraction images depicted various types of bone metastases (osteolytic, n = 28; osteoblastic, n = 26; mixed osteolytic and blastic, n = 11) as temporal changes. The average reading time was significantly reduced (384.3 vs 286.8 seconds; Wilcoxon signed rank test, P = .028). The average figure-of-merit value increased from 0.758 to 0.835; however, this difference was not significant (JAFROC analysis, P = .092). The subjective usefulness survey response showed a median score of 5 for use of the technique (range, 3-5). Conclusion TS images obtained from serial CT scans using nonrigid registration successfully depicted newly developed bone metastases and showed promise for their efficient detection. ^© RSNA, 2017 Online supplemental material is available for this article.

Evaluation of CT angiography for visualisation of the lenticulostriate artery: difference between normotensive and hypertensive patients

OBJECTIVE

High-resolution CT angiography (CTA) is currently available using multidetector row CT (MDCT); however, its use for small artery visualisation has been limited. To evaluate its capability, we investigated CTA visualisation for difference in number of the lenticulostriate artery (LSA) branches between normotensive and hypertensive patients, because hypertension is a major cause of LSA damage.

METHODS

This was a retrospective study evaluating cerebrovascular CTA at our hospital conducted from February 2008 to June 2009 under approval of the institutional review board. 117 patients (39 males and 78 females, 19-88 years old) were included. CTA was conducted using a 64 channel MDCT. Total numbers of LSA branches were examined for differences by age with regression analysis and the presence or absence of hypertension and/or aneurysm using two-sample t-tests. A p-value <0.016 was considered statistically significant after correction for multiple comparisons. A multiple variable analysis of three factors was also conducted.

RESULTS

The average number of LSA branches was 3.6 [95% confidence interval (CI) 3.0-4.1] and 4.4 (95% CI 4.1-4.7), respectively, for a patient with and without history of hypertension, and the difference was statistically significant (p=0.013). The difference was approximately one branch in the multiple variable analysis. No significant correlation was observed for age and no significant difference was observed for the presence or absence of aneurysms.

CONCLUSIONS

Contrast-enhanced CTA can visualise significant differences in the number of LSA branches among patients with and without hypertension. Advances in knowledge Current high-resolution CTA can visualise LSA well, which enables finding a difference in the LSA between normotensive subjects and hypertensive patients.

Automatic segmentation of uterine endometrial cancer on multi-sequence MRI using a convolutional neural network

Endometrial cancer (EC) is the most common gynecological tumor in developed countries, and preoperative risk stratification is essential for personalized medicine. There have been several radiomics studies for noninvasive risk stratification of EC using MRI. Although tumor segmentation is usually necessary for these studies, manual segmentation is not only labor-intensive but may also be subjective. Therefore, our study aimed to perform the automatic segmentation of EC on MRI with a convolutional neural network. The effect of the input image sequence and batch size on the segmentation performance was also investigated. Of 200 patients with EC, 180 patients were used for training the modified U-net model; 20 patients for testing the segmentation performance and the robustness of automatically extracted radiomics features. Using multi-sequence images and larger batch size was effective for improving segmentation accuracy. The mean Dice similarity coefficient, sensitivity, and positive predictive value of our model for the test set were 0.806, 0.816, and 0.834, respectively. The robustness of automatically extracted first-order and shape-based features was high (median ICC = 0.86 and 0.96, respectively). Other high-order features presented moderate-high robustness (median ICC = 0.57-0.93). Our model could automatically segment EC on MRI and extract radiomics features with high reliability.

Automatic inference model construction for computer-aided diagnosis of lung nodule: Explanation adequacy, inference accuracy, and experts' knowledge

