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Impact of a content-based image retrieval system on the interpretation of chest CTs of patients with diffuse parenchymal lung disease. Eur Radiol 2022; 33:360-367. [PMID: 35779087 DOI: 10.1007/s00330-022-08973-3] [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: 02/03/2022] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES Content-based image retrieval systems (CBIRS) are a new and potentially impactful tool for radiological reporting, but their clinical evaluation is largely missing. This study aimed at assessing the effect of CBIRS on the interpretation of chest CT scans from patients with suspected diffuse parenchymal lung disease (DPLD). MATERIALS AND METHODS A total of 108 retrospectively included chest CT scans with 22 unique, clinically and/or histopathologically verified diagnoses were read by eight radiologists (four residents, four attending, median years reading chest CT scans 2.1± 0.7 and 12 ± 1.8, respectively). The radiologists read and provided the suspected diagnosis at a certified radiological workstation to simulate clinical routine. Half of the readings were done without CBIRS and half with the additional support of the CBIRS. The CBIRS retrieved the most likely of 19 lung-specific patterns from a large database of 6542 thin-section CT scans and provided relevant information (e.g., a list of potential differential diagnoses). RESULTS Reading time decreased by 31.3% (p < 0.001) despite the radiologists searching for additional information more frequently when the CBIRS was available (154 [72%] vs. 95 [43%], p < 0.001). There was a trend towards higher overall diagnostic accuracy (42.2% vs 34.7%, p = 0.083) when the CBIRS was available. CONCLUSION The use of the CBIRS had a beneficial impact on the reading time of chest CT scans in cases with DPLD. In addition, both resident and attending radiologists were more likely to consult informational resources if they had access to the CBIRS. Further studies are needed to confirm the observed trend towards increased diagnostic accuracy with the use of a CBIRS in practice. KEY POINTS • A content-based image retrieval system for supporting the diagnostic process of reading chest CT scans can decrease reading time by 31.3% (p < 0.001). • The decrease in reading time was present despite frequent usage of the content-based image retrieval system. • Additionally, a trend towards higher diagnostic accuracy was observed when using the content-based image retrieval system (42.2% vs 34.7%, p = 0.083).
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Kuramoto Y, Wada N, Uchiyama Y. Prediction of pathological complete response using radiomics on MRI in patients with breast cancer undergoing neoadjuvant pharmacotherapy. Int J Comput Assist Radiol Surg 2022; 17:619-625. [PMID: 35023018 DOI: 10.1007/s11548-022-02560-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 01/03/2022] [Indexed: 12/17/2022]
Abstract
PURPOSE Neoadjuvant pharmacotherapy is essential for patients with breast cancer who wish to preserve the breast by shrinking the malignant tumor, allowing breast-conserving surgery. It may eliminate cancer cells completely, which is known as pathologic complete response (pCR). Patients with pCR have a lower risk of recurrence. The purpose of this study was to develop a method for predicting patients who achieve pCR by neoadjuvant pharmacotherapy using radiomic features in MR images. METHODS Fat-suppressed T2-weighted MR images of 64 cases were identified from the ISPY1 dataset. There were 26 cases of pCR and 38 cases of non-pCR. The image slice with the largest tumor diameter was selected from MR images, and the tumor region was manually segmented. A total of 371 radiomic features were calculated from the tumor region. We selected nine radiomic features using Lasso in this study. A support vector machine (SVM) with nine radiomic features was used for predicting patients with pCR. RESULTS The result of the ROC analysis showed that the area under the curve of SVM was 0.92 for distinguishing between pCR and non-pCR. Although the input data contain data that were misclassified by SVM, the survival curve classified into the pCR group was at a higher position than the non-pCR group. However, the log-rank test was [Formula: see text]. CONCLUSIONS We developed a method to predict patients with pCR by neoadjuvant pharmacotherapy using noninvasive MR images. The survival curve of patients classified as having pCR by the proposed method was higher than those classified as non-pCR. Since the proposed method predicts patients who achieve pCR by neoadjuvant pharmacotherapy, it enhances the value of preoperative image information.
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Affiliation(s)
- Yuka Kuramoto
- Graduate School of Health Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, Kumamoto, 862-0976, Japan
| | - Natsumi Wada
- Graduate School of Health Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, Kumamoto, 862-0976, Japan
| | - Yoshikazu Uchiyama
- Department of Medical Image Sciences, Faculty of Life Sciences, Kumamoto University, 4-24-1 Kuhonji, Chuo-ku, Kumamoto, Kumamoto, 862-0976, Japan.
