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Al Mohammad B, Aldaradkeh A, Gharaibeh M, Reed W. Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges. Br J Radiol 2024; 97:763-769. [PMID: 38273675 PMCID: PMC11027289 DOI: 10.1093/bjr/tqae022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 09/30/2023] [Accepted: 01/21/2024] [Indexed: 01/27/2024] Open
Abstract
OBJECTIVES The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challenges and barriers that radiologists and radiographers face when learning about AI. METHODS A nationwide, online descriptive cross-sectional survey was distributed to radiologists and radiographers working in hospitals and medical centres from May 29, 2023 to July 30, 2023. The questionnaire examined the participants' opinions, feelings, and predictions regarding AI and its applications in the radiology department. Descriptive statistics were used to report the participants' demographics and responses. Five-points Likert-scale data were reported using divergent stacked bar graphs to highlight any central tendencies. RESULTS Responses were collected from 258 participants, revealing a positive attitude towards implementing AI. Both radiologists and radiographers predicted breast imaging would be the subspecialty most impacted by the AI revolution. MRI, mammography, and CT were identified as the primary modalities with significant importance in the field of AI application. The major barrier encountered by radiologists and radiographers when learning about AI was the lack of mentorship, guidance, and support from experts. CONCLUSION Participants demonstrated a positive attitude towards learning about AI and implementing it in the radiology practice. However, radiologists and radiographers encounter several barriers when learning about AI, such as the absence of experienced professionals support and direction. ADVANCES IN KNOWLEDGE Radiologists and radiographers reported several barriers to AI learning, with the most significant being the lack of mentorship and guidance from experts, followed by the lack of funding and investment in new technologies.
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Affiliation(s)
- Badera Al Mohammad
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Afnan Aldaradkeh
- Department of Allied Medical Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan
| | - Monther Gharaibeh
- Department of Special Surgery, Faculty of Medicine, The Hashemite University, Zarqa 13133, Jordan
| | - Warren Reed
- Discipline of Medical Imaging Science, Faculty of Medicine and Health, University of Sydney 2006, Sydney, NSW, Australia
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Mokhtari A, Casale R, Salahuddin Z, Paquier Z, Guiot T, Woodruff HC, Lambin P, Van Laethem JL, Hendlisz A, Bali MA. Development of Clinical Radiomics-Based Models to Predict Survival Outcome in Pancreatic Ductal Adenocarcinoma: A Multicenter Retrospective Study. Diagnostics (Basel) 2024; 14:712. [PMID: 38611625 PMCID: PMC11011556 DOI: 10.3390/diagnostics14070712] [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: 02/17/2024] [Revised: 03/11/2024] [Accepted: 03/21/2024] [Indexed: 04/14/2024] Open
Abstract
PURPOSE This multicenter retrospective study aims to identify reliable clinical and radiomic features to build machine learning models that predict progression-free survival (PFS) and overall survival (OS) in pancreatic ductal adenocarcinoma (PDAC) patients. METHODS Between 2010 and 2020 pre-treatment contrast-enhanced CT scans of 287 pathology-confirmed PDAC patients from two sites of the Hopital Universitaire de Bruxelles (HUB) and from 47 hospitals within the HUB network were retrospectively analysed. Demographic, clinical, and survival data were also collected. Gross tumour volume (GTV) and non-tumoral pancreas (RPV) were semi-manually segmented and radiomics features were extracted. Patients from two HUB sites comprised the training dataset, while those from the remaining 47 hospitals of the HUB network constituted the testing dataset. A three-step method was used for feature selection. Based on the GradientBoostingSurvivalAnalysis classifier, different machine learning models were trained and tested to predict OS and PFS. Model performances were assessed using the C-index and Kaplan-Meier curves. SHAP analysis was applied to allow for post hoc interpretability. RESULTS A total of 107 radiomics features were extracted from each of the GTV and RPV. Fourteen subgroups of features were selected: clinical, GTV, RPV, clinical & GTV, clinical & GTV & RPV, GTV-volume and RPV-volume both for OS and PFS. Subsequently, 14 Gradient Boosting Survival Analysis models were trained and tested. In the testing dataset, the clinical & GTV model demonstrated the highest performance for OS (C-index: 0.72) among all other models, while for PFS, the clinical model exhibited a superior performance (C-index: 0.70). CONCLUSIONS An integrated approach, combining clinical and radiomics features, excels in predicting OS, whereas clinical features demonstrate strong performance in PFS prediction.
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Affiliation(s)
- Ayoub Mokhtari
- Radiology Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Roberto Casale
- Radiology Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Zohaib Salahuddin
- Department of Precision Medicine, GROW—Research Institute for Oncology and Reproduction, Maastricht University, 6220MD Maastricht, The Netherlands
| | - Zelda Paquier
- Medical Physics Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Thomas Guiot
- Medical Physics Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Henry C. Woodruff
- Department of Precision Medicine, GROW—Research Institute for Oncology and Reproduction, Maastricht University, 6220MD Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229HX Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, GROW—Research Institute for Oncology and Reproduction, Maastricht University, 6220MD Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, GROW—School for Oncology and Reproduction, Maastricht University Medical Centre+, 6229HX Maastricht, The Netherlands
| | - Jean-Luc Van Laethem
- Department of Gastroenterology and Digestive Oncology, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Alain Hendlisz
- Department of Gastroenterology and Digestive Oncology, Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
| | - Maria Antonietta Bali
- Radiology Department, Institut Jules Bordet Hôpital Universitaire de Bruxelles, Université Libre de Bruxelles, 1070 Brussels, Belgium
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Barat M, Pellat A, Hoeffel C, Dohan A, Coriat R, Fishman EK, Nougaret S, Chu L, Soyer P. CT and MRI of abdominal cancers: current trends and perspectives in the era of radiomics and artificial intelligence. Jpn J Radiol 2024; 42:246-260. [PMID: 37926780 DOI: 10.1007/s11604-023-01504-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/12/2023] [Indexed: 11/07/2023]
Abstract
Abdominal cancers continue to pose daily challenges to clinicians, radiologists and researchers. These challenges are faced at each stage of abdominal cancer management, including early detection, accurate characterization, precise assessment of tumor spread, preoperative planning when surgery is anticipated, prediction of tumor aggressiveness, response to therapy, and detection of recurrence. Technical advances in medical imaging, often in combination with imaging biomarkers, show great promise in addressing such challenges. Information extracted from imaging datasets owing to the application of radiomics can be used to further improve the diagnostic capabilities of imaging. However, the analysis of the huge amount of data provided by these advances is a difficult task in daily practice. Artificial intelligence has the potential to help radiologists in all these challenges. Notably, the applications of AI in the field of abdominal cancers are expanding and now include diverse approaches for cancer detection, diagnosis and classification, genomics and detection of genetic alterations, analysis of tumor microenvironment, identification of predictive biomarkers and follow-up. However, AI currently has some limitations that need further refinement for implementation in the clinical setting. This review article sums up recent advances in imaging of abdominal cancers in the field of image/data acquisition, tumor detection, tumor characterization, prognosis, and treatment response evaluation.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Anna Pellat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Hopital Robert Debré, CHU Reims, Université Champagne-Ardennes, 51092, Reims, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
| | - Romain Coriat
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France
- Department of Gastroenterology and Digestive Oncology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Stéphanie Nougaret
- Department of Radiology, Montpellier Cancer Institute, 34000, Montpellier, France
- PINKCC Lab, IRCM, U1194, 34000, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014, Paris, France.
- Faculté de Médecine, Université Paris Cité, 75006, Paris, France.
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Anghel C, Grasu MC, Anghel DA, Rusu-Munteanu GI, Dumitru RL, Lupescu IG. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics (Basel) 2024; 14:438. [PMID: 38396476 PMCID: PMC10887967 DOI: 10.3390/diagnostics14040438] [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: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
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Affiliation(s)
- Cristian Anghel
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Mugur Cristian Grasu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Denisa Andreea Anghel
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Gina-Ionela Rusu-Munteanu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Radu Lucian Dumitru
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Ioana Gabriela Lupescu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
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Nakaura T, Yoshida N, Kobayashi N, Shiraishi K, Nagayama Y, Uetani H, Kidoh M, Hokamura M, Funama Y, Hirai T. Preliminary assessment of automated radiology report generation with generative pre-trained transformers: comparing results to radiologist-generated reports. Jpn J Radiol 2024; 42:190-200. [PMID: 37713022 PMCID: PMC10811038 DOI: 10.1007/s11604-023-01487-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/29/2023] [Indexed: 09/16/2023]
Abstract
PURPOSE In this preliminary study, we aimed to evaluate the potential of the generative pre-trained transformer (GPT) series for generating radiology reports from concise imaging findings and compare its performance with radiologist-generated reports. METHODS This retrospective study involved 28 patients who underwent computed tomography (CT) scans and had a diagnosed disease with typical imaging findings. Radiology reports were generated using GPT-2, GPT-3.5, and GPT-4 based on the patient's age, gender, disease site, and imaging findings. We calculated the top-1, top-5 accuracy, and mean average precision (MAP) of differential diagnoses for GPT-2, GPT-3.5, GPT-4, and radiologists. Two board-certified radiologists evaluated the grammar and readability, image findings, impression, differential diagnosis, and overall quality of all reports using a 4-point scale. RESULTS Top-1 and Top-5 accuracies for the different diagnoses were highest for radiologists, followed by GPT-4, GPT-3.5, and GPT-2, in that order (Top-1: 1.00, 0.54, 0.54, and 0.21, respectively; Top-5: 1.00, 0.96, 0.89, and 0.54, respectively). There were no significant differences in qualitative scores about grammar and readability, image findings, and overall quality between radiologists and GPT-3.5 or GPT-4 (p > 0.05). However, qualitative scores of the GPT series in impression and differential diagnosis scores were significantly lower than those of radiologists (p < 0.05). CONCLUSIONS Our preliminary study suggests that GPT-3.5 and GPT-4 have the possibility to generate radiology reports with high readability and reasonable image findings from very short keywords; however, concerns persist regarding the accuracy of impressions and differential diagnoses, thereby requiring verification by radiologists.
