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Walston SL, Seki H, Takita H, Mitsuyama Y, Sato S, Hagiwara A, Ito R, Hanaoka S, Miki Y, Ueda D. Data set terminology of deep learning in medicine: a historical review and recommendation. Jpn J Radiol 2024; 42:1100-1109. [PMID: 38856878 DOI: 10.1007/s11604-024-01608-1] [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: 02/29/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
Medicine and deep learning-based artificial intelligence (AI) engineering represent two distinct fields each with decades of published history. The current rapid convergence of deep learning and medicine has led to significant advancements, yet it has also introduced ambiguity regarding data set terms common to both fields, potentially leading to miscommunication and methodological discrepancies. This narrative review aims to give historical context for these terms, accentuate the importance of clarity when these terms are used in medical deep learning contexts, and offer solutions to mitigate misunderstandings by readers from either field. Through an examination of historical documents, including articles, writing guidelines, and textbooks, this review traces the divergent evolution of terms for data sets and their impact. Initially, the discordant interpretations of the word 'validation' in medical and AI contexts are explored. We then show that in the medical field as well, terms traditionally used in the deep learning domain are becoming more common, with the data for creating models referred to as the 'training set', the data for tuning of parameters referred to as the 'validation (or tuning) set', and the data for the evaluation of models as the 'test set'. Additionally, the test sets used for model evaluation are classified into internal (random splitting, cross-validation, and leave-one-out) sets and external (temporal and geographic) sets. This review then identifies often misunderstood terms and proposes pragmatic solutions to mitigate terminological confusion in the field of deep learning in medicine. We support the accurate and standardized description of these data sets and the explicit definition of data set splitting terminologies in each publication. These are crucial methods for demonstrating the robustness and generalizability of deep learning applications in medicine. This review aspires to enhance the precision of communication, thereby fostering more effective and transparent research methodologies in this interdisciplinary field.
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Affiliation(s)
- Shannon L Walston
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Hiroshi Seki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Hirotaka Takita
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Yasuhito Mitsuyama
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Shingo Sato
- Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, PA, USA
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University, Nagoya, Japan
| | - Shouhei Hanaoka
- Department of Radiology, University of Tokyo Hospital, Tokyo, Japan
| | - Yukio Miki
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan
| | - Daiju Ueda
- Department of Diagnostic and Interventional Radiology, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Osaka, Japan.
- Center for Health Science Innovation, Osaka Metropolitan University, Osaka, Japan.
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Tsuboyama T, Yanagawa M, Fujioka T, Fujita S, Ueda D, Ito R, Yamada A, Fushimi Y, Tatsugami F, Nakaura T, Nozaki T, Kamagata K, Matsui Y, Hirata K, Fujima N, Kawamura M, Naganawa S. Recent trends in AI applications for pelvic MRI: a comprehensive review. LA RADIOLOGIA MEDICA 2024; 129:1275-1287. [PMID: 39096356 DOI: 10.1007/s11547-024-01861-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Accepted: 07/25/2024] [Indexed: 08/05/2024]
Abstract
Magnetic resonance imaging (MRI) is an essential tool for evaluating pelvic disorders affecting the prostate, bladder, uterus, ovaries, and/or rectum. Since the diagnostic pathway of pelvic MRI can involve various complex procedures depending on the affected organ, the Reporting and Data System (RADS) is used to standardize image acquisition and interpretation. Artificial intelligence (AI), which encompasses machine learning and deep learning algorithms, has been integrated into both pelvic MRI and the RADS, particularly for prostate MRI. This review outlines recent developments in the use of AI in various stages of the pelvic MRI diagnostic pathway, including image acquisition, image reconstruction, organ and lesion segmentation, lesion detection and classification, and risk stratification, with special emphasis on recent trends in multi-center studies, which can help to improve the generalizability of AI.
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Affiliation(s)
- Takahiro Tsuboyama
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, Kobe-City, Hyogo, 650-0017, Japan.
