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VJ MJ, S K. Multi-classification approach for lung nodule detection and classification with proposed texture feature in X-ray images. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-28. [PMID: 37362672 PMCID: PMC10188326 DOI: 10.1007/s11042-023-15281-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 10/22/2022] [Accepted: 04/06/2023] [Indexed: 06/28/2023]
Abstract
Lung cancer is a widespread type of cancer around the world. It is, moreover, a lethal type of tumor. Nevertheless, analysis signifies that earlier recognition of lung cancer considerably develops the possibilities of survival. By deploying X-rays and Computed Tomography (CT) scans, radiologists could identify hazardous nodules at an earlier period. However, when more citizens adopt these diagnoses, the workload rises for radiologists. Computer Assisted Diagnosis (CAD)-based detection systems can identify these nodules automatically and could assist radiologists in reducing their workloads. However, they result in lower sensitivity and a higher count of false positives. The proposed work introduces a new approach for Lung Nodule (LN) detection. At first, Histogram Equalization (HE) is done during pre-processing. As the next step, improved Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) based segmentation is done. Then, the characteristics, including "Gray Level Run-Length Matrix (GLRM), Gray Level Co-Occurrence Matrix (GLCM), and the proposed Local Vector Pattern (LVP)," are retrieved. These features are then categorized utilizing an optimized Convolutional Neural Network (CNN) and itdetectsnodule or non-nodule images. Subsequently, Long Short-Term Memory (LSTM) is deployed to categorize nodule types (benign, malignant, or normal). The CNN weights are fine-tuned by the Chaotic Population-based Beetle Swarm Algorithm (CP-BSA). Finally, the superiority of the proposed approach is confirmed across various measures. The developed approach has exhibited a high precision value of 0.9575 for the best case scenario, and high sensitivity value of 0.9646 for the mean case scenario. The superiority of the proposed approach is confirmed across various measures.
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Affiliation(s)
- Mary Jaya VJ
- Department of Computer Science, Assumption Autonomous College, Changanassery, Kerala India
| | - Krishnakumar S
- Department of Electronics, School of Technology and Applied Sciences, Mahatma Gandhi University Research Centre, Kochi, Kerala India
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2
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Sebastian AE, Dua D. Lung Nodule Detection via Optimized Convolutional Neural Network: Impact of Improved Moth Flame Algorithm. SENSING AND IMAGING 2023; 24:11. [PMID: 36936054 PMCID: PMC10009866 DOI: 10.1007/s11220-022-00406-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Revised: 09/30/2022] [Accepted: 11/02/2022] [Indexed: 06/18/2023]
Abstract
Lung cancer is a high-risk disease that affects people all over the world, and lung nodules are the most common sign of early lung cancer. Since early identification of lung cancer can considerably improve a lung scanner patient's chances of survival, an accurate and efficient nodule detection system can be essential. Automatic lung nodule recognition decreases radiologists' effort, as well as the risk of misdiagnosis and missed diagnoses. Hence, this article developed a new lung nodule detection model with four stages like "Image pre-processing, segmentation, feature extraction and classification". In this processes, pre-processing is the first step, in which the input image is subjected to a series of operations. Then, the "Otsu Thresholding model" is used to segment the pre-processed pictures. Then in the third stage, the LBP features are retrieved that is then classified via optimized Convolutional Neural Network (CNN). In this, the activation function and convolutional layer count of CNN is optimally tuned via a proposed algorithm known as Improved Moth Flame Optimization (IMFO). At the end, the betterment of the scheme is validated by carrying out analysis in terms of certain measures. Especially, the accuracy of the proposed work is 6.85%, 2.91%, 1.75%, 0.73%, 1.83%, as well as 4.05% superior to the extant SVM, KNN, CNN, MFO, WTEEB as well as GWO + FRVM methods respectively.