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Affiliation(s)
- Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan.
| | - Naofumi Yoshida
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Naoki Kobayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Kaori Shiraishi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Masamichi Hokamura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
| | - Yoshinori Funama
- Department of Medical Physics, Faculty of Life Sciences, Kumamoto University, Honjo 1-1-1, Kumamoto, 860-8556, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, 1-1-1 Honjo, Chuo-ku, Kumamoto-shi, Kumamoto, 860-8556, Japan
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Fujima N, Nakagawa J, Kameda H, Ikebe Y, Harada T, Shimizu Y, Tsushima N, Kano S, Homma A, Kwon J, Yoneyama M, Kudo K. Improvement of image quality in diffusion-weighted imaging with model-based deep learning reconstruction for evaluations of the head and neck. MAGMA (NEW YORK, N.Y.) 2023:10.1007/s10334-023-01129-4. [PMID: 37989922 DOI: 10.1007/s10334-023-01129-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 10/18/2023] [Accepted: 10/23/2023] [Indexed: 11/23/2023]
Abstract
OBJECTIVES To investigate the utility of deep learning (DL)-based image reconstruction using a model-based approach in head and neck diffusion-weighted imaging (DWI). MATERIALS AND METHODS We retrospectively analyzed the cases of 41 patients who underwent head/neck DWI. The DWI in 25 patients demonstrated an untreated lesion. We performed qualitative and quantitative assessments in the DWI analyses with both deep learning (DL)- and conventional parallel imaging (PI)-based reconstructions. For the qualitative assessment, we visually evaluated the overall image quality, soft tissue conspicuity, degree of artifact(s), and lesion conspicuity based on a five-point system. In the quantitative assessment, we measured the signal-to-noise ratio (SNR) of the bilateral parotid glands, submandibular gland, the posterior muscle, and the lesion. We then calculated the contrast-to-noise ratio (CNR) between the lesion and the adjacent muscle. RESULTS Significant differences were observed in the qualitative analysis between the DWI with PI-based and DL-based reconstructions for all of the evaluation items (p < 0.001). In the quantitative analysis, significant differences in the SNR and CNR between the DWI with PI-based and DL-based reconstructions were observed for all of the evaluation items (p = 0.002 ~ p < 0.001). DISCUSSION DL-based image reconstruction with the model-based technique effectively provided sufficient image quality in head/neck DWI.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 060-8638, Japan.
| | - Junichi Nakagawa
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 060-8638, Japan
| | - Hiroyuki Kameda
- Faculty of Dental Medicine Department of Radiology, Hokkaido University, N13 W7, Kita-Ku, Sapporo, Hokkaido, 060-8586, Japan
| | - Yohei Ikebe
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Taisuke Harada
- Center for Cause of Death Investigation, Faculty of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
| | - Yukie Shimizu
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, 060-8638, Japan
| | - Nayuta Tsushima
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita Ku, Sapporo, 060-8638, Japan
| | - Satoshi Kano
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita Ku, Sapporo, 060-8638, Japan
| | - Akihiro Homma
- Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, N15 W7, Kita Ku, Sapporo, 060-8638, Japan
| | - Jihun Kwon
- Philips Japan, 3-37 Kohnan 2-Chome, Minato-Ku, Tokyo, 108-8507, Japan
| | - Masami Yoneyama
- Philips Japan, 3-37 Kohnan 2-Chome, Minato-Ku, Tokyo, 108-8507, Japan
| | - Kohsuke Kudo
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, N15 W7, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Medical AI Research and Development Center, Hokkaido University Hospital, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
- Global Center for Biomedical Science and Engineering, Faculty of Medicine, Hokkaido University, N14 W5, Kita-Ku, Sapporo, Hokkaido, 060-8638, Japan
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S D, D S, P VK, K S. Enhancing pancreatic cancer classification through dynamic weighted ensemble: a game theory approach. Comput Methods Biomech Biomed Engin 2023:1-25. [PMID: 37982236 DOI: 10.1080/10255842.2023.2281277] [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: 08/10/2023] [Accepted: 11/02/2023] [Indexed: 11/21/2023]
Abstract
The significant research carried out on medical healthcare networks is giving computing innovations lots of space to produce the most recent innovations. Pancreatic cancer, which ranks among of the most common tumors that are thought to be fatal and unsuspected since it is positioned in the region of the abdomen beyond the stomach and can't be adequately treated once diagnosed. In radiological imaging, such as MRI and CT, computer-aided diagnosis (CAD), quantitative evaluations, and automated pancreatic cancer classification approaches are routinely provided. This study provides a dynamic weighted ensemble framework for pancreatic cancer classification inspired by game theory. Grey Level Co-occurrence Matrix (GLCM) is utilized for feature extraction, together with Gaussian kernel-based fuzzy rough sets theory (GKFRST) for feature reduction and the Random Forest (RF) classifier for categorization. The ResNet50 and VGG16 are used in the transfer learning (TL) paradigm. The combination of the outcomes from the TL paradigm and the RF classifier paradigm is suggested using an innovative ensemble classifier that relies on the game theory method. When compared with the current models, the ensemble technique considerably increases the pancreatic cancer classification accuracy and yields exceptional performance. The study improves the categorization of pancreatic cancer by using game theory, a mathematical paradigm that simulates strategic interactions. Because game theory has been not frequently used in the discipline of cancer categorization, this research is distinctive in its methodology.
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Affiliation(s)
- Dhanasekaran S
- Department of Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India
| | - Silambarasan D
- Department of Electronics and Communication Engineering, Sri Venkateswara College of Engineering, Tamil Nadu, India
| | - Vivek Karthick P
- Department of Electronics and Communication Engineering, Sona College of Technology, Salem, Tamil Nadu, India
| | - Sudhakar K
- Department of Electronics and Communication Engineering, M. Kumarasamy College of Engineering, Karur, Tamil Nadu, India
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8
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Homps M, Soyer P, Coriat R, Dermine S, Pellat A, Fuks D, Marchese U, Terris B, Groussin L, Dohan A, Barat M. A preoperative computed tomography radiomics model to predict disease-free survival in patients with pancreatic neuroendocrine tumors. Eur J Endocrinol 2023; 189:476-484. [PMID: 37787635 DOI: 10.1093/ejendo/lvad130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 08/18/2023] [Accepted: 08/22/2023] [Indexed: 10/04/2023]
Abstract
IMPORTANCE Imaging has demonstrated capabilities in the diagnosis of pancreatic neuroendocrine tumors (pNETs), but its utility for prognostic prediction has not been elucidated yet. OBJECTIVE The aim of this study was to build a radiomics model using preoperative computed tomography (CT) data that may help predict recurrence-free survival (RFS) or OS in patients with pNET. DESIGN We performed a retrospective observational study in a cohort of French patients with pNETs. PARTICIPANTS Patients with surgically resected pNET and available CT examinations were included. INTERVENTIONS Radiomics features of preoperative CT data were extracted using 3D-Slicer® software with manual segmentation. Discriminant features were selected with penalized regression using least absolute shrinkage and selection operator method with training on the tumor Ki67 rate (≤2 or >2). Selected features were used to build a radiomics index ranging from 0 to 1. OUTCOME AND MEASURE A receiving operator curve was built to select an optimal cutoff value of the radiomics index to predict patient RFS and OS. Recurrence-free survival and OS were assessed using Kaplan-Meier analysis. RESULTS Thirty-seven patients (median age, 61 years; 20 men) with 37 pNETs (grade 1, 21/37 [57%]; grade 2, 12/37 [32%]; grade 3, 4/37 [11%]) were included. Patients with a radiomics index >0.4 had a shorter median RFS (36 months; range: 1-133) than those with a radiomics index ≤0.4 (84 months; range: 9-148; P = .013). No associations were found between the radiomics index and OS (P = .86).
