A quantitative analysis of progressive fibrosing interstitial lung disease on computed tomography for the assessment of decreased vital capacity.

BACKGROUND: The results of a quantitative analysis of computed tomography (CT) of interstitial lung disease (ILD) using a computer-aided detection (CAD) technique were correlated with the results of pulmonary function tests. PURPOSE: To evaluate the correlation between a quantitative analysis of CT of progressive fibrosing interstitial lung disease (PF-ILD) including idiopathic pulmonary fibrosis (IPF) and non-IPF, which can manifest progressive pulmonary fibrosis and the vital capacity (VC), and to identify indicators for the assessment of a decreased VC. MATERIAL AND METHODS: A total of 73 patients (46 patients with IPF and 27 patients with non-IPF) were included in this study. Associations between the quantitative analysis of CT and the %VC using a CAD software program were investigated using Spearman's rank correlation and a logistic regression analysis. The appropriate cutoff vale for predicting a decreased VC was determined (%VC <80) and the area under the curve (AUC) was calculated. RESULTS: A multiple logistic regression analysis showed that the total extent of interstitial pneumonia on CT was a significant indicator of a decreased VC (P = 0.0001; odds ratio [OR]=1.15; 95% confidence interval [CI]=1.06-1.27 in IPF and P = 0.0025; OR=1.16; 95% CI=1.03-1.30 in non-IPF). The cutoff values of the total extent of interstitial pneumonia in IPF and non-IPF for predicting a decreased VC were determined to be 23.3% and 21.5%, and the AUCs were 0.83 and 0.91, respectively. CONCLUSION: A quantitative analysis of CT of PF-ILD using a CAD software program could be useful for predicting a decreased VC.

Treatment of Inflammatory Bowel Disease with Biologics in Japan: A Single-Center, Retrospective Pharmacoeconomic Study.

Biologics are essential for treating inflammatory bowel disease (IBD); however, only a few studies have validated cost-effective treatment options and patient factors for biologic use using real-world data from Japanese patients with IBD. Here, we aimed to provide pharmacoeconomic evidence to support clinical decisions for IBD treatment using biologics. We assessed 183 cases (127 patients) of IBD treated with biologics between November 2004 and September 2021. Data on patient background, treatment other than biologics, treatment-related medical costs, and effectiveness index (ratio of the C-reactive protein-negative period to drug survival time) were analyzed using univariate and multivariate logistic regression analyses. Drug survival was determined using Kaplan-Meier survival curve analysis. The outcomes were to validate a novel assessment index and elucidate the following aspects using this index: the effectiveness-cost relationship of long-term biologic use in IBD and cost-effectiveness-associated patient factors. Body mass index ≥25 kg/m2 and duration of hypoalbuminemia during drug survival correlated significantly with the therapeutic effectiveness of biologics. There were no significant differences in surgical, granulocyte apheresis, or adverse-event costs per drug survival time. Biologic costs were significantly higher in the group showing lower effectiveness than in the group showing higher effectiveness. These findings hold major pharmacoeconomic implications for not only improving therapeutic outcomes through the amelioration of low albumin levels and obesity but also potentially reducing healthcare expenditure related to the use of biotherapeutics. To our knowledge, this is the first pharmacoeconomic study based on real-world data from Japanese patients with IBD receiving long-term biologic therapy.

A low prognostic nutritional index is associated with increased remote infections within 30 days of colorectal surgery: A retrospective cohort study.

OBJECTIVE: This study aimed to identify risk factors for remote infection (RI) within 30 days after colorectal surgery. METHODS: This retrospective study included 660 patients who underwent colorectal surgery at Yamaguchi University Hospital or Ube Kosan Central Hospital between April 2015 and March 2019. Using electronic medical records, we identified the incidence of surgical site infection and RI within 30 days after surgery and obtained information on associated factors. Univariate and multivariable analyses were performed to identify significant risk factors in 607 (median age, 71 years) patients. RESULTS: Seventy-eight (13%) and 38 (6.3%) patients had surgical site infection and RI, respectively. Of the 38 patients diagnosed with RI, 14 (36.8%) had a bloodstream infection, 13 (34.2%) had a urinary tract infection, 8 (21.1%) had a Clostridioides difficile infection, and 7 (18.4%) had respiratory tract infections. Multivariable analysis showed that a preoperative prognostic nutritional index of ≤40 (OR, 2.30; 95% CI, 1.07-4.92; P = .032), intraoperative blood transfusion (OR (odds ratio), 3.06; 95% CI, 1.25-7.47; P = .014), and concomitant stoma creation (OR, 4.13; 95% CI, 1.93-8.83; P = .0002) were significant RI predictors. CONCLUSIONS: Nutritional interventions prompted by low preoperative prognostic nutritional index in colorectal surgery may lead to decreases in postoperative RI.