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Affiliation(s)
| | - Disha Dua
- Indira Gandhi Delhi Technical University for Women, Delhi, Delhi, India
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3
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Comparison of single- and dual-energy CT combined with artificial intelligence for the diagnosis of pulmonary nodules. Clin Radiol 2023; 78:e99-e105. [PMID: 36266099 DOI: 10.1016/j.crad.2022.09.114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 08/26/2022] [Accepted: 09/02/2022] [Indexed: 01/18/2023]
Abstract
AIM To explore the efficiency of single- and dual-energy computed tomography (CT) with artificial intelligence (AI) for the diagnosis of pulmonary nodules. MATERIALS AND METHODS In a prospective study, 682 patients undergoing a chest CT examination using a dual-energy system were divided randomly into two groups: single-energy mode (group S, n=341) and dual-energy mode (group D, n=341). CT images were first analysed automatically with the AI pulmonary nodule-detection software. CT features including nodule number, lesion size, and nodule type were then analysed by experienced radiologists to establish a reference diagnosis. Subsequently, the accuracy, sensitivity, false-positive rate, and miss rate of AI were calculated. Additionally, image quality and radiation dose were also compared between the two groups. RESULTS The contrast-to-noise ratio data suggested that the image quality of group D was superior to that of group S (0.16 ± 0.10 versus 0.00 ± 0.17), and the radiation dose of group D was lower than that of group S (0.32 ± 0.10 versus 0.62 ± 0.11 mSv.cm). Compared to group S, group D exhibited a significantly higher sensitivity and lower accuracy for nodule identification, size classification, and nodule type (all p<0.05, except for 5-10 mm and calcified nodules). CONCLUSIONS Compared with single-energy CT, dual-energy CT may significantly improve the sensitivity of AI for the diagnosis of pulmonary nodules and is practical for the screening of pulmonary nodules in a large population. In addition, dual-energy CT examination demonstrates improved image quality and is associated with reduced exposure to ionising radiation, but its accuracy is poorer.
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Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe? Diagnostics (Basel) 2022; 12:diagnostics12102326. [PMID: 36292015 PMCID: PMC9601268 DOI: 10.3390/diagnostics12102326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/24/2022] Open
Abstract
Annual screening of lung cancer (LC) with chest low-dose computed tomography (CT) and screening of colorectal cancer (CRC) with CT colonography every 5 years are recommended by the United States Prevention Service Task Force. We review epidemiological and pathological data on LC and CRC, and the features of screening chest low-dose CT and CT colonography comprising execution, reading, radiation exposure and harm, and the cost effectiveness of the two CT screening interventions. The possibility of combining chest low-dose CT and CT colonography examinations for double LC and CRC screening in a single CT appointment is then addressed. We demonstrate how this approach appears feasible and is already reasonable as an opportunistic screening intervention in 50–75-year-old subjects with smoking history and average CRC risk. In addition to the crucial role Computer Assisted Diagnosis systems play in decreasing the test reading times and the need to educate radiologists in screening chest LDCT and CT colonography, in view of a single CT appointment for double screening, the following uncertainties need to be solved: (1) the schedule of the screening CT; (2) the effectiveness of iterative reconstruction and deep learning algorithms affording an ultra-low-dose CT acquisition technique and (3) management of incidental findings. Resolving these issues will imply new cost-effectiveness analyses for LC screening with chest low dose CT and for CRC screening with CT colonography and, especially, for the double LC and CRC screening with a single-appointment CT.
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Au C, Reeves R, Li Z, Gingold E, Halpern E, Sundaram B. Impact of multidetector computed tomography scan parameters, novel reconstruction settings, and lung nodule characteristics on nodule diameter measurements: A Phantom Study. Med Phys 2022; 49:3936-3943. [PMID: 35358333 DOI: 10.1002/mp.15639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 03/09/2022] [Accepted: 03/18/2022] [Indexed: 11/12/2022] Open
Abstract
PURPOSE Novel CT reconstruction techniques strive to maintain image quality and processing efficiency. The purpose of this study is to investigate the impact of a newer hybrid iterative reconstruction technique, Adaptive Statistical Iterative Reconstruction-V (ASIR-V) in combination with various CT scan parameters on the semi-automated quantification using various lung nodules. METHODS A chest phantom embedded with eight spherical objects was scanned using varying CT parameters such as tube current and ASIR-V levels. We calculated absolute percentage error (APE) and mean APE (MAPE) using differences between the semi-automated measured diameters and known dimensions. Predictive variables were assessed using a multivariable general linear model. The linear regression slope coefficients (β) were reported to demonstrate effect size and directionality. RESULTS The APE of the semi-automated measured diameters was higher in ground-glass than solid nodules (β = 9.000, p<0.001). APE had an inverse relationship with nodule diameter (mm; β = -3.499, p<0.001) and tube current (mA; β = -0.006, p<0.001). MAPE did not vary based on the ASIR-V level (range: 5.7-13.1%). CONCLUSION Error is dominated by nodule characteristics with a small effect of tube current. Regardless of phantom size, nodule size accuracy is not affected by tube voltage or ASIR-V level, maintaining accuracy while maximizing radiation dose reduction. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Cherry Au