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Affiliation(s)
- Margaux Homps
- Department of Diagnostic and Interventional Imaging, APHP, Hôpital Cochin, Paris F-75014, France
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
| | - Philippe Soyer
- Department of Diagnostic and Interventional Imaging, APHP, Hôpital Cochin, Paris F-75014, France
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
| | - Romain Coriat
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
- Department of Gastroenterology and Digestive Oncology, AP-HP, Hôpital Cochin, Paris F-75014, France
| | - Solène Dermine
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
- Department of Gastroenterology and Digestive Oncology, AP-HP, Hôpital Cochin, Paris F-75014, France
| | - Anna Pellat
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
- Department of Gastroenterology and Digestive Oncology, AP-HP, Hôpital Cochin, Paris F-75014, France
| | - David Fuks
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
- Department of Surgery, Hôpital Cochin, APHP, Paris F-75014, France
| | - Ugo Marchese
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
- Department of Surgery, Hôpital Cochin, APHP, Paris F-75014, France
| | - Benoit Terris
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
- Department of Pathology, Center for Rare Adrenal Diseases, AP-HP, Hôpital Cochin, Paris F-75014, France
| | - Lionel Groussin
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
- Department of Endocrinology, Center for Rare Adrenal Diseases, AP-HP, Hôpital Cochin, Paris F-75014, France
| | - Anthony Dohan
- Department of Diagnostic and Interventional Imaging, APHP, Hôpital Cochin, Paris F-75014, France
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
| | - Maxime Barat
- Department of Diagnostic and Interventional Imaging, APHP, Hôpital Cochin, Paris F-75014, France
- Faculté de Médecine, Université Paris Cité, Paris F-75006, France
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9
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Fujioka T, Kubota K, Hsu JF, Chang RF, Sawada T, Ide Y, Taruno K, Hankyo M, Kurita T, Nakamura S, Tateishi U, Takei H. Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound. J Med Ultrason (2001) 2023; 50:511-520. [PMID: 37400724 PMCID: PMC10556122 DOI: 10.1007/s10396-023-01332-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/03/2023] [Indexed: 07/05/2023]
Abstract
PURPOSE This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound. METHODS The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection. RESULT The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe (p < 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43). CONCLUSION The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis.
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Affiliation(s)
- Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Kazunori Kubota
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan.
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, 2-1-50 Minami-Koshigaya, Koshigaya, Saitama, 343-8555, Japan.
| | - Jen Feng Hsu
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, ROC
| | - Ruey Feng Chang
- Department of Computer Science and Information Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd, Taipei, 10617, Taiwan, ROC
| | - Terumasa Sawada
- Department of Breast Surgery, NTT Medical Center Tokyo, 5-9-22 Higashi-Gotanda, Shinagawa-ku, Tokyo, 141-8625, Japan
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Yoshimi Ide
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
- Department of Breast Oncology, Kikuna Memorial Hospital, 4-4-27 Kikuna, Kohoku-ku, Yokohama, 222-0011, Japan
| | - Kanae Taruno
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Meishi Hankyo
- Department of Breast Surgical Oncology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8602, Japan
| | - Tomoko Kurita
- Department of Breast Surgical Oncology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8602, Japan
| | - Seigo Nakamura
- Department of Breast Surgical Oncology, Department of Surgery, Showa University School of Medicine, 1-5-8 Hatanodai, Shinagawa-ku, Tokyo, 142-8666, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8510, Japan
| | - Hiroyuki Takei
- Department of Breast Surgical Oncology, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8602, Japan
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10
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Fujima N, Kamagata K, Ueda D, Fujita S, Fushimi Y, Yanagawa M, Ito R, Tsuboyama T, Kawamura M, Nakaura T, Yamada A, Nozaki T, Fujioka T, Matsui Y, Hirata K, Tatsugami F, Naganawa S. Current State of Artificial Intelligence in Clinical Applications for Head and Neck MR Imaging. Magn Reson Med Sci 2023; 22:401-414. [PMID: 37532584 PMCID: PMC10552661 DOI: 10.2463/mrms.rev.2023-0047] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 07/09/2023] [Indexed: 08/04/2023] Open
Abstract
Due primarily to the excellent soft tissue contrast depictions provided by MRI, the widespread application of head and neck MRI in clinical practice serves to assess various diseases. Artificial intelligence (AI)-based methodologies, particularly deep learning analyses using convolutional neural networks, have recently gained global recognition and have been extensively investigated in clinical research for their applicability across a range of categories within medical imaging, including head and neck MRI. Analytical approaches using AI have shown potential for addressing the clinical limitations associated with head and neck MRI. In this review, we focus primarily on the technical advancements in deep-learning-based methodologies and their clinical utility within the field of head and neck MRI, encompassing aspects such as image acquisition and reconstruction, lesion segmentation, disease classification and diagnosis, and prognostic prediction for patients presenting with head and neck diseases. We then discuss the limitations of current deep-learning-based approaches and offer insights regarding future challenges in this field.
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Affiliation(s)
- Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Kyoto, Kyoto, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Kumamoto, Kumamoto, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Okayama, Okayama, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Sapporo, Hokkaido, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Hiroshima, Hiroshima, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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11
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Yanagawa M, Ito R, Nozaki T, Fujioka T, Yamada A, Fujita S, Kamagata K, Fushimi Y, Tsuboyama T, Matsui Y, Tatsugami F, Kawamura M, Ueda D, Fujima N, Nakaura T, Hirata K, Naganawa S. New trend in artificial intelligence-based assistive technology for thoracic imaging. LA RADIOLOGIA MEDICA 2023; 128:1236-1249. [PMID: 37639191 PMCID: PMC10547663 DOI: 10.1007/s11547-023-01691-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/02/2023] [Accepted: 07/25/2023] [Indexed: 08/29/2023]
Abstract
Although there is no solid agreement for artificial intelligence (AI), it refers to a computer system with intelligence similar to that of humans. Deep learning appeared in 2006, and more than 10 years have passed since the third AI boom was triggered by improvements in computing power, algorithm development, and the use of big data. In recent years, the application and development of AI technology in the medical field have intensified internationally. There is no doubt that AI will be used in clinical practice to assist in diagnostic imaging in the future. In qualitative diagnosis, it is desirable to develop an explainable AI that at least represents the basis of the diagnostic process. However, it must be kept in mind that AI is a physician-assistant system, and the final decision should be made by the physician while understanding the limitations of AI. The aim of this article is to review the application of AI technology in diagnostic imaging from PubMed database while particularly focusing on diagnostic imaging in thorax such as lesion detection and qualitative diagnosis in order to help radiologists and clinicians to become more familiar with AI in thorax.
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Affiliation(s)
- Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-City, Osaka, 565-0871, Japan.
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, Japan
| | - Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-2621, Japan
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita-City, Osaka, 565-0871, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-8558, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-Machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nish I 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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12
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Gao L, Yusufaly TI, Williamson CW, Mell LK. Optimized Atlas-Based Auto-Segmentation of Bony Structures from Whole-Body Computed Tomography. Pract Radiat Oncol 2023; 13:e442-e450. [PMID: 37030539 DOI: 10.1016/j.prro.2023.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 04/09/2023]
Abstract
PURPOSE To develop and test a method for fully automated segmentation of bony structures from whole-body computed tomography (CT) and evaluate its performance compared with manual segmentation. METHODS AND MATERIALS We developed a workflow for automatic whole-body bone segmentation using atlas-based segmentation (ABS) method with a postprocessing module (ABSPP) in MIM MAESTRO software. Fifty-two CT scans comprised the training set to build the atlas library, and 29 CT scans comprised the test set. To validate the workflow, we compared Dice similarity coefficient (DSC), mean distance to agreement, and relative volume errors between ABSPP and ABS with no postprocessing (ABSNPP) with manual segmentation as the reference (gold standard). RESULTS The ABSPP method resulted in significantly improved segmentation accuracy (DSC range, 0.85-0.98) compared with the ABSNPP method (DSC range, 0.55-0.87; P < .001). Mean distance to agreement results also indicated high agreement between ABSPP and manual reference delineations (range, 0.11-1.56 mm), which was significantly improved compared with ABSNPP (range, 1.00-2.34 mm) for the majority of tested bony structures. Relative volume errors were also significantly lower for ABSPP compared with ABSNPP for most bony structures. CONCLUSIONS We developed a fully automated MIM workflow for bony structure segmentation from whole-body CT, which exhibited high accuracy compared with manual delineation. The integrated postprocessing module significantly improved workflow performance.
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Affiliation(s)
- Lei Gao
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California
| | - Tahir I Yusufaly
- Russell H. Morgan Department of Radiology and Radiologic Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland
| | - Casey W Williamson
- Department of Radiation Medicine, Oregon Health Sciences University, Portland, Oregon
| | - Loren K Mell
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, California.
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13
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Ahmed TM, Kawamoto S, Hruban RH, Fishman EK, Soyer P, Chu LC. A primer on artificial intelligence in pancreatic imaging. Diagn Interv Imaging 2023; 104:435-447. [PMID: 36967355 DOI: 10.1016/j.diii.2023.03.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
Artificial Intelligence (AI) is set to transform medical imaging by leveraging the vast data contained in medical images. Deep learning and radiomics are the two main AI methods currently being applied within radiology. Deep learning uses a layered set of self-correcting algorithms to develop a mathematical model that best fits the data. Radiomics converts imaging data into mineable features such as signal intensity, shape, texture, and higher-order features. Both methods have the potential to improve disease detection, characterization, and prognostication. This article reviews the current status of artificial intelligence in pancreatic imaging and critically appraises the quality of existing evidence using the radiomics quality score.