We aimed to describe the development of an inference model for computer-aided diagnosis of lung nodules that could provide valid reasoning for any inferences, thereby improving the interpretability and performance of the system. An automatic construction method was used that considered explanation adequacy and inference accuracy. In addition, we evaluated the usefulness of prior experts’ (radiologists’) knowledge while constructing the models. In total, 179 patients with lung nodules were included and divided into 79 and 100 cases for training and test data, respectively. F-measure and accuracy were used to assess explanation adequacy and inference accuracy, respectively. For F-measure, reasons were defined as proper subsets of Evidence that had a strong influence on the inference result. The inference models were automatically constructed using the Bayesian network and Markov chain Monte Carlo methods, selecting only those models that met the predefined criteria. During model constructions, we examined the effect of including radiologist’s knowledge in the initial Bayesian network models. Performance of the best models in terms of F-measure, accuracy, and evaluation metric were as follows: 0.411, 72.0%, and 0.566, respectively, with prior knowledge, and 0.274, 65.0%, and 0.462, respectively, without prior knowledge. The best models with prior knowledge were then subjectively and independently evaluated by two radiologists using a 5-point scale, with 5, 3, and 1 representing beneficial, appropriate, and detrimental, respectively. The average scores by the two radiologists were 3.97 and 3.76 for the test data, indicating that the proposed computer-aided diagnosis system was acceptable to them. In conclusion, the proposed method incorporating radiologists’ knowledge could help in eliminating radiologists’ distrust of computer-aided diagnosis and improving its performance.

CT temporal subtraction improves early detection of bone metastases compared to SPECT

OBJECTIVES

To compare observer performance of detecting bone metastases between bone scintigraphy, including planar scan and single-photon emission computed tomography, and computed tomography (CT) temporal subtraction (TS).

METHODS

Data on 60 patients with cancer who had undergone CT (previous and current) and bone scintigraphy were collected. Previous CT images were registered to the current ones by large deformation diffeomorphic metric mapping; the registered previous images were subtracted from the current ones to produce TS. Definitive diagnosis of bone metastases was determined by consensus between two radiologists. Twelve readers independently interpreted the following pairs of examinations: NM-pair, previous and current CTs and bone scintigraphy, and TS-pair, previous and current CTs and TS. The readers assigned likelihood levels to suspected bone metastases for diagnosis. Sensitivity, number of false positives per patient (FPP), and reading time for each pair of examinations were analysed for evaluating observer performance by performing the Wilcoxon signed-rank test. Figure-of-merit (FOM) was calculated using jackknife alternative free-response receiver operating characteristic analysis.

RESULTS

The sensitivity of TS was significantly higher than that of bone scintigraphy (54.3% vs. 41.3%, p = 0.006). FPP with TS was significantly higher than that with bone scintigraphy (0.189 vs. 0.0722, p = 0.003). FOM of TS tended to be better than that of bone scintigraphy (0.742 vs. 0.691, p = 0.070).

CONCLUSION

Sensitivity of TS in detecting bone metastasis was significantly higher than that of bone scintigraphy, but still limited to 54%. TS might be superior to bone scintigraphy for early detection of bone metastasis.

KEY POINTS

• Computed tomography temporal subtraction was helpful in early detection of bone metastases. • Sensitivity for bone metastasis was higher for computed tomography temporal subtraction than for bone scintigraphy. • Figure-of-merit of computed tomography temporal subtraction was better than that of bone scintigraphy.

A study of computer-aided diagnosis for pulmonary nodule: comparison between classification accuracies using calculated image features and imaging findings annotated by radiologists

PURPOSE

In our previous study, we developed a computer-aided diagnosis (CADx) system using imaging findings annotated by radiologists. The system, however, requires radiologists to input many imaging findings. In order to reduce such an interaction of radiologists, we further developed a CADx system using derived imaging findings based on calculated image features, in which the system only requires few user operations. The purpose of this study is to check whether calculated image features (CFT) or derived imaging findings (DFD) can represent information in imaging findings annotated by radiologists (AFD).

METHODS

We calculate 2282 image features and derive 39 imaging findings by using information on a nodule position and its type (solid or ground-glass). These image features are categorized into shape features, texture features and imaging findings-specific features. Each imaging finding is derived based on each corresponding classifier using random forest. To check whether CFT or DFD can represent information in AFD, under an assumption that the accuracies of classifiers are the same if information included in input is the same, we constructed classifiers by using various types of information (CTT, DFD and AFD) and compared accuracies on an inferred diagnosis of a nodule. We employ SVM with RBF kernel as classifier to infer a diagnosis name.

RESULTS

Accuracies of classifiers using DFD, CFT, AFD and CFT [Formula: see text] AFD were 0.613, 0.577, 0.773 and 0.790, respectively. Concordance rates between DFD and AFD of shape findings, texture findings and surrounding findings were 0.644, 0.871 and 0.768, respectively.