- Department of Internal Medicine, Rush University Medical Center, 1620 W Harrison St, Chicago, IL, 60612
| | - Russell Reeves
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Zhenteng Li
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107.,The Vascular Center, St. Luke's Anderson Campus - Medical Office Building, 1700 St. Luke's Boulevard, Suite 301, Easton, PA
| | - Eric Gingold
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Ethan Halpern
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
| | - Baskaran Sundaram
- Department of Radiology, Thomas Jefferson University Hospital, 111 S 11th St, Philadelphia, PA, 19107
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6
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Wu YJ, Shi QT, Zhang Y, Wang YL. Thoracoscopic segmentectomy and lobectomy assisted by three-dimensional computed-tomography bronchography and angiography for the treatment of primary lung cancer. World J Clin Cases 2021; 9:10494-10506. [PMID: 35004981 PMCID: PMC8686156 DOI: 10.12998/wjcc.v9.i34.10494] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 07/20/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Anatomical segmentectomy has been proposed as a substitution for lobectomy for early-stage lung cancer. However, it requires technical meticulousness due to the complex anatomical variations of segmental vessels and bronchi.
AIM To assess the safety and feasibility of three-dimensional computed-tomography bronchography and angiography (3D-CTBA) in performing video-assisted thoracoscopic surgery (VATS) for lung cancers.
METHODS In this study, we enrolled 123 patients who consented to undergo thoracoscopic segmentectomy and lobectomy assisted by 3D-CTBA between May 2017 and June 2019. The image data of enhanced computed tomography (CT) scans was reconstructed three-dimensionally by the Mimics software. The results of preoperative 3D-CTBA, in combination with intraoperative navigation, guided the surgery.
RESULTS A total of 59 women and 64 men were enrolled, of whom 57 (46.3%) underwent segmentectomy and 66 (53.7%) underwent lobectomy. The majority of tumor appearance on CT was part-solid ground-glass nodule (pGGN; 55.3%). The mean duration of chest tube placement was 3.5 ± 1.6 d, and the average length of postoperative hospital stay was 6.8 ± 1.8 d. Surgical complications included one case of pneumonia and four cases of prolonged air leak lasting > 5 d. Notably, there was no intraoperative massive hemorrhage, postoperative intensive-care unit stay, or 30-d mortality. Preoperative 3D-CTBA images can display clearly and vividly the targeted structure and the variations of vessels and bronchi. To reduce the risk of locoregional recurrence, the application of 3D-CTBA with a virtual 3D surgical margin help the VATS surgeon determine accurate distances and positional relations among the tumor, bronchial trees, and the intersegmental vessels. Three-dimensional navigation was performed to confirm the segmental structure, precisely cut off the targeted segment, and avoid intersegmental veins injury.
CONCLUSION VATS and 3D-CTBA worked in harmony in our study. This combination also provided a new pattern of transition from lesion-directed location of tumors to computer-aided surgery for the management of early lung cancer.
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Affiliation(s)
- Yun-Jiang Wu
- Department of Thoracic Surgery, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225009, Jiangsu Province, China
| | - Qing-Tong Shi
- Department of Thoracic Surgery, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225009, Jiangsu Province, China
| | - Yong Zhang
- Department of Radiology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou 225009, Jiangsu Province, China
| | - Ya-Li Wang
- Department of Respiratory Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
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Vonder M, Dorrius MD, Vliegenthart R. Latest CT technologies in lung cancer screening: protocols and radiation dose reduction. Transl Lung Cancer Res 2021; 10:1154-1164. [PMID: 33718053 PMCID: PMC7947397 DOI: 10.21037/tlcr-20-808] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The aim of this review is to provide clinicians and technicians with an overview of the development of CT protocols in lung cancer screening. CT protocols have evolved from pre-fixed settings in early lung cancer screening studies starting in 2004 towards automatic optimized settings in current international guidelines. The acquisition protocols of large lung cancer screening studies and guidelines are summarized. Radiation dose may vary considerably between CT protocols, but has reduced gradually over the years. Ultra-low dose acquisition can be achieved by applying latest dose reduction techniques. The use of low tube current or tin-filter in combination with iterative reconstruction allow to reduce the radiation dose to a submilliSievert level. However, one should be cautious in reducing the radiation dose to ultra-low dose settings since performed studies lacked generalizability. Continuous efforts are made by international radiology organizations to streamline the CT data acquisition and image quality assurance and to keep track of new developments in CT lung cancer screening. Examples like computer-aided diagnosis and radiomic feature extraction are discussed and current limitations are outlined. Deep learning-based solutions in post-processing of CT images are provided. Finally, future perspectives and recommendations are provided for lung cancer screening CT protocols.