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Morishita J, Ueda Y. [Recent Review Article in Radiological Physics and Technology]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2021; 77:876-878. [PMID: 34421077 DOI: 10.6009/jjrt.2021_jsrt_77.8.876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Sotomayor CG, Mendoza M, Castañeda V, Farías H, Molina G, Pereira G, Härtel S, Solar M, Araya M. Content-Based Medical Image Retrieval and Intelligent Interactive Visual Browser for Medical Education, Research and Care. Diagnostics (Basel) 2021; 11:diagnostics11081470. [PMID: 34441404 PMCID: PMC8392084 DOI: 10.3390/diagnostics11081470] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Revised: 08/03/2021] [Accepted: 08/09/2021] [Indexed: 01/17/2023] Open
Abstract
Medical imaging is essential nowadays throughout medical education, research, and care. Accordingly, international efforts have been made to set large-scale image repositories for these purposes. Yet, to date, browsing of large-scale medical image repositories has been troublesome, time-consuming, and generally limited by text search engines. A paradigm shift, by means of a query-by-example search engine, would alleviate these constraints and beneficially impact several practical demands throughout the medical field. The current project aims to address this gap in medical imaging consumption by developing a content-based image retrieval (CBIR) system, which combines two image processing architectures based on deep learning. Furthermore, a first-of-its-kind intelligent visual browser was designed that interactively displays a set of imaging examinations with similar visual content on a similarity map, making it possible to search for and efficiently navigate through a large-scale medical imaging repository, even if it has been set with incomplete and curated metadata. Users may, likewise, provide text keywords, in which case the system performs a content- and metadata-based search. The system was fashioned with an anonymizer service and designed to be fully interoperable according to international standards, to stimulate its integration within electronic healthcare systems and its adoption for medical education, research and care. Professionals of the healthcare sector, by means of a self-administered questionnaire, underscored that this CBIR system and intelligent interactive visual browser would be highly useful for these purposes. Further studies are warranted to complete a comprehensive assessment of the performance of the system through case description and protocolized evaluations by medical imaging specialists.
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Affiliation(s)
- Camilo G. Sotomayor
- Radiology Department, Clinical Hospital University of Chile, University of Chile, Santiago 8380453, Chile; (C.G.S.); (G.P.)
- Center for Medical Informatics and Telemedicine, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (V.C.); (S.H.)
- Department of Electronic Engineering, Federico Santa Maria Technical University, Valparaíso 2340000, Chile
| | - Marcelo Mendoza
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Víctor Castañeda
- Center for Medical Informatics and Telemedicine, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (V.C.); (S.H.)
- Department of Medical Technology, Faculty of Medicine, University of Chile, Santiago 8380453, Chile
| | - Humberto Farías
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Gabriel Molina
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Gonzalo Pereira
- Radiology Department, Clinical Hospital University of Chile, University of Chile, Santiago 8380453, Chile; (C.G.S.); (G.P.)
| | - Steffen Härtel
- Center for Medical Informatics and Telemedicine, Institute of Biomedical Sciences, Faculty of Medicine, University of Chile, Santiago 8380453, Chile; (V.C.); (S.H.)
| | - Mauricio Solar
- Department of Informatics, Federico Santa Maria Technical University, Santiago 8380453, Chile; (M.M.); (H.F.); (G.M.); (M.S.)
| | - Mauricio Araya
- Department of Electronic Engineering, Federico Santa Maria Technical University, Valparaíso 2340000, Chile
- Correspondence:
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Morishita J, Ueda Y. New solutions for automated image recognition and identification: challenges to radiologic technology and forensic pathology. Radiol Phys Technol 2021; 14:123-133. [PMID: 33710498 DOI: 10.1007/s12194-021-00611-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 02/26/2021] [Accepted: 02/28/2021] [Indexed: 11/30/2022]
Abstract
This paper outlines the history of biometrics for personal identification, the current status of the initial biological fingerprint techniques for digital chest radiography, and patient verification during medical imaging, such as computed tomography and magnetic resonance imaging. Automated image recognition and identification developed for clinical images without metadata could also be applied to the identification of victims in mass disasters or other unidentified individuals. The development of methods that are adaptive to a wide range of recent imaging modalities in the fields of radiologic technology, patient safety, forensic pathology, and forensic odontology is still in its early stages. However, its importance in practice will continue to increase in the future.
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Affiliation(s)
- Junji Morishita
- Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka, Fukuoka, 812-8582, Japan.
| | - Yasuyuki Ueda
- Department of Medical Physics and Engineering, Area of Medical Imaging Technology and Science, Division of Health Sciences, Graduate School of Medicine, Osaka University, 1-7 Yamadaoka, Suita, Osaka, 565-0871, Japan.
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Goto Y, Tanaka R, Furuya Y, Maezato M, Akita F, Shiraishi J. [Investigation of Clinical Utility of Radiological Technologist's Reading Report as a Second Opinion for Medical Doctor Reading of Digital Mammogram]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2020; 76:997-1008. [PMID: 33087659 DOI: 10.6009/jjrt.2020_jsrt_76.10.997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
PURPOSE We investigated the clinical utility of a radiological technologist's (RT)'s reports (RRs) as a second opinion by the free-response receiver operating characteristic (FROC) observer study that compared the performance of medical doctors' (MDs') reading of digital mammogram with and without consulting the RR. METHOD One hundred women (39 malignant, 61 benign or normal) who underwent diagnostic mammography were selected from among 1674 routine clinical images classified by the degree of difficulty and categories for inclusion in the FROC study. The first FROC study performed by three RTs (RT 1-3) was conducted to collect the data for RR utilized in the second FROC study. The second FROC study was performed by five MDs, and the statistical significance of MDs' performances with and without reference to the RR was investigated by figure of merit (FOM). RESULT The FOM values of three RTs obtained in the first FROC study were 0.529, 0.576, and 0.539, respectively. In the second FROC study, RT 2 had the highest FOM, RT 1 the lowest false positives/case, and RT 3 the highest sensitivity. The average FOM values in the second FROC study for the five MDs with/without reference to the RR were as follows: RT 2's RR was 0.534/0.588 (p=0.003), RT 1's RR was 0.500/0.545 (p=0.099), and RT 3's RR was 0.569/0.592 (p=0.324). CONCLUSION We concluded that the MDs' performance of reading mammogram was statistically improved by consulting the RR when the RT's reading skill was high.