CONCLUSIONS

The results suggest that CFT and AFD are similar information and CFT represent only a portion of AFD. Particularly, CFT did not contain shape information in AFD. In order to decrease an interaction of radiologists, a development of a method which overcomes these problems is necessary.

Performance of dedicated breast PET in breast cancer screening: comparison with digital mammography plus digital breast tomosynthesis and ultrasound

OBJECTIVE

To compare the diagnostic performance of dedicated breast positron emission tomography (dbPET) in breast cancer screening with digital mammography plus digital breast tomosynthesis (DM-DBT) and breast ultrasound (US).

METHODS

Women who participated in opportunistic whole-body PET/computed tomography cancer screening programs with breast examinations using dbPET, DM-DBT, and US between 2016-2020, whose results were determined pathologically or by follow-up for at least 1 year, were included. DbPET, DM-DBT, and US assessments were classified into four diagnostic categories: A (no abnormality), B (mild abnormality), C (need for follow-up), and D (recommend further examination). Category D was defined as screening positive. Each modality's recall rate, sensitivity, specificity, and positive predictive value (PPV) were calculated per examination to evaluate their diagnostic performance for breast cancer.

RESULTS

Out of 2156 screenings, 18 breast cancer cases were diagnosed during the follow-up period (10 invasive cancers and eight ductal carcinomas in situ [DCIS]). The recall rates for dbPET, DM-DBT, and US were 17.8%, 19.2%, and 9.4%, respectively. The recall rate of dbPET was highest in the first year and subsequently decreased to 11.4%. dbPET, DM-DBT, and US had sensitivities of 72.2%, 88.9%, and 83.3%; specificities of 82.6%, 81.4%, and 91.2%; and PPVs of 3.4%, 3.9%, and 7.4%, respectively. The sensitivities of dbPET, DM-DBT, and US for invasive cancers were 90%, 100%, and 90%, respectively. There were no significant differences between the modalities. One case of dbPET-false-negative invasive cancer was identified in retrospect. DbPET had 50% sensitivity for DCIS, while that of both DM-DBT and US was 75%. Furthermore, the specificity of dbPET in the first year was the lowest among all periods, and modalities increased over the years to 88.7%. The specificity of dbPET was significantly higher than that of DM-DBT (p < 0.01) in the last 3 years.

CONCLUSIONS

DbPET had a compatible sensitivity to DM-DBT and breast US for invasive breast cancer. The specificity of dbPET was improved and became higher than that of DM-DBT. DbPET may be a feasible screening modality.

Optimised paediatric CT dose at a tertiary children's hospital in Japan: a 4-y single-centre analysis

Since diagnostic reference levels (DRLs) for children are not currently established in Japan, the authors determined local DRLs for the full range of paediatric CT examinations in a single tertiary care children's hospital. A retrospective review of 4801 CT performance records for paediatric patients (<15 y old) who had undergone CT examinations from 2008 to 2011 was conducted. The most frequent examinations were of the head (52 %), followed by cardiac (15 %), temporal bone (9 %), abdomen (7 %), chest (6 %) and others (11 %). Approximately one-third of children received two or more CT scans. The authors' investigation showed that mean CTDIvol and DLP for head, chest and abdomen increased as a function of age. Benchmarking of the results showed that CTDIvol, DLP and effective dose for chest and abdomen examinations in this hospital were below average, whereas those for the head tended to be at or slightly above average of established DRL values from five countries. The results suggest that CT examinations as performed in a tertiary children's hospital in Japan are well optimised.

Temporal subtraction CT with nonrigid image registration improves detection of bone metastases by radiologists: results of a large-scale observer study