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Affiliation(s)
- Taha M Ahmed
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Sol Goldman Pancreatic Research Center, Department of Pathology, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Philippe Soyer
- Université Paris Cité, Faculté de Médecine, Department of Radiology, Hôpital Cochin-APHP, 75014, 75006, Paris, France, 7501475006
| | - Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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14
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Yamada A, Kamagata K, Hirata K, Ito R, Nakaura T, Ueda D, Fujita S, Fushimi Y, Fujima N, Matsui Y, Tatsugami F, Nozaki T, Fujioka T, Yanagawa M, Tsuboyama T, Kawamura M, Naganawa S. Clinical applications of artificial intelligence in liver imaging. LA RADIOLOGIA MEDICA 2023:10.1007/s11547-023-01638-1. [PMID: 37165151 DOI: 10.1007/s11547-023-01638-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 04/21/2023] [Indexed: 05/12/2023]
Abstract
This review outlines the current status and challenges of the clinical applications of artificial intelligence in liver imaging using computed tomography or magnetic resonance imaging based on a topic analysis of PubMed search results using latent Dirichlet allocation. LDA revealed that "segmentation," "hepatocellular carcinoma and radiomics," "metastasis," "fibrosis," and "reconstruction" were current main topic keywords. Automatic liver segmentation technology using deep learning is beginning to assume new clinical significance as part of whole-body composition analysis. It has also been applied to the screening of large populations and the acquisition of training data for machine learning models and has resulted in the development of imaging biomarkers that have a significant impact on important clinical issues, such as the estimation of liver fibrosis, recurrence, and prognosis of malignant tumors. Deep learning reconstruction is expanding as a new technological clinical application of artificial intelligence and has shown results in reducing contrast and radiation doses. However, there is much missing evidence, such as external validation of machine learning models and the evaluation of the diagnostic performance of specific diseases using deep learning reconstruction, suggesting that the clinical application of these technologies is still in development.
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Affiliation(s)
- Akira Yamada
- Department of Radiology, Shinshu University School of Medicine, Matsumoto, Nagano, Japan.
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-Ku, Tokyo, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Chuo-Ku, Kumamoto, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan
| | - Shohei Fujita
- Department of Radiology, University of Tokyo, Tokyo, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-Ku, Okayama, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima City, Hiroshima, Japan
| | - Taiki Nozaki
- Department of Radiology, St. Luke's International Hospital, Tokyo, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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15
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Endocrine Tumor Classification via Machine-Learning-Based Elastography: A Systematic Scoping Review. Cancers (Basel) 2023; 15:cancers15030837. [PMID: 36765794 PMCID: PMC9913672 DOI: 10.3390/cancers15030837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 01/26/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023] Open
Abstract
Elastography complements traditional medical imaging modalities by mapping tissue stiffness to identify tumors in the endocrine system, and machine learning models can further improve diagnostic accuracy and reliability. Our objective in this review was to summarize the applications and performance of machine-learning-based elastography on the classification of endocrine tumors. Two authors independently searched electronic databases, including PubMed, Scopus, Web of Science, IEEEXpress, CINAHL, and EMBASE. Eleven (n = 11) articles were eligible for the review, of which eight (n = 8) focused on thyroid tumors and three (n = 3) considered pancreatic tumors. In all thyroid studies, the researchers used shear-wave ultrasound elastography, whereas the pancreas researchers applied strain elastography with endoscopy. Traditional machine learning approaches or the deep feature extractors were used to extract the predetermined features, followed by classifiers. The applied deep learning approaches included the convolutional neural network (CNN) and multilayer perceptron (MLP). Some researchers considered the mixed or sequential training of B-mode and elastographic ultrasound data or fusing data from different image segmentation techniques in machine learning models. All reviewed methods achieved an accuracy of ≥80%, but only three were ≥90% accurate. The most accurate thyroid classification (94.70%) was achieved by applying sequential training CNN; the most accurate pancreas classification (98.26%) was achieved using a CNN-long short-term memory (LSTM) model integrating elastography with B-mode and Doppler images.
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16
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Søreide K, Ismail W, Roalsø M, Ghotbi J, Zaharia C. Early Diagnosis of Pancreatic Cancer: Clinical Premonitions, Timely Precursor Detection and Increased Curative-Intent Surgery. Cancer Control 2023; 30:10732748231154711. [PMID: 36916724 PMCID: PMC9893084 DOI: 10.1177/10732748231154711] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND The overall poor prognosis in pancreatic cancer is related to late clinical detection. Early diagnosis remains a considerable challenge in pancreatic cancer. Unfortunately, the onset of clinical symptoms in patients usually indicate advanced disease or presence of metastasis. ANALYSIS AND RESULTS Currently, there are no designated diagnostic or screening tests for pancreatic cancer in clinical use. Thus, identifying risk groups, preclinical risk factors or surveillance strategies to facilitate early detection is a target for ongoing research. Hereditary genetic syndromes are a obvious, but small group at risk, and warrants close surveillance as suggested by society guidelines. Screening for pancreatic cancer in asymptomatic individuals is currently associated with the risk of false positive tests and, thus, risk of harms that outweigh benefits. The promise of cancer biomarkers and use of 'omics' technology (genomic, transcriptomics, metabolomics etc.) has yet to see a clinical breakthrough. Several proposed biomarker studies for early cancer detection lack external validation or, when externally validated, have shown considerably lower accuracy than in the original data. Biopsies or tissues are often taken at the time of diagnosis in research studies, hence invalidating the value of a time-dependent lag of the biomarker to detect a pre-clinical, asymptomatic yet operable cancer. New technologies will be essential for early diagnosis, with emerging data from image-based radiomics approaches, artificial intelligence and machine learning suggesting avenues for improved detection. CONCLUSIONS Early detection may come from analytics of various body fluids (eg 'liquid biopsies' from blood or urine). In this review we present some the technological platforms that are explored for their ability to detect pancreatic cancer, some of which may eventually change the prospects and outcomes of patients with pancreatic cancer.
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Affiliation(s)
- Kjetil Søreide
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway.,Department of Clinical Medicine, University of Bergen, Bergen, Norway.,Gastrointestinal Translational Research Group, Laboratory for Molecular Medicine, 60496Stavanger University Hospital, Stavanger, Norway
| | - Warsan Ismail
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway
| | - Marcus Roalsø
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway.,Gastrointestinal Translational Research Group, Laboratory for Molecular Medicine, 60496Stavanger University Hospital, Stavanger, Norway.,Department of Quality and Health Technology, 60496University of Stavanger, Stavanger, Norway
| | - Jacob Ghotbi
- Department of Gastrointestinal Surgery, HPB unit, 60496Stavanger University Hospital, Stavanger, Norway
| | - Claudia Zaharia
- Gastrointestinal Translational Research Group, Laboratory for Molecular Medicine, 60496Stavanger University Hospital, Stavanger, Norway.,Department of Pathology, 60496Stavanger University Hospital, Stavanger, Norway
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17
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Lacroix M, Aouad T, Feydy J, Biau D, Larousserie F, Fournier L, Feydy A. Artificial intelligence in musculoskeletal oncology imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:18-23. [PMID: 36270953 DOI: 10.1016/j.diii.2022.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 10/05/2022] [Indexed: 01/10/2023]
Abstract
Artificial intelligence (AI) is increasingly being studied in musculoskeletal oncology imaging. AI has been applied to both primary and secondary bone tumors and assessed for various predictive tasks that include detection, segmentation, classification, and prognosis. Still, in the field of clinical research, further efforts are needed to improve AI reproducibility and reach an acceptable level of evidence in musculoskeletal oncology. This review describes the basic principles of the most common AI techniques, including machine learning, deep learning and radiomics. Then, recent developments and current results of AI in the field of musculoskeletal oncology are presented. Finally, limitations and future perspectives of AI in this field are discussed.