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Affiliation(s)
- Marleen Vonder
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Monique D Dorrius
- Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rozemarijn Vliegenthart
- Department of Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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8
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Blazis SP, Dickerscheid DBM, Linsen PVM, Martins Jarnalo CO. Effect of CT reconstruction settings on the performance of a deep learning based lung nodule CAD system. Eur J Radiol 2021; 136:109526. [PMID: 33453573 DOI: 10.1016/j.ejrad.2021.109526] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 12/23/2020] [Accepted: 01/04/2021] [Indexed: 01/08/2023]
Abstract
PURPOSE To study the effect of different reconstruction parameter settings on the performance of a commercially available deep learning based pulmonary nodule CAD system. MATERIALS AND METHODS We performed a retrospective analysis of 24 chest CT scans, reconstructed at 16 different reconstruction settings for two different iterative reconstruction algorithms (SAFIRE and ADMIRE) varying in slice thickness, kernel size and iterative reconstruction level strength using a commercially available deep learning pulmonary nodule CAD system. The DL-CAD software was evaluated at 25 different sensitivity threshold settings and nodules detected by the DL-CAD software were matched against a reference standard based on the consensus reading of three radiologists. RESULTS A total of 384 CT reconstructions was analysed from 24 patients, resulting in a total of 5786 found nodules. We matched the detected nodules against the reference standard, defined by a team of thoracic radiologists, and showed a gradual drop in recall, and an improvement in precision when the iterative strength levels were increased for a constant kernel size. The optimal DL-CAD threshold setting for use in our clinical workflow was found to be 0.88 with an F2 of 0.73 ± 0.053. CONCLUSIONS The DL-CAD system behaves differently on IR data than on FBP data, there is a gradual drop in recall, and growth in precision when the iterative strength levels are increased. As a result, caution should be taken when implementing deep learning software in a hospital with multiple CT scanners and different reconstruction protocols. To the best of our knowledge, this is the first study that demonstrates this result from a DL-CAD system on clinical data.
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Affiliation(s)
- Stephan P Blazis
- Department of Clinical Physics, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands.
| | | | - Philip V M Linsen
- Department of Radiology, Albert Schweitzer Ziekenhuis, Dordrecht, the Netherlands
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9
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Fu B, Wang G, Wu M, Li W, Zheng Y, Chu Z, Lv F. Influence of CT effective dose and convolution kernel on the detection of pulmonary nodules in different artificial intelligence software systems: A phantom study. Eur J Radiol 2020; 126:108928. [DOI: 10.1016/j.ejrad.2020.108928] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 01/05/2020] [Accepted: 02/28/2020] [Indexed: 12/29/2022]
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10
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Nomura Y, Miki S, Hayashi N, Hanaoka S, Sato I, Yoshikawa T, Masutani Y, Abe O. Novel platform for development, training, and validation of computer-assisted detection/diagnosis software. Int J Comput Assist Radiol Surg 2020; 15:661-672. [PMID: 32157503 PMCID: PMC7142060 DOI: 10.1007/s11548-020-02132-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 02/27/2020] [Indexed: 11/28/2022]
Abstract
PURPOSE To build a novel, open-source, purely web-based platform system to address problems in the development and clinical use of computer-assisted detection/diagnosis (CAD) software. The new platform system will replace the existing system for the development and validation of CAD software, Clinical Infrastructure for Radiologic Computation of United Solutions (CIRCUS). METHODS In our new system, the two top-level applications visible to users are the web-based image database (CIRCUS DB; database) and the Docker plug-in-based CAD execution platform (CIRCUS CS; clinical server). These applications are built on top of a shared application programming interface server, a three-dimensional image viewer component, and an image repository. RESULTS We successfully installed our new system into a Linux server at two clinical sites. A total of 1954 cases were registered in CIRCUS DB. We have been utilizing CIRCUS CS with four Docker-based CAD plug-ins. CONCLUSIONS We have successfully built a new version of the CIRCUS system. Our platform was successfully implemented at two clinical sites, and we plan to publish it as an open-source software project.