To determine whether temporal subtraction (TS) CT obtained with non-rigid image registration improves detection of various bone metastases during serial clinical follow-up examinations by numerous radiologists. Six board-certified radiologists retrospectively scrutinized CT images for patients with history of malignancy sequentially. These radiologists selected 50 positive and 50 negative subjects with and without bone metastases, respectively. Furthermore, for each subject, they selected a pair of previous and current CT images satisfying predefined criteria by consensus. Previous images were non-rigidly transformed to match current images and subtracted from current images to automatically generate TS images. Subsequently, 18 radiologists independently interpreted the 100 CT image pairs to identify bone metastases, both without and with TS images, with each interpretation separated from the other by an interval of at least 30 days. Jackknife free-response receiver operating characteristics (JAFROC) analysis was conducted to assess observer performance. Compared with interpretation without TS images, interpretation with TS images was associated with a significantly higher mean figure of merit (0.710 vs. 0.658; JAFROC analysis, P = 0.0027). Mean sensitivity at lesion-based was significantly higher for interpretation with TS compared with that without TS (46.1% vs. 33.9%; P = 0.003). Mean false positive count per subject was also significantly higher for interpretation with TS than for that without TS (0.28 vs. 0.15; P < 0.001). At the subject-based, mean sensitivity was significantly higher for interpretation with TS images than that without TS images (73.2% vs. 65.4%; P = 0.003). There was no significant difference in mean specificity (0.93 vs. 0.95; P = 0.083). TS significantly improved overall performance in the detection of various bone metastases.

Temporal subtraction of computed tomography images improves detectability of bone metastases by radiology residents

OBJECTIVE

Temporal subtraction of CT (TS) images improves detection of newly developed bone metastases (BM). We sought to determine whether TS improves detection of BM by radiology residents as well.

METHODS

We performed an observer study using a previously reported dataset, consisting of 60 oncology patients, each with previous and current CT images. TS images were calculated using in-house software. Four residents independently interpreted twice the 60 sets of CT images, without and with TS. They identified BM by marking suspicious lesions likely to be BM. Lesion-based sensitivity and number of false positives per patient were calculated. Figure-of-merit (FOM) was calculated. Detectability of BM, with and without TS, was compared between radiology residents and board-certified radiologists, as published previously.

RESULTS

FOM of residents significantly improved by implementing TS (p value < 0.0001). Lesion-based sensitivity, false positives per patients, and FOM were 40.8%, 0.121, and 0.657, respectively, without TS, and 58.1%, 0.0958, and 0.796, respectively, with TS. These findings were comparable with the previously published values for board-certified radiologists without TS (58.0%, 0.19, and 0.758, respectively).

CONCLUSION

The detectability of BM by residents improved markedly by implementing TS and reached that of board-certified radiologists without TS.

KEY POINTS

• Detectability of bone metastases on CT by residents improved significantly when using temporal subtraction of CT (TS). • Detections by residents with TS and board-certified radiologists without TS were comparable. • TS is useful for residents as it is for board-certified radiologists.

An End-to-End Secure Patient Information Access Card System

The rapid development of the Internet and the increasing interest in Internet-based solutions has promoted the idea of creating Internet-based health information applications. This will force a change in the role of IC cards in healthcare card systems from a data carrier to an access key medium. At the Medical Informatics Department of Kyoto University Hospital we are developing a smart card patient information project where patient databases are accessed via the Internet. Strong end-to-end data encryption is performed via Secure Socket Layers, transparent to transmit patient information. The smart card is playing the crucial role of access key to the database: user authentication is performed internally without ever revealing the actual key. For easy acceptance by healthcare professionals, the user interface is integrated as a plug-in for two familiar Web browsers, Netscape Navigator and MS Internet Explorer.

Using a high-speed movie camera to evaluate slice dropping in clinical image interpretation with stack mode viewers

The purpose of this study is to verify objectively the rate of slice omission during paging on picture archiving and communication system (PACS) viewers by recording the images shown on the computer displays of these viewers with a high-speed movie camera. This study was approved by the institutional review board. A sequential number from 1 to 250 was superimposed on each slice of a series of clinical Digital Imaging and Communication in Medicine (DICOM) data. The slices were displayed using several DICOM viewers, including in-house developed freeware and clinical PACS viewers. The freeware viewer and one of the clinical PACS viewers included functions to prevent slice dropping. The series was displayed in stack mode and paged in both automatic and manual paging modes. The display was recorded with a high-speed movie camera and played back at a slow speed to check whether slices were dropped. The paging speeds were also measured. With a paging speed faster than half the refresh rate of the display, some viewers dropped up to 52.4 % of the slices, while other well-designed viewers did not, if used with the correct settings. Slice dropping during paging was objectively confirmed using a high-speed movie camera. To prevent slice dropping, the viewer must be specially designed for the purpose and must be used with the correct settings, or the paging speed must be slower than half of the display refresh rate.