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Affiliation(s)
- Maxime Lacroix
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Theodore Aouad
- Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France
| | - Jean Feydy
- Université Paris Cité, HeKA team, Inria Paris, Inserm, 75006, Paris, France
| | - David Biau
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Orthopedic Surgery, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Frédérique Larousserie
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Pathology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
| | - Laure Fournier
- Department of Radiology, Hôpital Européen Georges Pompidou, Assistance Publique-Hôpitaux de Paris, Paris, 75015, France; Université Paris Cité, Faculté de Médecine, Paris, 75006, France; PARCC UMRS 970, INSERM, Paris 75015, France
| | - Antoine Feydy
- Université Paris Cité, Faculté de Médecine, Paris, 75006, France; Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris, 75014, France
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18
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Barat M, Gaillard M, Cottereau AS, Fishman EK, Assié G, Jouinot A, Hoeffel C, Soyer P, Dohan A. Artificial intelligence in adrenal imaging: A critical review of current applications. Diagn Interv Imaging 2023; 104:37-42. [PMID: 36163169 DOI: 10.1016/j.diii.2022.09.003] [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: 09/14/2022] [Accepted: 09/14/2022] [Indexed: 01/10/2023]
Abstract
In the elective field of adrenal imaging, artificial intelligence (AI) can be used for adrenal lesion detection, characterization, hypersecreting syndrome management and patient follow-up. Although a perfect AI tool that includes all required steps from detection to analysis does not exist yet, multiple AI algorithms have been developed and tested with encouraging results. However, AI in this setting is still at an early stage. In this regard, most published studies about AI in adrenal gland imaging report preliminary results that do not have yet daily applications in clinical practice. In this review, recent developments and current results of AI in the field of adrenal imaging are presented. Limitations and future perspectives of AI are discussed.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France.
| | - Martin Gaillard
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Digestive, Hepatobiliary and Pancreatic Surgery, Hôpital Cochin, AP-HP, Paris 75014, France
| | - Anne-Ségolène Cottereau
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Nuclear Medicine, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Guillaume Assié
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Endocrinology, Center for Rare Adrenal Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | - Anne Jouinot
- Université Paris Cité, Faculté de Médecine, Paris 75006, France; Department of Endocrinology, Center for Rare Adrenal Diseases, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France
| | | | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris, Paris 75014, France; Université Paris Cité, Faculté de Médecine, Paris 75006, France
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19
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Tarján D, Hegyi P. Acute Pancreatitis Severity Prediction: It Is Time to Use Artificial Intelligence. J Clin Med 2022; 12:jcm12010290. [PMID: 36615090 PMCID: PMC9821076 DOI: 10.3390/jcm12010290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
The clinical course of acute pancreatitis (AP) can be variable depending on the severity of the disease, and it is crucial to predict the probability of organ failure to initiate early adequate treatment and management. Therefore, possible high-risk patients should be admitted to a high-dependence unit. For risk assessment, we have three options: (1) There are univariate biochemical markers for predicting severe AP. One of their main characteristics is that the absence or excess of these factors affects the outcome of AP in a dose-dependent manner. Unfortunately, all of these parameters have low accuracy; therefore, they cannot be used in clinical settings. (2) Score systems have been developed to prognosticate severity by using 4-25 factors. They usually require multiple parameters that are not measured on a daily basis, and they often require more than 24 h for completion, resulting in the loss of valuable time. However, these scores can foresee specific organ failure or severity, but they only use dichotomous parameters, resulting in information loss. Therefore, their use in clinical settings is limited. (3) Artificial intelligence can detect the complex nonlinear relationships between multiple biochemical parameters and disease outcomes. We have recently developed the very first easy-to-use tool, EASY-APP, which uses multiple continuous variables that are available at the time of admission. The web-based application does not require all of the parameters for prediction, allowing early and easy use on admission. In the future, prognostic scores should be developed with the help of artificial intelligence to avoid information loss and to provide a more individualized risk assessment.
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Affiliation(s)
- Dorottya Tarján
- Heart and Vascular Center, Division of Pancreatic Diseases, Semmelweis University, 1083 Budapest, Hungary
- Centre for Translational Medicine, Semmelweis University, 1085 Budapest, Hungary
- Institute for Translational Medicine, Medical School, University of Pécs, 7623 Pécs, Hungary
| | - Péter Hegyi
- Heart and Vascular Center, Division of Pancreatic Diseases, Semmelweis University, 1083 Budapest, Hungary
- Centre for Translational Medicine, Semmelweis University, 1085 Budapest, Hungary
- Institute for Translational Medicine, Medical School, University of Pécs, 7623 Pécs, Hungary
- Translational Pancreatology Research Group, Interdisciplinary Centre of Excellence for Research Development and Innovation University of Szeged, 6725 Szeged, Hungary
- Correspondence: ; Tel.: +36-703751031
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20
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Fujioka T, Satoh Y, Imokawa T, Mori M, Yamaga E, Takahashi K, Kubota K, Onishi H, Tateishi U. Proposal to Improve the Image Quality of Short-Acquisition Time-Dedicated Breast Positron Emission Tomography Using the Pix2pix Generative Adversarial Network. Diagnostics (Basel) 2022; 12:diagnostics12123114. [PMID: 36553120 PMCID: PMC9777139 DOI: 10.3390/diagnostics12123114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 11/26/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022] Open
Abstract
This study aimed to evaluate the ability of the pix2pix generative adversarial network (GAN) to improve the image quality of low-count dedicated breast positron emission tomography (dbPET). Pairs of full- and low-count dbPET images were collected from 49 breasts. An image synthesis model was constructed using pix2pix GAN for each acquisition time with training (3776 pairs from 16 breasts) and validation data (1652 pairs from 7 breasts). Test data included dbPET images synthesized by our model from 26 breasts with short acquisition times. Two breast radiologists visually compared the overall image quality of the original and synthesized images derived from the short-acquisition time data (scores of 1−5). Further quantitative evaluation was performed using a peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). In the visual evaluation, both readers revealed an average score of >3 for all images. The quantitative evaluation revealed significantly higher SSIM (p < 0.01) and PSNR (p < 0.01) for 26 s synthetic images and higher PSNR for 52 s images (p < 0.01) than for the original images. Our model improved the quality of low-count time dbPET synthetic images, with a more significant effect on images with lower counts.
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Affiliation(s)
- Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Yoko Satoh
- Yamanashi PET Imaging Clinic, Chuo City 409-3821, Japan
- Department of Radiology, University of Yamanashi, Chuo City 409-3898, Japan
- Correspondence:
| | - Tomoki Imokawa
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Mio Mori
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Emi Yamaga
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Kanae Takahashi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
| | - Kazunori Kubota
- Department of Radiology, Dokkyo Medical University Saitama Medical Center, Koshigaya 343-8555, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Chuo City 409-3898, Japan
| | - Ukihide Tateishi
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo 113-8510, Japan
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21
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Yuan L, Ji M, Wang S, Wen X, Huang P, Shen L, Xu J. Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study. BMC Med Inform Decis Mak 2022; 22:312. [PMID: 36447180 PMCID: PMC9707001 DOI: 10.1186/s12911-022-02066-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 11/23/2022] [Indexed: 12/05/2022] Open
Abstract
BACKGROUND Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. OBJECTIVE To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. METHODS 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. RESULTS Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08-8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67-7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28-5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14-9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. CONCLUSION Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.
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Affiliation(s)
- Lei Yuan
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.412632.00000 0004 1758 2270Department of Information Center, Wuhan University Renmin Hospital, Wuhan, Hubei China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
| | - Mengyao Ji
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Shuo Wang
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Xinyu Wen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Pingxiao Huang
- grid.33199.310000 0004 0368 7223Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei China
| | - Lei Shen
- grid.412632.00000 0004 1758 2270Department of Gastroenterology, Wuhan University Renmin Hospital, Wuhan, Hubei China
| | - Jun Xu
- grid.260478.f0000 0000 9249 2313School of Automation, Nanjing University of Information Science and Technology, Nanjing, China ,grid.260478.f0000 0000 9249 2313Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, NanJing, China
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22
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Jan Z, El Assadi F, Abd-alrazaq A, Jithesh PV. Artificial Intelligence for the Prediction and Early Diagnosis of Pancreatic Cancer: Scoping Review (Preprint).. [DOI: 10.2196/preprints.44248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
Abstract
BACKGROUND
Pancreatic cancer is the 12th most common cancer worldwide, with an overall survival rate of 4.9%. Early diagnosis of pancreatic cancer is essential for timely treatment and survival. Artificial intelligence (AI) provides advanced models and algorithms for better diagnosis of pancreatic cancer.
OBJECTIVE
This study aims to explore AI models used for the prediction and early diagnosis of pancreatic cancers as reported in the literature.
METHODS
A scoping review was conducted and reported in line with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. PubMed, Google Scholar, Science Direct, BioRXiv, and MedRxiv were explored to identify relevant articles. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively.
RESULTS
Of the 1185 publications, 30 studies were included in the scoping review. The included articles reported the use of AI for 6 different purposes. Of these included articles, AI techniques were mostly used for the diagnosis of pancreatic cancer (14/30, 47%). Radiological images (14/30, 47%) were the most frequently used data in the included articles. Most of the included articles used data sets with a size of <1000 samples (11/30, 37%). Deep learning models were the most prominent branch of AI used for pancreatic cancer diagnosis in the studies, and the convolutional neural network was the most used algorithm (18/30, 60%). Six validation approaches were used in the included studies, of which the most frequently used approaches were k-fold cross-validation (10/30, 33%) and external validation (10/30, 33%). A higher level of accuracy (99%) was found in studies that used support vector machine, decision trees, and k-means clustering algorithms.
CONCLUSIONS
This review presents an overview of studies based on AI models and algorithms used to predict and diagnose pancreatic cancer patients. AI is expected to play a vital role in advancing pancreatic cancer prediction and diagnosis. Further research is required to provide data that support clinical decisions in health care.