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Affiliation(s)
- Yukihiro Nomura
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
| | - Soichiro Miki
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Naoto Hayashi
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Shouhei Hanaoka
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
| | - Issei Sato
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
- Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Center for Advanced Intelligence Project, RIKEN, Tokyo, Japan
| | - Takeharu Yoshikawa
- Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yoshitaka Masutani
- Graduate School of Information Sciences, Hiroshima City University, Hiroshima, Japan
| | - Osamu Abe
- Department of Radiology, The University of Tokyo Hospital, Tokyo, Japan
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11
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Myocardial extracellular volume quantification in cardiac CT: comparison of the effects of two different iterative reconstruction algorithms with MRI as a reference standard. Eur Radiol 2019; 30:691-701. [DOI: 10.1007/s00330-019-06418-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 08/03/2019] [Accepted: 08/08/2019] [Indexed: 01/23/2023]
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12
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Can fully iterative reconstruction technique enable routine abdominal CT at less than 1 mSv? Eur J Radiol Open 2019; 6:225-230. [PMID: 31304196 PMCID: PMC6603257 DOI: 10.1016/j.ejro.2019.05.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2019] [Revised: 04/15/2019] [Accepted: 05/13/2019] [Indexed: 12/16/2022] Open
Abstract
Objective We assessed the effect of the forward projected model-based reconstruction technique (FIRST) on lesion detection of routine abdomen CT at <1 mSv. Materials and methods Thirty-seven adult patients gave written informed consent for acquisition of low-dose CT (LDCT) immediately after their clinically-indicated, standard of care dose (SDCT), routine abdomen CT on a 640-slice MDCT (Aquillion One, Canon Medical System). The LDCT series were reconstructed with FIRST (at STD (Standard) and STR (Strong) levels), and SDCT series with filtered back projection (FBP). Two radiologists assessed lesions in LD-FBP and FIRST images followed by SDCT images. Then, SDCT and LDCT were compared for presence of artifacts in a randomized and blinded fashion. Patient demographics, size and radiation dose descriptors (CTDIvol, DLP) were recorded. Descriptive statistics and inter-observer variability were calculated for data analysis. Results Mean CTDIvol for SDCT and LDCT were 13 ± 4.7 mGy and 2.2 ± 0.8 mGy, respectively. There were 46 true positive lesions detected on SDCT. Radiologists detected 38/46 lesions on LD-FIRST-STD compared to 26/46 lesions on LD-FIRST-STR. The eight lesions (liver and kidney cysts, pancreatic lesions, sub-cm peritoneal lymph node) missed on LD-FIRST-STD were seen in patients with BMI > 25.8 kg/m2. Diagnostic confidence for lesion assessment was optimal in LD-FIRST-STD setting in most patients regardless of their size. The inter-observer agreement (kappa-value) for overall image quality were 0.98 and 0.84 for LD-FIRST-STD and STR levels, respectively. Conclusion FIRST enabled optimal lesion detection in routine abdomen CT at less than 1 mSv radiation dose in patients with body mass less than ≤25.8 kg/m2.