A Proposed Dedicated Breast PET Lexicon: Standardization of Description and Reporting of Radiotracer Uptake in the Breast

Dedicated breast positron emission tomography (dbPET) is a new diagnostic imaging modality recently used in clinical practice for the detection of breast cancer and the assessment of tumor biology. dbPET has higher spatial resolution than that of conventional whole body PET systems, allowing recognition of detailed morphological attributes of radiotracer accumulation within the breast. ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) accumulation in the breast may be due to benign or malignant entities, and recent studies suggest that morphology characterization of ¹⁸F-FDG uptake could aid in estimating the probability of malignancy. However, across the world, there are many descriptors of breast ¹⁸F-FDG uptake, limiting comparisons between studies. In this article, we propose a lexicon for breast radiotracer uptake to standardize description and reporting of image findings on dbPET, consisting of terms for image quality, radiotracer fibroglandular uptake, breast lesion uptake.

Investigation of Radiologist Diagnostic Difficulty Prediction Without CT Images

For better collaboration among radiologists, the interpretation workload should be evaluated, considering the difference in difficulty for interpreting each case. However, objective evaluation of difficulty is challenging. This study proposes a multimodal classifier of structural and textual data to predict difficulty based on order information and patient data without using images. The classifier showed performance with a specificity of 0.9 and an accuracy of 0.7.

Reducing the artifacts in the identification of outer retinal boundary in the SD-OCT image with inherit retinal dystrophies

This paper presents a new SD-OCT outer retinal boundary identification method based on the improved graph-theoretic approach in SD-OCT retinal image, which is robust to the image quality degradation and the pathological morphology variability. The performance of the proposed method was verified using the SD-OCT image database with inherit retinal dystrophies, which suffer from the artifacts most among different macular degeneration diseases. The experimental results of the subjective evaluation indicated that the identification results using the proposed method was substantially improved compared with the current built-in software in the SD-OCT devices.

Visceral fat obesity is the key risk factor for the development of reflux erosive esophagitis in 40-69-years subjects

BACKGROUND

Visceral fat obesity can be defined quantitatively by abdominal computed tomography, however, the usefulness of measuring visceral fat area to assess the etiology of gastrointestinal reflux disease has not been fully elucidated.

METHODS

A total of 433 healthy subjects aged 40-69 years (234 men, 199 women) were included in the study. The relationship between obesity-related factors (total fat area, visceral fat area, subcutaneous fat area, waist circumference, and body mass index) and the incidence of reflux erosive esophagitis was investigated. Lifestyle factors and stomach conditions relevant to the onset of erosive esophagitis were also analyzed.

RESULTS

The prevalence of reflux erosive esophagitis was 27.2% (118/433; 106 men, 12 women). Visceral fat area was higher in subjects with erosive esophagitis than in those without (116.6 cm² vs. 64.9 cm², respectively). The incidence of erosive esophagitis was higher in subjects with visceral fat obesity (visceral fat area ≥ 100 cm²) than in those without (61.2% vs. 12.8%, respectively). Visceral fat obesity had the highest odds ratio (OR) among obesity-related factors. Multivariate analysis showed that visceral fat area was associated with the incidence of erosive esophagitis (OR = 2.18), indicating that it is an independent risk factor for erosive esophagitis. In addition, daily alcohol intake (OR = 1.54), gastric atrophy open type (OR = 0.29), and never-smoking history (OR = 0.49) were also independently associated with the development of erosive esophagitis.

CONCLUSIONS

Visceral fat obesity is the key risk factor for the development of reflux erosive esophagitis in subjects aged 40-69 years.

An end-to-end secure patient information access card system

The rapid development of the Internet and the increasing interest in Internet-based solutions has promoted the idea of creating Internet-based health information applications. This will force a change in the role of IC cards in healthcare card systems from a data carrier to an access key medium. At the Medical Informatics Department of Kyoto University Hospital we are developing a smart card patient information project where patient databases are accessed via the Internet. Strong end-to-end data encryption is performed via Secure Socket Layers, transparent to transmit patient information. The smart card is playing the crucial role of access key to the database: user authentication is performed internally without ever revealing the actual key. For easy acceptance by healthcare professionals, the user interface is integrated as a plug-in for two familiar Web browsers, Netscape Navigator and MS Internet Explorer.