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita-City, Osaka, 565-0871, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, 1-5-45 Yushima, Bunkyo-ku, Tokyo, 113-8519, 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
| | - Daiju Ueda
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
| | - Akira Yamada
- Medical Data Science Course, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano, 390-8621, Japan
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Shogoin Kawaharacho, Sakyoku, Kyoto, 606-8507, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8551, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, 1-1-1 Honjo Chuo-ku, Kumamoto, 860-8556, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku-ku, Tokyo, 160-0016, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo, 113-8421, 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
| | - Kenji Hirata
- Department of Diagnostic Imaging, Graduate School of Medicine, Hokkaido University, Kita 15 Nishi 7, Kita-ku, Sapporo, Hokkaido, 060-8648, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, N15, W5, Kita-ku, Sapporo, 060-8638, Japan
| | - Mariko Kawamura
- 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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Wang Y, Han Q, Wen B, Yang B, Zhang C, Song Y, Zhang L, Xian J. Development and validation of a prediction model for malignant sinonasal tumors based on MR radiomics and machine learning. Eur Radiol 2024:10.1007/s00330-024-11033-7. [PMID: 39210161 DOI: 10.1007/s00330-024-11033-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/23/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024]
Abstract
OBJECTIVES This study aimed to utilize MR radiomics-based machine learning classifiers on a large-sample, multicenter dataset to develop an optimal model for predicting malignant sinonasal tumors and tumor-like lesions. METHODS This study included 1711 adult patients (875 benign and 836 malignant) with sinonasal tumors or tumor-like lesions from three institutions. Patients from institution 1 (n = 1367) constituted both the training and validation cohorts, while those from institution 2 and 3 (n = 158/186) made up the test cohorts. Manual segmentation of the region of interest of the tumor was performed on T1WI, T2WI, and contrast-enhanced T1WI (CE-T1WI). Data normalization, dimensional reductions, feature selection, and classifications were performed using ten machine-learning classifiers. Four fusion models, namely T1WI + T2WI, T1WI + CE-T1WI, T2WI + CE-T1WI, and T1WI + T2WI + CE-T1WI, were constructed using the top ten features with the highest contribution in feature selection in the optimal models of T1WI, T2WI, and CE-T1WI. The Delong test compared areas under the curve (AUC) between models. RESULTS The AUCs of training/validation/test1/test2 datasets for T1WI, T2WI, and CE-T1WI were 0.900/0.842/0.872/0.839, 0.876/0.789/0.842/0.863, and 0.899/0.824/0.831/0.707, respectively. The fusion model from T1WI + T2WI + CE-T1WI had the highest AUC. The AUCs of training/validation/test1/test2 datasets were 0.947/0.849/0.871/0.887. The T1WI + T2WI + CE-T1WI model demonstrated a significantly higher AUC than the T2WI + CE-T1WI model in both cohorts (p < 0.05) and outperformed the T2WI model in test 1 (p = 0.008) and the T1WI model in test 2 (p = 0.006). CONCLUSIONS This fusion model based on radiomics from T1WI + T2WI + CE-T1WI images and machine learning can improve the power in predicting malignant sinonasal tumors with high accuracy, resilience, and robustness. CLINICAL RELEVANCE STATEMENT Our study proposes a radiomics-based machine learning fusion model from T1- and T2-weighted images and contrast-enhanced T1-weighted images, which can non-invasively identify the nature of sinonasal tumors and improve the performance in predicting malignant sinonasal tumors. KEY POINTS Differentiating benign and malignant sinonasal tumors is difficult due to similar clinical presentations. A radiomics model from T1 + T2 + contrast-enhanced T1 images can identify the nature of sinonasal tumors. This model can help distinguish benign and malignant sinonasal tumors.
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Affiliation(s)
- Yuchen Wang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Qinghe Han
- Department of Radiology, The Second Hospital of Jilin University, Changchun, China
| | - Baohong Wen
- Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bingbing Yang
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Chen Zhang
- MR Research Collaboration Team, Siemens Healthcare, Beijing, China
| | - Yang Song
- MR Research Collaboration Team, Siemens Healthcare, Beijing, China
| | - Luo Zhang
- Department of Otolaryngology-Head and Neck Surgery, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
- Beijing Laboratory of Allergic Diseases and Beijing Key Laboratory of Nasal Diseases, Beijing Institute of Otorhinolaryngology, Beijing, China.