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23
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Hameed BS, Krishnan UM. Artificial Intelligence-Driven Diagnosis of Pancreatic Cancer. Cancers (Basel) 2022; 14:5382. [PMID: 36358800 PMCID: PMC9657087 DOI: 10.3390/cancers14215382] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 10/28/2022] [Accepted: 10/28/2022] [Indexed: 08/01/2023] Open
Abstract
Pancreatic cancer is among the most challenging forms of cancer to treat, owing to its late diagnosis and aggressive nature that reduces the survival rate drastically. Pancreatic cancer diagnosis has been primarily based on imaging, but the current state-of-the-art imaging provides a poor prognosis, thus limiting clinicians' treatment options. The advancement of a cancer diagnosis has been enhanced through the integration of artificial intelligence and imaging modalities to make better clinical decisions. In this review, we examine how AI models can improve the diagnosis of pancreatic cancer using different imaging modalities along with a discussion on the emerging trends in an AI-driven diagnosis, based on cytopathology and serological markers. Ethical concerns regarding the use of these tools have also been discussed.
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Affiliation(s)
- Bahrudeen Shahul Hameed
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
| | - Uma Maheswari Krishnan
- Centre for Nanotechnology & Advanced Biomaterials (CeNTAB), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Chemical & Biotechnology (SCBT), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
- School of Arts, Sciences, Humanities & Education (SASHE), Shanmugha Arts, Science, Technology and Research Academy, Deemed University, Thanjavur 613401, India
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24
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Barat M, Marchese U, Pellat A, Dohan A, Coriat R, Hoeffel C, Fishman EK, Cassinotto C, Chu L, Soyer P. Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances. Can Assoc Radiol J 2022; 74:351-361. [PMID: 36065572 DOI: 10.1177/08465371221124927] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Abstract
Pancreatic ductal carcinoma (PDAC) is one of the leading causes of cancer-related death worldwide. Computed tomography (CT) remains the primary imaging modality for diagnosis of PDAC. However, CT has limitations for early pancreatic tumor detection and tumor characterization so that it is currently challenged by magnetic resonance imaging. More recently, a particular attention has been given to radiomics for the characterization of pancreatic lesions using extraction and analysis of quantitative imaging features. In addition, radiomics has currently many applications that are developed in conjunction with artificial intelligence (AI) with the aim of better characterizing pancreatic lesions and providing a more precise assessment of tumor burden. This review article sums up recent advances in imaging of PDAC in the field of image/data acquisition, tumor detection, tumor characterization, treatment response evaluation, and preoperative planning. In addition, current applications of radiomics and AI in the field of PDAC are discussed.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
| | - Ugo Marchese
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Digestive, Hepatobiliary and Pancreatic Surgery, 26935Hopital Cochin, AP-HP, Paris, France
| | - Anna Pellat
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Gastroenterology, 26935Hopital Cochin, AP-HP, Paris, France
| | - Anthony Dohan
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
| | - Romain Coriat
- Université Paris Cité, Faculté de Médecine, 555089Paris, France.,Department of Gastroenterology, 26935Hopital Cochin, AP-HP, Paris, France
| | | | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, 1466Johns Hopkins University, Baltimore, MD, USA
| | - Christophe Cassinotto
- Department of Radiology, CHU Montpellier, 27037University of Montpellier, Saint-Éloi Hospital, Montpellier, France
| | - Linda Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, School of Medicine, 1466Johns Hopkins University, Baltimore, MD, USA
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hôpitaux de Paris543341, Paris, France.,Université Paris Cité, Faculté de Médecine, 555089Paris, France
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25
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Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, Díaz Beveridge R, Montalvá Orón E, Pérez Rojas J, Alberich-Bayarri A. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol 2022; 95:20220072. [PMID: 35687700 PMCID: PMC10996946 DOI: 10.1259/bjr.20220072] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 05/19/2022] [Accepted: 05/27/2022] [Indexed: 11/05/2022] Open
Abstract
Patients with pancreatic ductal adenocarcinoma (PDAC) are generally classified into four categories based on contrast-enhanced CT at diagnosis: resectable, borderline resectable, unresectable, and metastatic disease. In the initial grading and staging of PDAC, structured radiological templates are useful but limited, as there is a need to define the aggressiveness and microscopic disease stage of these tumours to ensure adequate treatment allocation. Quantitative imaging analysis allows radiomics and dynamic imaging features to provide information of clinical outcomes, and to construct clinical models based on radiomics signatures or imaging phenotypes. These quantitative features may be used as prognostic and predictive biomarkers in clinical decision-making, enabling personalised management of advanced PDAC. Deep learning and convolutional neural networks also provide high level bioinformatics tools that can help define features associated with a given aspect of PDAC biology and aggressiveness, paving the way to define outcomes based on these features. Thus, the prediction of tumour phenotype, treatment response and patient prognosis may be feasible by using such comprehensive and integrated radiomics models. Despite these promising results, quantitative imaging is not ready for clinical implementation in PDAC. Limitations include the instability of metrics and lack of external validation. Large properly annotated datasets, including relevant semantic features (demographics, blood markers, genomics), image harmonisation, robust radiomics analysis, clinically significant tasks as outputs, comparisons with gold-standards (such as TNM or pretreatment classifications) and fully independent validation cohorts, will be required for the development of trustworthy radiomics and artificial intelligence solutions to predict PDAC aggressiveness in a clinical setting.
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Affiliation(s)
- Luis Marti-Bonmati
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Department of Radiology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Leonor Cerdá-Alberich
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
| | | | | | - Eva Montalvá Orón
- Department of Surgery, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Judith Pérez Rojas
- Department of Pathology, Hospital Universitario y
Politécnico La Fe, Valencia,
Spain
| | - Angel Alberich-Bayarri
- GIBI230 Research Group on Biomedical Imaging, Instituto de
Investigación Sanitaria La Fe,
Valencia, Spain
- Quantitative Imaging Biomarkers in Medicine, Quibim
SL, Valencia,
Spain
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26
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Soyer P, Fishman EK, Rowe SP, Patlas MN, Chassagnon G. Does artificial intelligence surpass the radiologist? Diagn Interv Imaging 2022; 103:445-447. [PMID: 35973913 DOI: 10.1016/j.diii.2022.08.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 08/02/2022] [Indexed: 12/30/2022]
Affiliation(s)
- Philippe Soyer
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France.
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Steven P Rowe
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Michael N Patlas
- Department of Radiology, Hamilton General Hospital, McMaster University Hamilton, ON, Canada L8L 2X2
| | - Guillaume Chassagnon
- Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014 Paris, France; Université Paris Cité, Faculté de Médecine, 75006, Paris, France
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27
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Anta JA, Martínez-Ballestero I, Eiroa D, García J, Rodríguez-Comas J. Artificial intelligence for the detection of pancreatic lesions. Int J Comput Assist Radiol Surg 2022; 17:1855-1865. [PMID: 35951286 DOI: 10.1007/s11548-022-02706-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 06/17/2022] [Indexed: 11/30/2022]
Abstract
PURPOSE Pancreatic cancer is one of the most lethal neoplasms among common cancers worldwide, and PCLs are well-known precursors of this type of cancer. Artificial intelligence (AI) could help to improve and speed up the detection and classification of pancreatic lesions. The aim of this review is to summarize the articles addressing the diagnostic yield of artificial intelligence applied to medical imaging (computed tomography [CT] and/or magnetic resonance [MR]) for the detection of pancreatic cancer and pancreatic cystic lesions. METHODS We performed a comprehensive literature search using PubMed, EMBASE, and Scopus (from January 2010 to April 2021) to identify full articles evaluating the diagnostic accuracy of AI-based methods processing CT or MR images to detect pancreatic ductal adenocarcinoma (PDAC) or pancreatic cystic lesions (PCLs). RESULTS We found 20 studies meeting our inclusion criteria. Most of the AI-based systems used were convolutional neural networks. Ten studies addressed the use of AI to detect PDAC, eight studies aimed to detect and classify PCLs, and 4 aimed to predict the presence of high-grade dysplasia or cancer. CONCLUSION AI techniques have shown to be a promising tool which is expected to be helpful for most radiologists' tasks. However, methodologic concerns must be addressed, and prospective clinical studies should be carried out before implementation in clinical practice.
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Affiliation(s)
- Julia Arribas Anta
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Gastroenterology, University Hospital, 12 Octubre. Av. de Córdoba, s/n, 28041, Madrid, Spain
| | - Iván Martínez-Ballestero
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Daniel Eiroa
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.,Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d'Hebrón, Passeig de la Vall d'Hebron, 119-129, 08035, Barcelona, Spain
| | - Javier García
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain
| | - Júlia Rodríguez-Comas
- Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain.
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28
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Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, Yang L, Li Z. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022; 12:973999. [PMID: 35982967 PMCID: PMC9380440 DOI: 10.3389/fonc.2022.973999] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 07/13/2022] [Indexed: 01/03/2023] Open
Abstract
Purpose We evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021. Methods Publications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis. Results A total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", “survival”, “classification”, and “management” are the research hotspots in this field. Conclusion The research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.