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Milanese G, Silva M, Frauenfelder T, Eberhard M, Sabia F, Martini C, Marchianò A, Prokop M, Sverzellati N, Pastorino U. Comparison of ultra-low dose chest CT scanning protocols for the detection of pulmonary nodules: a phantom study. TUMORI JOURNAL 2019; 105:394-403. [PMID: 31041885 DOI: 10.1177/0300891619847271] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE To test ultra-low-dose computed tomography (ULDCT) scanning protocols for the detection of pulmonary nodules (PN). METHODS A chest phantom containing 19 solid and 11 subsolid PNs was scanned on a third-generation dual-source computed tomography (CT) scanner. Five ULDCT scans (Sn100kVp and 120, 70, 50, 30, and 20 reference mAs, using tube current modulation), reconstructed with iterative reconstruction (IR) algorithm at strength levels 2, 3, 4, and 5, were compared with standard CT (120kVp, 150 reference mAs, using tube current modulation). PNs were subjectively assessed according to a 4-point scale: 0, nondetectable nodule; 1, detectable nodule, very unlikely to be correctly measured; 2, detectable nodule, likely to be correctly measured; 3, PN quality equal to standard of reference. PN scores were analysed according to the Lung Imaging Reporting and Data System (Lung-RADS), simulating detection of nodules at baseline and incidence screening round. RESULTS For the baseline round, there were 17 Lung-RADS 2, 4 Lung-RADS 3, 8 Lung-RADS 4A, and 1 Lung-RADS 4B PNs. They were detectable in any ULDCT protocol, with the exception of 1 nondetectable part-solid nodule in 1 scanning protocol (120 reference mAs; IR strength: 3). For the incidence round, there were 4 Lung-RADS 2, 14 Lung-RADS 3, 2 Lung-RADS 4A, and 10 Lung-RADS 4B PNs. Ten were nondetectable in at least one ULDCT dataset; however, they were at least detectable in ULDCT with 70 reference mAs (IR strength: 4 and 5). CONCLUSIONS ULDCT scanning protocols allowing the detection of PNs can be proposed for the purpose of lung cancer screening.
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Affiliation(s)
- Gianluca Milanese
- Division of Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy.,Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Mario Silva
- Division of Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy.,Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Thomas Frauenfelder
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Matthias Eberhard
- Institute for Diagnostic and Interventional Radiology, University Hospital Zurich, Zurich, Switzerland
| | - Federica Sabia
- Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Chiara Martini
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Alfonso Marchianò
- Department of Radiology, IRCCS Istituto Nazionale Tumori, Milan, Italy
| | - Mathias Prokop
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Nicola Sverzellati
- Division of Radiology, Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Ugo Pastorino
- Department of Thoracic Surgery, IRCCS Istituto Nazionale Tumori, Milan, Italy
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14
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Yoshida Y, Sakane T, Isogai J, Suzuki Y, Miki S, Nomura Y, Nakajima J. Computer-assisted detection of metastatic lung tumors on computed tomography. Asian Cardiovasc Thorac Ann 2019; 27:199-207. [PMID: 30789307 DOI: 10.1177/0218492319831836] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND This retrospective study examined the performance of computer-assisted detection in the identification of pulmonary metastases. METHODS Fifty-five patients (41.8% male) who underwent surgery for metastatic lung tumors in our hospital from 2005 to 2012 were included. Computer-assisted detection software configured to display the top five nodule candidates according to likelihood was applied as the first reader for the preoperative computed tomography images. Results from the software were classified as "metastatic nodule", "benign nodule", or "false-positive finding" by two observers. RESULTS Computer-assisted detection identified 85.3% (64/75) of pulmonary metastases that radiologists had detected, and 3 more (4%, 3/75) that radiologists had overlooked. Nodule candidates identified by computer-assisted detection included 86 benign nodules (median size 3.1 mm, range 1.2-18.7 mm) and 121 false-positive findings. CONCLUSIONS Computer-assisted detection identified pulmonary metastases overlooked by radiologists. However, this was at the cost of identifying a substantial number of benign nodules and false-positive findings.