Segmentation of Lung Nodules on CT Images Using a Nested Three-Dimensional Fully Connected Convolutional Network.

In computer-aided diagnosis systems for lung cancer, segmentation of lung nodules is important for analyzing image features of lung nodules on computed tomography (CT) images and distinguishing malignant nodules from benign ones. However, it is difficult to accurately and robustly segment lung nodules attached to the chest wall or with ground-glass opacities using conventional image processing methods. Therefore, this study aimed to develop a method for robust and accurate three-dimensional (3D) segmentation of lung nodule regions using deep learning. In this study, a nested 3D fully connected convolutional network with residual unit structures was proposed, and designed a new loss function. Compared with annotated images obtained under the guidance of a radiologist, the Dice similarity coefficient (DS) and intersection over union (IoU) were 0.845 ± 0.008 and 0.738 ± 0.011, respectively, for 332 lung nodules (lung adenocarcinoma) obtained from 332 patients. On the other hand, for 3D U-Net and 3D SegNet, the DS was 0.822 ± 0.009 and 0.786 ± 0.011, respectively, and the IoU was 0.711 ± 0.011 and 0.660 ± 0.012, respectively. These results indicate that the proposed method is significantly superior to well-known deep learning models. Moreover, we compared the results obtained from the proposed method with those obtained from conventional image processing methods, watersheds, and graph cuts. The DS and IoU results for the watershed method were 0.628 ± 0.027 and 0.494 ± 0.025, respectively, and those for the graph cut method were 0.566 ± 0.025 and 0.414 ± 0.021, respectively. These results indicate that the proposed method is significantly superior to conventional image processing methods. The proposed method may be useful for accurate and robust segmentation of lung nodules to assist radiologists in the diagnosis of lung nodules such as lung adenocarcinoma on CT images.

Pharmacoeconomic study of biologics for psoriasis treatment based on real-world drug survival.

The efficacy of biologics in psoriasis treatment is clinically proven; however, biologics are expensive. In this study, we assessed the real-world cost-effectiveness of biologics for psoriasis treatment by evaluating the relationship between biologic drug survival (DS) and total medical-treatment costs from a pharmacoeconomic viewpoint. Furthermore, the effects of patient factors on cost-effectiveness were investigated. We retrospectively reviewed the medical records of 135 cases who received either a tumor necrosis factor-alpha (TNF-α) monoclonal antibody (TNF-mab), interleukin (IL)-17 mab, or IL23p19-mab for psoriasis from January 2010 to June 2020 at Yamaguchi University Hospital. We compared the monthly medical-treatment costs according to biologic classification and found that costs of medical services, tests, and external preparations required for the treatment process were significantly higher in the TNF-mab group than in the other groups, and the total medical costs associated with TNF-mab treatment were significantly higher than those of IL17-mab treatment. The total monthly cost of medical care was lower in the long-term DS group than in the short-term group. The number of prescriptions for external preparations, comprising Vitamin D3 and corticosteroid, was significantly higher in the long-term DS group than in the short-term group; in the TNF-mab group, the proportion of patients without smoking habits was significantly higher in the long-term group as well. Our study indicated that when costly biologics are used for psoriasis treatment, the maintenance of long-term DS and appropriate patient guidance might improve the quality of medical care, thus allowing cost-effective medical care.

Deep Learning for Pulmonary Image Analysis: Classification, Detection, and Segmentation.

Image-based computer-aided diagnosis (CAD) algorithms by the use of convolutional neural network (CNN) which do not require the image-feature extractor are powerful compared with conventional feature-based CAD algorithms which require the image-feature extractor for classification of lung abnormalities. Moreover, computer-aided detection and segmentation algorithms by the use of CNN are useful for analysis of lung abnormalities. Deep learning will improve the performance of CAD systems dramatically. Therefore, they will change the roles of radiologists in the near future. In this article, we introduce development and evaluation of such image-based CAD algorithms for various kinds of lung abnormalities such as lung nodules and diffuse lung diseases.

Pulmonary Textures Classification via a Multi-Scale Attention Network.