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Affiliation(s)
- Hua Yin
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
| | - Feixiong Zhang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiaoli Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Xiangkun Meng
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | - Yu Miao
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
| | | | - Li Yang
- Department of Gastroenterology, General Hospital of Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
| | - Zhaoshen Li
- Postgraduate Training Base in Shanghai Gongli Hospital, Ningxia Medical University, Shanghai, China
- Clinical Medical College, Ningxia Medical University, Yinchuan, China
- *Correspondence: Zhaoshen Li, ; Li Yang,
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de la Pinta C. Radiomics in pancreatic cancer for oncologist: Present and future. Hepatobiliary Pancreat Dis Int 2022; 21:356-361. [PMID: 34961674 DOI: 10.1016/j.hbpd.2021.12.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/07/2021] [Indexed: 02/05/2023]
Abstract
Radiomics is changing the world of medicine and more specifically the world of oncology. Early diagnosis and treatment improve the prognosis of patients with cancer. After treatment, the evaluation of the response will determine future treatments. In oncology, every change in treatment means a loss of therapeutic options and this is key in pancreatic cancer. Radiomics has been developed in oncology in the early diagnosis and differential diagnosis of benign and malignant lesions, in the evaluation of response, in the prediction of possible side effects, marking the risk of recurrence, survival and prognosis of the disease. Some studies have validated its use to differentiate normal tissues from tumor tissues with high sensitivity and specificity, and to differentiate cystic lesions and pancreatic neuroendocrine tumor grades with texture parameters. In addition, these parameters have been related to survival in patients with pancreatic cancer and to response to radiotherapy and chemotherapy. This review aimed to establish the current status of the use of radiomics in pancreatic cancer and future perspectives.
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Affiliation(s)
- Carolina de la Pinta
- Radiation Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain.
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30
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Three-dimensional conditional generative adversarial network-based virtual thin-slice technique for the morphological evaluation of the spine. Sci Rep 2022; 12:12176. [PMID: 35842451 PMCID: PMC9288435 DOI: 10.1038/s41598-022-16637-x] [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: 01/11/2022] [Accepted: 07/13/2022] [Indexed: 11/08/2022] Open
Abstract
Virtual thin-slice (VTS) technique is a generative adversarial network-based algorithm that can generate virtual 1-mm-thick CT images from images of 3–10-mm thickness. We evaluated the performance of VTS technique for assessment of the spine. VTS was applied to 4-mm-thick CT images of 73 patients, and the visibility of intervertebral spaces was evaluated on the 4-mm-thick and VTS images. The heights of vertebrae measured on sagittal images reconstructed from the 4-mm-thick images and VTS images were compared with those measured on images reconstructed from 1-mm-thick images. Diagnostic performance for the detection of compression fractures was also compared. The intervertebral spaces were significantly more visible on the VTS images than on the 4-mm-thick images (P < 0.001). The absolute value of the measured difference in mean vertebral height between the VTS and 1-mm-thick images was smaller than that between the 4-mm-thick and 1-mm-thick images (P < 0.01–0.54). The diagnostic performance of the VTS images for detecting compression fracture was significantly lower than that of the 4-mm-thick images for one reader (P = 0.02). VTS technique enabled the identification of each vertebral body, and enabled accurate measurement of vertebral height. However, this technique is not suitable for diagnosing compression fractures.
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Kui B, Pintér J, Molontay R, Nagy M, Farkas N, Gede N, Vincze Á, Bajor J, Gódi S, Czimmer J, Szabó I, Illés A, Sarlós P, Hágendorn R, Pár G, Papp M, Vitális Z, Kovács G, Fehér E, Földi I, Izbéki F, Gajdán L, Fejes R, Németh BC, Török I, Farkas H, Mickevicius A, Sallinen V, Galeev S, Ramírez-Maldonado E, Párniczky A, Erőss B, Hegyi PJ, Márta K, Váncsa S, Sutton R, Szatmary P, Latawiec D, Halloran C, de-Madaria E, Pando E, Alberti P, Gómez-Jurado MJ, Tantau A, Szentesi A, Hegyi P. EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis. Clin Transl Med 2022; 12:e842. [PMID: 35653504 PMCID: PMC9162438 DOI: 10.1002/ctm2.842] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/06/2022] [Accepted: 04/11/2022] [Indexed: 12/17/2022] Open
Abstract
Background Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the pancreas. Early identification of patients at high risk for developing a severe course of the disease is crucial for preventing organ failure and death. Most of the former predictive scores require many parameters or at least 24 h to predict the severity; therefore, the early therapeutic window is often missed. Methods The early achievable severity index (EASY) is a multicentre, multinational, prospective and observational study (ISRCTN10525246). The predictions were made using machine learning models. We used the scikit‐learn, xgboost and catboost Python packages for modelling. We evaluated our models using fourfold cross‐validation, and the receiver operating characteristic (ROC) curve, the area under the ROC curve (AUC), and accuracy metrics were calculated on the union of the test sets of the cross‐validation. The most critical factors and their contribution to the prediction were identified using a modern tool of explainable artificial intelligence called SHapley Additive exPlanations (SHAP). Results The prediction model was based on an international cohort of 1184 patients and a validation cohort of 3543 patients. The best performing model was an XGBoost classifier with an average AUC score of 0.81 ± 0.033 and an accuracy of 89.1%, and the model improved with experience. The six most influential features were the respiratory rate, body temperature, abdominal muscular reflex, gender, age and glucose level. Using the XGBoost machine learning algorithm for prediction, the SHAP values for the explanation and the bootstrapping method to estimate confidence, we developed a free and easy‐to‐use web application in the Streamlit Python‐based framework (http://easy‐app.org/). Conclusions The EASY prediction score is a practical tool for identifying patients at high risk for severe AP within hours of hospital admission. The web application is available for clinicians and contributes to the improvement of the model.
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Affiliation(s)
- Balázs Kui
- Department of Medicine, University of Szeged, Szeged, Hungary.,Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - József Pintér
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Roland Molontay
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary.,MTA-BME Stochastics Research Group, Budapest, Hungary
| | - Marcell Nagy
- Department of Stochastics, Institute of Mathematics, Budapest University of Technology and Economics, Budapest, Hungary
| | - Nelli Farkas
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Institute of Bioanalysis, Medical School, University of Pécs, Pécs, Hungary
| | - Noémi Gede
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary
| | - Áron Vincze
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Judit Bajor
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Szilárd Gódi
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - József Czimmer
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Imre Szabó
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Anita Illés
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Patrícia Sarlós
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Roland Hágendorn
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Gabriella Pár
- Division of Gastroenterology, First Department of Medicine, University of Pécs, Medical School, Pécs, Hungary
| | - Mária Papp
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Zsuzsanna Vitális
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - György Kovács
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Eszter Fehér
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Ildikó Földi
- Department of Gastroenterology, Institute of Internal Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Hungary
| | - Ferenc Izbéki
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - László Gajdán
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - Roland Fejes
- Szent György Teaching Hospital of County Fejér, Székesfehérvár, Hungary
| | - Balázs Csaba Németh
- Department of Medicine, University of Szeged, Szeged, Hungary.,Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary
| | - Imola Török
- County Emergency Clinical Hospital of Târgu Mures-Gastroenterology Clinic and University of Medicine, Pharmacy, Sciences and Technology 'George Emil Palade', Targu Mures, Romania
| | - Hunor Farkas
- County Emergency Clinical Hospital of Târgu Mures-Gastroenterology Clinic and University of Medicine, Pharmacy, Sciences and Technology 'George Emil Palade', Targu Mures, Romania
| | | | - Ville Sallinen
- Department of Transplantation and Liver Surgery, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Shamil Galeev
- Saint Luke Clinical Hospital, St. Petersburg, Russia
| | | | - Andrea Párniczky
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Heim Pál National Pediatric Institute, Budapest, Hungary
| | - Bálint Erőss
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Péter Jenő Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary
| | - Katalin Márta
- Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Szilárd Váncsa
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Robert Sutton
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Peter Szatmary
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Diane Latawiec
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Chris Halloran
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool and Liverpool University Hospitals NHS Foundation Trust, Liverpool, England, UK
| | - Enrique de-Madaria
- Gastroenterology Department, Alicante University General Hospital, ISABIAL, Alicante, Spain
| | - Elizabeth Pando
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Piero Alberti
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Maria José Gómez-Jurado
- Department of Hepato-Pancreato-Biliary and Transplant Surgery, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Alina Tantau
- The 4th Medical Clinic, Iuliu Hatieganu' University of Medicine and Pharmacy, Cluj-Napoca, Romania.,Gastroenterology and Hepatology Medical Center, Cluj-Napoca, Romania
| | - Andrea Szentesi
- Centre for Translational Medicine, Department of Medicine, University of Szeged, Szeged, Hungary.,Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary
| | - Péter Hegyi
- Institute for Translational Medicine, Szentágothai Research Centre, Medical School, University of Pécs, Pécs, Hungary.,Division of Pancreatic Diseases, Heart and Vascular Centre, Semmelweis University, Budapest, Hungary.,Centre for Translational Medicine, Semmelweis University, Budapest, Hungary
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Lin KW, Ang TL, Li JW. Role of artificial intelligence in early detection and screening for pancreatic adenocarcinoma. Artif Intell Med Imaging 2022; 3:21-32. [DOI: 10.35711/aimi.v3.i2.21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/12/2022] [Accepted: 03/17/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic adenocarcinoma remains to be one of the deadliest malignancies in the world despite treatment advancement over the past few decades. Its low survival rates and poor prognosis can be attributed to ambiguity in recommendations for screening and late symptom onset, contributing to its late presentation. In the recent years, artificial intelligence (AI) as emerged as a field to aid in the process of clinical decision making. Considerable efforts have been made in the realm of AI to screen for and predict future development of pancreatic ductal adenocarcinoma. This review discusses the use of AI in early detection and screening for pancreatic adenocarcinoma, and factors which may limit its use in a clinical setting.