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Affiliation(s)
| | - Tomoya Sakane
- 2 Department of Radiology, Asahi General Hospital, Chiba, Japan
| | - Jun Isogai
- 2 Department of Radiology, Asahi General Hospital, Chiba, Japan
| | - Yoshio Suzuki
- 3 Department of Pathology, Asahi General Hospital, Chiba, Japan
| | - Soichiro Miki
- 4 Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Yukihiro Nomura
- 4 Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan
| | - Jun Nakajima
- 5 Department of Thoracic Surgery, The University of Tokyo Hospital, Tokyo, Japan
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15
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Wagner AK, Hapich A, Psychogios MN, Teichgräber U, Malich A, Papageorgiou I. Computer-Aided Detection of Pulmonary Nodules in Computed Tomography Using ClearReadCT. J Med Syst 2019; 43:58. [PMID: 30706143 DOI: 10.1007/s10916-019-1180-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2018] [Accepted: 01/22/2019] [Indexed: 12/19/2022]
Abstract
This study evaluates the accuracy of a computer-aided detection (CAD) application for pulmonary nodular lesions (PNL) in computed tomography (CT) scans, the ClearReadCT (Riverain Technologies). The study was retrospective for 106 biopsied PNLs from 100 patients. Seventy-five scans were Contrast-Enhanced (CECT) and 25 received no enhancer (NECT). Axial reconstructions in soft-tissue and lung kernel were applied at three different slice thicknesses, 0.75 mm (CECT/NECT n = 25/6), 1.5 mm (n = 18/9) and 3.0 mm (n = 43/18). We questioned the effect of (1) enhancer, (2) kernel and (3) slice thickness on the CAD performance. Our main findings are: (1) Vessel suppression is effective and specific in both NECT and CECT. (2) Contrast enhancement significantly increased the CAD sensitivity from 60% in NECT to 80% in CECT, P = 0.025 Fischer's exact test. (3) The CAD sensitivity was 84% in 3 mm slices compared to 68% in 0.75 mm slices, P > 0.2 Fischer's exact test. (4) Small lesions of low attenuation were detected with higher sensitivity. (5) Lung kernel reconstructions increased the false positive rate without affecting the sensitivity (P > 0.05 McNemar's test). In conclusion, ClearReadCT showed an optimized sensitivity of 84% and a positive predictive value of 67% in enhanced lung scans with thick, soft kernel reconstructions. NECT, thin slices and lung kernel reconstruction were associated with inferior performance.
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Affiliation(s)
- Anne-Kathrin Wagner
- Institute of Diagnostic and Interventional Radiology, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany.,Institute of Radiology, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany
| | - Arno Hapich
- Department of Thoracic Surgery, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany
| | - Marios Nikos Psychogios
- Institute of Diagnostic and Interventional Neuroradiology, University Medicine Göttingen, Robert Koch street 40, 37075, Göttingen, Germany
| | - Ulf Teichgräber
- Institute of Diagnostic and Interventional Radiology, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany
| | - Ansgar Malich
- Institute of Radiology, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany
| | - Ismini Papageorgiou
- Institute of Diagnostic and Interventional Radiology, University Hospital Jena, Am Klinikum 1, 07747, Jena, Germany. .,Institute of Radiology, Südharz Hospital Nordhausen, Dr.-Robert-Koch street 39, 99734, Nordhausen, Germany.
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16
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An Update on the European Lung Cancer Screening Trials and Comparison of Lung Cancer Screening Recommendations in Europe. J Thorac Imaging 2019; 34:65-71. [DOI: 10.1097/rti.0000000000000367] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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17
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Ohno Y, Koyama H, Seki S, Kishida Y, Yoshikawa T. Radiation dose reduction techniques for chest CT: Principles and clinical results. Eur J Radiol 2018; 111:93-103. [PMID: 30691672 DOI: 10.1016/j.ejrad.2018.12.017] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 12/06/2018] [Accepted: 12/16/2018] [Indexed: 11/19/2022]
Abstract
Computer tomography plays a major role in the evaluation of thoracic diseases, especially since the advent of the multidetector-row CT (MDCT) technology. However, the increase use of this technique has raised some concerns about the resulting radiation dose. In this review, we will present the various methods allowing limiting the radiation dose exposure resulting from chest CT acquisitions, including the options of image filtering and iterative reconstruction (IR) algorithms. The clinical applications of reduced dose protocols will be reviewed, especially for lung nodule detection and diagnosis of pulmonary thromboembolism. The performance of reduced dose protocols for infiltrative lung disease assessment will also be discussed. Lastly, the influence of using IR algorithms on computer-aided detection and volumetry of lung nodules, as well as on quantitative and functional assessment of chest diseases will be presented and discussed.
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Affiliation(s)
- Yoshiharu Ohno
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Japan.
| | | | - Shinichiro Seki
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Japan
| | - Yuji Kishida
- Division of Radiology, Department of Radiology, Kobe University Graduate School of Medicine, Japan
| | - Takeshi Yoshikawa
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Japan
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18
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Nishio M, Sugiyama O, Yakami M, Ueno S, Kubo T, Kuroda T, Togashi K. 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. PLoS One 2018; 13:e0200721. [PMID: 30052644 PMCID: PMC6063408 DOI: 10.1371/journal.pone.0200721] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 05/29/2018] [Indexed: 12/31/2022] Open
Abstract
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.