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Affiliation(s)
- Yuka Goto
- Breast and Imaging Center of St. Marianna University School of Medicine
| | - Rie Tanaka
- School of Health Sciences, College of Medical, Pharmaceutical and Health Sciences, Kanazawa University
| | - Yuko Furuya
- Breast and Imaging Center of St. Marianna University School of Medicine
| | - Miwako Maezato
- Imaging Center, St. Marianna University School of Medicine
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Fujita H. AI-based computer-aided diagnosis (AI-CAD): the latest review to read first. Radiol Phys Technol 2020; 13:6-19. [PMID: 31898014 DOI: 10.1007/s12194-019-00552-4] [Citation(s) in RCA: 90] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 12/19/2019] [Accepted: 12/21/2019] [Indexed: 12/18/2022]
Abstract
The third artificial intelligence (AI) boom is coming, and there is an inkling that the speed of its evolution is quickly increasing. In games like chess, shogi, and go, AI has already defeated human champions, and the fact that it is able to achieve autonomous driving is also being realized. Under these circumstances, AI has evolved and diversified at a remarkable pace in medical diagnosis, especially in diagnostic imaging. Therefore, this commentary focuses on AI in medical diagnostic imaging and explains the recent development trends and practical applications of computer-aided detection/diagnosis using artificial intelligence, especially deep learning technology, as well as some topics surrounding it.
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Affiliation(s)
- Hiroshi Fujita
- Department of Electrical, Electronic and Computer Engineering, Faculty of Engineering, Gifu University, 1-1, Yanagido, Gifu City, Gifu, 501-1194, Japan.
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Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 2018; 29:102-127. [PMID: 30553609 DOI: 10.1016/j.zemedi.2018.11.002] [Citation(s) in RCA: 695] [Impact Index Per Article: 115.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Revised: 11/19/2018] [Accepted: 11/21/2018] [Indexed: 02/06/2023]
Abstract
What has happened in machine learning lately, and what does it mean for the future of medical image analysis? Machine learning has witnessed a tremendous amount of attention over the last few years. The current boom started around 2009 when so-called deep artificial neural networks began outperforming other established models on a number of important benchmarks. Deep neural networks are now the state-of-the-art machine learning models across a variety of areas, from image analysis to natural language processing, and widely deployed in academia and industry. These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, but put particular focus on deep learning in MRI. Our aim is threefold: (i) give a brief introduction to deep learning with pointers to core references; (ii) indicate how deep learning has been applied to the entire MRI processing chain, from acquisition to image retrieval, from segmentation to disease prediction; (iii) provide a starting point for people interested in experimenting and perhaps contributing to the field of deep learning for medical imaging by pointing out good educational resources, state-of-the-art open-source code, and interesting sources of data and problems related medical imaging.
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Affiliation(s)
- Alexander Selvikvåg Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Department of Computing, Mathematics and Physics, Western Norway University of Applied Sciences, Norway.
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Haukeland University Hospital, Norway; Neuroinformatics and Image Analysis Laboratory, Department of Biomedicine, University of Bergen, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Norway.
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Arimura H, Soufi M, Ninomiya K, Kamezawa H, Yamada M. Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis. Radiol Phys Technol 2018; 11:365-374. [PMID: 30374837 DOI: 10.1007/s12194-018-0486-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 10/14/2018] [Accepted: 10/17/2018] [Indexed: 12/22/2022]
Abstract
Computer-aided diagnosis (CAD) is a field that is essentially based on pattern recognition that improves the accuracy of a diagnosis made by a physician who takes into account the computer's "opinion" derived from the quantitative analysis of radiological images. Radiomics is a field based on data science that massively and comprehensively analyzes a large number of medical images to extract a large number of phenotypic features reflecting disease traits, and explores the associations between the features and patients' prognoses for precision medicine. According to the definitions for both, you may think that radiomics is not a paraphrase of CAD, but you may also think that these definitions are "image manipulation". However, there are common and different features between the two fields. This review paper elaborates on these common and different features and introduces the potential of radiomics for cancer diagnosis and treatment by comparing it with CAD.
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Affiliation(s)
- Hidetaka Arimura
- Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Mazen Soufi
- Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan
| | - Kenta Ninomiya
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hidemi Kamezawa
- Department of Radiological Technology, Faculty of Fukuoka Medical Technology, Teikyo University, Omuta, Japan
| | - Masahiro Yamada
- Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
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