Precise classification of pulmonary textures is crucial to develop a computer aided diagnosis (CAD) system of diffuse lung diseases (DLDs). Although deep learning techniques have been applied to this task, the classification performance is not satisfied for clinical requirements, since commonly-used deep networks built by stacking convolutional blocks are not able to learn discriminative feature representation to distinguish complex pulmonary textures. For addressing this problem, we design a multi-scale attention network (MSAN) architecture comprised by several stacked residual attention modules followed by a multi-scale fusion module. Our deep network can not only exploit powerful information on different scales but also automatically select optimal features for more discriminative feature representation. Besides, we develop visualization techniques to make the proposed deep model transparent for humans. The proposed method is evaluated by using a large dataset. Experimental results show that our method has achieved the average classification accuracy of 94.78% and the average f-value of 0.9475 in the classification of 7 categories of pulmonary textures. Besides, visualization results intuitively explain the working behavior of the deep network. The proposed method has achieved the state-of-the-art performance to classify pulmonary textures on high resolution CT images.

Automatic classification of lung nodules on MDCT images with the temporal subtraction technique.

PURPOSE: A temporal subtraction (TS) image is obtained by subtracting a previous image, which is warped to match the structures of the previous image and the related current image. The TS technique removes normal structures and enhances interval changes such as new lesions and substitutes in existing abnormalities from a medical image. However, many artifacts remaining on the TS image can be detected as false positives. METHOD: This paper presents a novel automatic segmentation of lung nodules using the Watershed method, multiscale gradient vector flow snakes and a detection method using the extracted features and classifiers for small lung nodules (20 mm or less). RESULT: Using the proposed method, we conduct an experiment on 30 thoracic multiple-detector computed tomography cases including 31 small lung nodules. CONCLUSION: The experimental results indicate the efficiency of our segmentation method.

Unsupervised class labeling of diffuse lung diseases using frequent attribute patterns.

PURPOSE: For realizing computer-aided diagnosis (CAD) of computed tomography (CT) images, many pattern recognition methods have been applied to automatic classification of normal and abnormal opacities; however, for the learning of accurate classifier, a large number of images with correct labels are necessary. It is a very time-consuming and impractical task for radiologists to give correct labels for a large number of CT images. In this paper, to solve the above problem and realize an unsupervised class labeling mechanism without using correct labels, a new clustering algorithm for diffuse lung diseases using frequent attribute patterns is proposed. METHODS: A large number of frequently appeared patterns of opacities are extracted by a data mining algorithm named genetic network programming (GNP), and the extracted patterns are automatically distributed to several clusters using genetic algorithm (GA). In this paper, lung CT images are used to make clusters of normal and diffuse lung diseases. RESULTS: After executing the pattern extraction by GNP, 1,148 frequent attribute patterns were extracted; then, GA was executed to make clusters. This paper deals with making clusters of normal and five kinds of abnormal opacities (i.e., six-class problem), and then, the proposed method without using correct class labels in the training showed 47.7 % clustering accuracy. CONCLUSION: It is clarified that the proposed method can make clusters without using correct labels and has the potential to apply to CAD, reducing the time cost for labeling CT images.

Characterization of a new carmovirus isolated from an Adonis plant.

An isometric virus was isolated from a cultivated Adonis plant (A. ramosa). The purified virus particle is 28 nm in diameter and is composed of a single coat protein and a single RNA genome of 3,991 nucleotides. Sequence analysis showed that the virus is closely related to carnation mottle virus. The virus was used to mechanically infect healthy A. ramosa plants, resulting in mosaic and leaf curl symptoms; however, attempts to inoculate carnation plants did not result in infection. We propose the virus as a new carmovirus and have named it adonis mosaic virus (AdMV).

A sparse representation based method to classify pulmonary patterns of diffuse lung diseases.