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Affiliation(s)
- Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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Barat M, Cottereau AS, Gaujoux S, Tenenbaum F, Sibony M, Bertherat J, Libé R, Gaillard M, Jouinot A, Assié G, Hoeffel C, Soyer P, Dohan A. Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art. Cancers (Basel) 2022; 14:cancers14030569. [PMID: 35158836 PMCID: PMC8833697 DOI: 10.3390/cancers14030569] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 01/14/2022] [Accepted: 01/18/2022] [Indexed: 12/30/2022] Open
Abstract
Simple Summary Non-invasive characterization of adrenal lesions requires a rigorous approach. Although CT is the cornerstone of adrenal lesion characterization, a multimodality multiparametric imaging approach helps improve confidence in adrenal lesion characterization. Abstract Detection and characterization of adrenal lesions have evolved during the past two decades. Although the role of imaging in adrenal lesions associated with hormonal secretion is usually straightforward, characterization of non-functioning adrenal lesions may be challenging to confidently identify those that need to be resected. Although many adrenal lesions can be readily diagnosed when they display typical imaging features, the diagnosis may be challenging for atypical lesions. Computed tomography (CT) remains the cornerstone of adrenal imaging, but other morphological or functional modalities can be used in combination to reach a diagnosis and avoid useless biopsy or surgery. Early- and delayed-phase contrast-enhanced CT images are essential for diagnosing lipid-poor adenoma. Ongoing studies are evaluating the capabilities of dual-energy CT to provide valid virtual non-contrast attenuation and iodine density measurements from contrast-enhanced examinations. Adrenal lesions with attenuation values between 10 and 30 Hounsfield units (HU) on unenhanced CT can be characterized by MRI when iodinated contrast material injection cannot be performed. 18F-FDG PET/CT helps differentiate between atypical benign and malignant adrenal lesions, with the adrenal-to-liver maximum standardized uptake value ratio being the most discriminative variable. Recent studies evaluating the capabilities of radiomics and artificial intelligence have shown encouraging results.
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Affiliation(s)
- Maxime Barat
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
| | - Anne-Ségolène Cottereau
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Nuclear Medicine, Cochin Hospital, AP-HP, 75014 Paris, France;
| | - Sébastien Gaujoux
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Pancreatic and Endocrine Surgery, Pitié-Salpetrière Hospital, AP-HP, 75013 Paris, France
| | - Florence Tenenbaum
- Department of Nuclear Medicine, Cochin Hospital, AP-HP, 75014 Paris, France;
| | - Mathilde Sibony
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Pathology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Jérôme Bertherat
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Rossella Libé
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Martin Gaillard
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Digestive, Hepatobiliary and Endocrine Surgery, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Anne Jouinot
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | - Guillaume Assié
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Department of Endocrinology, Cochin Hospital, AP-HP, 75014 Paris, France
| | | | - Philippe Soyer
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
| | - Anthony Dohan
- Department of Radiology, Cochin Teaching Hospital, AP-HP, Université de Paris, 75014 Paris, France; (M.B.); (P.S.)
- Faculté de Médecine, Université de Paris, 75006 Paris, France; (A.-S.C.); (S.G.); (M.S.); (J.B.); (R.L.); (M.G.); (A.J.); (G.A.)
- Correspondence:
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Tasu JP, Guen RL, Rhouma IB, Guerrab A, Beydoun N, Bergougnoux B, Ingrand P, Herpe G. Accuracy of a CT density threshold enhancement in distinguishing pancreas parenchymal necrosis in cases of acute pancreatitis in the first week. Diagn Interv Imaging 2022; 103:266-272. [PMID: 34991994 DOI: 10.1016/j.diii.2021.12.003] [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: 09/21/2021] [Revised: 12/10/2021] [Accepted: 12/14/2021] [Indexed: 11/03/2022]
Abstract
PURPOSE The purpose of this study was to identify attenuation threshold value on computed tomography (CT) that allowed discriminating between interstitial edematous pancreatitis (IEP) and necrotizing pancreatitis (NP) in patients with acute pancreatitis during the first week of the disease and evaluate interobserver reproducibility for the diagnosis of acute pancreatitis category. MATERIALS AND METHODS Patients with acute pancreatitis who underwent CT examination of the abdomen between March 2015 and December 2019 were retrospectively included. Actual diagnosis of IEP or NP was based on final clinical report, follow-up evaluation, and complications. Six regions of interest were manually placed in the pancreatic gland and peripancreatic fat, and differences in CT attenuation values before contrast injection and during the portal venous phase of enhancement were computed. Performance in the diagnosis of AP category was evaluated using receiver operating characteristic analysis. Interobserver agreement was estimated by the intraclass correlation coefficient (ICC) and Bland Altman analysis was used to estimate reproducibility between pairs of observers. RESULTS Sixty-six patients with NP (46 men, 20 women; mean age, 55 ± 17 [SD] years; age range: 20-89 years) and 70 patients with IEP (39 men, 31 women; mean age, 54 ± 18 [SD] years; age range: 21-87 years) were included. An enhancement value less than 30 Hounsfield units (HU) in the pancreatic gland during the portal phase compared to non-contrast phase, yielded 90.9% sensitivity (60/66; 95% CI: 81.3-96.6), 94.3% specificity (66/70; 95% CI: 86.0-98.4) and an area under curve of 0.958 (95% CI: 0.919-0.996) for the diagnosis of NP versus IEP. Interobserver reproducibility for pancreas enhancement was good using Bland Altman plot and ICC was excellent for pancreatic gland analysis (ICC 0.978; 95% CI: 0.961-0.988) but poor or moderate (ICC ≤0.634) regarding peripancreatic fat necrosis. CONCLUSION By using a pancreas enhancement threshold value of 30 HU, CT is accurate and reproducible for the diagnosis of NP during the first week of the disease.
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Affiliation(s)
- Jean Pierre Tasu
- Department of Diagnostic and Interventional Radiology, University Hospital of Poitiers, 86021 Poitiers, France; LaTim, UBO and INSERM 1101, University of Brest, 29000 Brest, France.
| | - Raphael Le Guen
- Department of Diagnostic and Interventional Radiology, University Hospital of Poitiers, 86021 Poitiers, France
| | - Inès Ben Rhouma
- Department of Diagnostic and Interventional Radiology, University Hospital of Poitiers, 86021 Poitiers, France
| | - Ayoub Guerrab
- Department of Diagnostic and Interventional Radiology, University Hospital of Poitiers, 86021 Poitiers, France
| | - Nadeem Beydoun
- Department of Diagnostic and Interventional Radiology, University Hospital of Poitiers, 86021 Poitiers, France
| | - Brice Bergougnoux
- Department of Diagnostic and Interventional Radiology, University Hospital of Poitiers, 86021 Poitiers, France
| | - Pierre Ingrand
- CIC 1402, Clinical Investigation center, Bio-statistic and epidemiology, University of Poitiers, 86021 Poitiers, France
| | - Guillaume Herpe
- Department of Diagnostic and Interventional Radiology, University Hospital of Poitiers, 86021 Poitiers, France
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Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021; 127:39-56. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 10/14/2021] [Indexed: 12/11/2022]
Abstract
Breast magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis and is widely used clinically. Dynamic contrast-enhanced MRI is the basis for breast MRI, but ultrafast images, T2-weighted images, and diffusion-weighted images are also taken to improve the characteristics of the lesion. Such multiparametric MRI with numerous morphological and functional data poses new challenges to radiologists, and thus, new tools for reliable, reproducible, and high-volume quantitative assessments are warranted. In this context, radiomics, which is an emerging field of research involving the conversion of digital medical images into mineable data for clinical decision-making and outcome prediction, has been gaining ground in oncology. Recent development in artificial intelligence has promoted radiomics studies in various fields including breast cancer treatment and numerous studies have been conducted. However, radiomics has shown a translational gap in clinical practice, and many issues remain to be solved. In this review, we will outline the steps of radiomics workflow and investigate clinical application of radiomics focusing on breast MRI based on published literature, as well as current discussion about limitations and challenges in radiomics.
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Affiliation(s)
- Hiroko Satake
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
| | - Satoko Ishigaki
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
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