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Affiliation(s)
- Mizuho Nishio
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
- * E-mail: ,
| | - Osamu Sugiyama
- Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
| | - Masahiro Yakami
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
- Preemptive Medicine and Lifestyle-related Disease Research Center, Kyoto University Hospital, Kyoto, Japan
| | - Syoko Ueno
- Department of Social Informatics, Kyoto University Graduate School of Informatics Yoshidahonmachi, Kyoto, Japan
| | - Takeshi Kubo
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Tomohiro Kuroda
- Division of Medical Information Technology and Administrative Planning, Kyoto University Hospital, Kyoto, Japan
| | - Kaori Togashi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
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19
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Usefulness of model-based iterative reconstruction in semi-automatic volumetry for ground-glass nodules at ultra-low-dose CT: a phantom study. Radiol Phys Technol 2018; 11:235-241. [DOI: 10.1007/s12194-018-0442-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 01/20/2018] [Accepted: 01/25/2018] [Indexed: 01/11/2023]
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20
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Ohno Y, Aoyagi K, Chen Q, Sugihara N, Iwasawa T, Okada F, Aoki T. Comparison of computer-aided detection (CADe) capability for pulmonary nodules among standard-, reduced- and ultra-low-dose CTs with and without hybrid type iterative reconstruction technique. Eur J Radiol 2018; 100:49-57. [PMID: 29496079 DOI: 10.1016/j.ejrad.2018.01.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 12/07/2017] [Accepted: 01/08/2018] [Indexed: 12/17/2022]
Abstract
PURPOSE To directly compare the effect of a reconstruction algorithm on nodule detection capability of the computer-aided detection (CADe) system using standard-dose, reduced-dose and ultra-low dose chest CTs with and without adaptive iterative dose reduction 3D (AIDR 3D). MATERIALS AND METHODS Our institutional review board approved this study, and written informed consent was obtained from each patient. Standard-, reduced- and ultra-low-dose chest CTs (250 mA, 50 mA and 10 mA) were used to examine 40 patients, 21 males (mean age ± standard deviation: 63.1 ± 11.0 years) and 19 females (mean age, 65.1 ± 12.7 years), and reconstructed as 1 mm-thick sections. Detection of nodule equal to more than 4 mm in dimeter was automatically performed by our proprietary CADe software. The utility of iterative reconstruction method for improving nodule detection capability, sensitivity and false positive rate (/case) of the CADe system using all protocols were compared by means of McNemar's test or signed rank test. RESULTS Sensitivity (SE: 0.43) and false-positive rate (FPR: 7.88) of ultra-low-dose CT without AIDR 3D was significantly inferior to those of standard-dose CTs (with AIDR 3D: SE, 0.78, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.80, p < .0001, FPR: 2.63, p < .0001), reduced-dose CTs (with AIDR 3D: SE, 0.81, p < .0001, FPR, 3.05, p < .0001; and without AIDR 3D: SE, 0.62, p < .0001, FPR: 2.95, p < .0001) and ultra-low-dose CT with AIDR 3D (SE, 0.79, p < .0001, FPR, 4.88, p = .0001). CONCLUSION The AIDR 3D has a significant positive effect on nodule detection capability of the CADe system even when radiation dose is reduced.
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Affiliation(s)
- Yoshiharu Ohno
- Division of Functional and Diagnostic Imaging Research, Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan; Advanced Biomedical Imaging Research Center, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
| | - Kota Aoyagi
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Qi Chen
- Canon Medical Systems (China) Co., Ltd., Beijing, China
| | - Naoki Sugihara
- Canon Medical Systems Corporation, Otawara, Tochigi, Japan
| | - Tae Iwasawa
- Department of Radiology, Kanagawa Cardiovascular and Respiratory Center, Yokohama, Kanagawa, Japan
| | - Fumito Okada
- Department of Radiology, Faculty of Medicine, University of Oita, Yufu, Oita, Japan
| | - Takatoshi Aoki
- Department of Radiology, University of Occupational and Environmental Health, Kitakyushu, Fukuoka, Japan
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