We applied and optimized the sparse representation (SR) approaches in the computer-aided diagnosis (CAD) to classify normal tissues and five kinds of diffuse lung disease (DLD) patterns: consolidation, ground-glass opacity, honeycombing, emphysema, and nodule. By using the K-SVD which is based on the singular value decomposition (SVD) and orthogonal matching pursuit (OMP), it can achieve a satisfied recognition rate, but too much time was spent in the experiment. To reduce the runtime of the method, the K-Means algorithm was substituted for the K-SVD, and the OMP was simplified by searching the desired atoms at one time (OMP1). We proposed three SR based methods for evaluation: SR1 (K-SVD+OMP), SR2 (K-Means+OMP), and SR3 (K-Means+OMP1). 1161 volumes of interest (VOIs) were used to optimize the parameters and train each method, and 1049 VOIs were adopted to evaluate the performances of the methods. The SR based methods were powerful to recognize the DLD patterns (SR1: 96.1%, SR2: 95.6%, SR3: 96.4%) and significantly better than the baseline methods. Furthermore, when the K-Means and OMP1 were applied, the runtime of the SR based methods can be reduced by 98.2% and 55.2%, respectively. Therefore, we thought that the method using the K-Means and OMP1 (SR3) was efficient for the CAD of the DLDs.

Determination of lung regions on chest ct images with diffuse lung diseases by use of anatomical structures and pulmonary textures.

To analyze diffuse lung diseases based on chest region computed tomography (CT) imaging by using a computer-aided diagnosis (CAD) system, it is necessary to first determine the lung regions subject to analysis. The lung regions can be selected relatively easily for healthy individuals, by applying a threshold. Selecting an area by using a threshold-based method can be difficult when dealing with lungs with diffuse lung diseases, owing to the abnormal opacities that characterize the diseases. Trials for determining the lung regions were conducted in this study, through texture analysis and machine learning, by narrowing down the lung regions to rough regions, and by referring to ribs and the diaphragm. This method can be used for determining lung regions for analysis of diffuse lung diseases.

Texture analysis on (18)F-FDG PET/CT images to differentiate malignant and benign bone and soft-tissue lesions.

OBJECTIVE: The purpose is to develop and evaluate the ability of the computer-aided diagnosis (CAD) methods that apply texture analysis and pattern classification to differentiate malignant and benign bone and soft-tissue lesions on 18F-fluorodeoxy-glucose positron emission tomography/computed tomography ((18)F-FDG PET/CT) images. METHODS: Subjects were 103 patients with 59 malignant and 44 benign bone and soft tissue lesions larger than 25 mm in diameter. Variable texture parameters of standardized uptake values (SUV) and CT Hounsfield unit values were three-dimensionally calculated in lesional volumes-of-interest segmented on PET/CT images. After selection of a subset of the most optimal texture parameters, a support vector machine classifier was used to automatically differentiate malignant and benign lesions. We developed three kinds of CAD method. Two of them utilized only texture parameters calculated on either CT or PET images, and the other one adopted the combined PET and CT texture parameters. Their abilities of differential diagnosis were compared with the SUV method with an optimal cut-off value of the maximum SUV. RESULTS: The CAD methods utilizing only optimal PET (or CT) texture parameters showed sensitivity of 83.05 % (81.35 %), specificity of 63.63 % (61.36 %), and accuracy of 74.76 % (72.82 %). Although the ability of differential diagnosis by PET or CT texture analysis alone was not significantly different from the SUV method whose sensitivity, specificity, and accuracy were 64.41, 61.36, and 63.11 % (the optimal cut-off SUVmax was 5.4 ± 0.9 in the 10-fold cross-validation test), the CAD method with the combined PET and CT optimal texture parameters (PET: entropy and coarseness, CT: entropy and correlation) exhibited significantly better performance compared with the SUV method (p = 0.0008), showing a sensitivity of 86.44 %, specificity of 77.27 %, and accuracy of 82.52 %. CONCLUSIONS: The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.

Development and evaluation of statistical shape modeling for principal inner organs on torso CT images.

The shapes of the inner organs are important information for medical image analysis. Statistical shape modeling provides a way of quantifying and measuring shape variations of the inner organs in different patients. In this study, we developed a universal scheme that can be used for building the statistical shape models for different inner organs efficiently. This scheme combines the traditional point distribution modeling with a group-wise optimization method based on a measure called minimum description length to provide a practical means for 3D organ shape modeling. In experiments, the proposed scheme was applied to the building of five statistical shape models for hearts, livers, spleens, and right and left kidneys by use of 50 cases of 3D torso CT images. The performance of these models was evaluated by three measures: model compactness, model generalization, and model specificity. The experimental results showed that the constructed shape models have good "compactness" and satisfied the "generalization" performance for different organ shape representations; however, the "specificity" of these models should be improved in the future.

Classification of diffuse lung diseases patterns by a sparse representation based method on HRCT images.

This paper describes a computer-aided diagnosis (CAD) method to classify diffuse lung diseases (DLD) patterns on HRCT images. Due to the high variety and complexity of DLD patterns, the performance of conventional methods on recognizing DLD patterns featured by geometrical information is limited. In this paper, we introduced a sparse representation based method to classify normal tissues and five types of DLD patterns including consolidation, ground-glass opacity, honeycombing, emphysema and nodular. Both CT values and eigenvalues of Hessian matrices were adopted to calculate local features. The 2360 VOIs from 117 subjects were separated into two independent set. One set was used to optimize parameters, and the other set was adopted to evaluation. The proposed technique has a overall accuracy of 95.4%. Experimental results show that our method would be useful to classify DLD patterns on HRCT images.

Semantic characteristics prediction of pulmonary nodule using Artificial Neural Networks.

Since it is difficult to choose which computer calculated features are effective to predict the malignancy of pulmonary nodules, in this study, we add a semantic-level of Artificial Neural Networks (ANNs) structure to improve intuition of features selection. The works of this study include two: 1) seeking the relationships between computer-calculated features and medical semantic concepts which could be understood by human; 2) providing an objective assessment method to predict the malignancy from semantic characteristics. We used 60 thoracic CT scans collected from the Lung Image Database Consortium (LIDC) database, in which the suspicious lesions had been delineated and annotated by 4 radiologists independently. Corresponding to the two works of this study, correlation analysis experiment and agreement experiment were performed separately.

Particle system based adaptive sampling on spherical parameter space to improve the MDL method for construction of statistical shape models.

Minimum description length (MDL) based group-wise registration was a state-of-the-art method to determine the corresponding points of 3D shapes for the construction of statistical shape models (SSMs). However, it suffered from the problem that determined corresponding points did not uniformly spread on original shapes, since corresponding points were obtained by uniformly sampling the aligned shape on the parameterized space of unit sphere. We proposed a particle-system based method to obtain adaptive sampling positions on the unit sphere to resolve this problem. Here, a set of particles was placed on the unit sphere to construct a particle system whose energy was related to the distortions of parameterized meshes. By minimizing this energy, each particle was moved on the unit sphere. When the system became steady, particles were treated as vertices to build a spherical mesh, which was then relaxed to slightly adjust vertices to obtain optimal sampling-positions. We used 47 cases of (left and right) lungs and 50 cases of livers, (left and right) kidneys, and spleens for evaluations. Experiments showed that the proposed method was able to resolve the problem of the original MDL method, and the proposed method performed better in the generalization and specificity tests.

Classification of diffuse lung disease patterns on high-resolution computed tomography by a bag of words approach.

Visual inspection of diffuse lung disease (DLD) patterns on high-resolution computed tomography (HRCT) is difficult because of their high complexity. We proposed a bag of words based method on the classification of these textural patters in order to improve the detection and diagnosis of DLD for radiologists. Six kinds of typical pulmonary patterns were considered in this work. They were consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal tissue. Because they were characterized by both CT values and shapes, we proposed a set of statistical measure based local features calculated from both CT values and the eigen-values of Hessian matrices. The proposed method could achieve the recognition rate of 95.85%, which was higher comparing with one global feature based method and two other CT values based bag of words methods.

Computer analysis system of the physician-patient consultation process.

PURPOSE: Measurements of the quality of physician-patient communication are important in assessing patient outcomes, but the quality of communication is difficult to quantify. The aim of this paper is to develop a computer analysis system for the physician-patient consultation process (CASC), which will use a quantitative method to quantify and analyze communication exchanges between physicians and patients during the consultation process. DESIGN/METHODOLOGY/APPROACH: CASC is based on the concept of narrative-based medicine using a computer-mediated communication (CMC) technique from a cognitive dialog processing system. Effective and ineffective consultation samples from the works of Saito and Kleinman were tested with CASC in order to establish the validity of CASC for use in clinical practice. After validity was confirmed, three researchers compared their assessments of consultation processes in a physician's office with CASCs. Consultations of 56 migraine patients were recorded with permission, and for this study consultations of 29 patients that included more than 50 words were used. FINDINGS: Transcribed data from the 29 consultations input into CASC resulted in two diagrams of concept structure and concept space to assess the quality of consultation. The concordance rate between the assessments by CASC and the researchers was 75 percent. ORIGINALITY/VALUE: In this study, a computer-based communication analysis system was established that efficiently quantifies the quality of the physician-patient consultation process. The system is promising as an effective tool for evaluating the quality of physician-patient communication in clinical and educational settings.