- Research Unit of Diagnosis and Treatment of Chronic Nasal Diseases, Chinese Academy of Medical Sciences, Beijing, China.
- Department of Allergy, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China.
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Sollmann N, Fuderer M, Crameri F, Weingärtner S, Baeßler B, Gulani V, Keenan KE, Mandija S, Golay X, deSouza NM. Color Maps: Facilitating the Clinical Impact of Quantitative MRI. J Magn Reson Imaging 2024. [PMID: 39180202 DOI: 10.1002/jmri.29573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 08/05/2024] [Accepted: 08/05/2024] [Indexed: 08/26/2024] Open
Abstract
Presenting quantitative data using non-standardized color maps potentially results in unrecognized misinterpretation of data. Clinically meaningful color maps should intuitively and inclusively represent data without misleading interpretation. Uniformity of the color gradient for color maps is critically important. Maximal color and lightness contrast, readability for color vision-impaired individuals, and recognizability of the color scheme are highly desirable features. This article describes the use of color maps in five key quantitative MRI techniques: relaxometry, diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE)-MRI, MR elastography (MRE), and water-fat MRI. Current display practice of color maps is reviewed and shortcomings against desirable features are highlighted. EVIDENCE LEVEL: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Miha Fuderer
- Radiotherapy, Division Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Sebastian Weingärtner
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Bettina Baeßler
- Department of Diagnostic and Interventional Radiology, University Hospital Wuerzburg, Wuerzburg, Germany
| | - Vikas Gulani
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Kathryn E Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, Boulder, Colorado, USA
| | - Stefano Mandija
- Radiotherapy, Division Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Xavier Golay
- Queen Square Institute of Neurology, University College London, London, UK
- Gold Standard Phantoms, Sheffield, UK
- Bioxydyn, Manchester, UK
| | - Nandita M deSouza
- The Institute of Cancer Research, London, UK
- The Royal Marsden NHS Foundation Trust, London, UK
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5
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Fushimi Y, Nakajima S, Sakata A, Okuchi S, Otani S, Nakamoto Y. Value of Quantitative Susceptibility Mapping in Clinical Neuroradiology. J Magn Reson Imaging 2024; 59:1914-1929. [PMID: 37681441 DOI: 10.1002/jmri.29010] [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: 05/31/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023] Open
Abstract
Quantitative susceptibility mapping (QSM) is a unique technique for providing quantitative information on tissue magnetic susceptibility using phase image data. QSM can provide valuable information regarding physiological and pathological processes such as iron deposition, hemorrhage, calcification, and myelin. QSM has been considered for use as an imaging biomarker to investigate physiological status and pathological changes. Although various studies have investigated the clinical applications of QSM, particularly regarding the use of QSM in clinical practice, have not been examined well. This review provides on an overview of the basics of QSM and its clinical applications in neuroradiology. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Satoshi Nakajima
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akihiko Sakata
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Sachi Okuchi
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Sayo Otani
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yuji Nakamoto
- Department of Diagnostic Imaging and Nuclear Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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6
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Shen L, Li Y, Huang H, Lu Z, Chen B. HER2 in Gastric Cancer: A Comprehensive Analysis Combining Meta-Analysis and DCE-MRI Radiomics. Cancer Control 2024; 31:10732748241293699. [PMID: 39448273 DOI: 10.1177/10732748241293699] [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] [Indexed: 10/26/2024] Open
Abstract
OBJECTIVE Advanced gastric cancer (AGC) is a severe malignant tumor, and overexpression of HER2/ERBB2 may play a crucial role in its development. The purpose of this study is to investigate the overexpression of HER2/ERBB2 in gastric cancer through a meta-analysis and examine its relationship with perfusion parameters using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) technology. METHODS We conducted an extensive literature search and collected relevant studies for the meta-analysis. We used a random-effects model and assigned weights using the "inverse variance" method. Additionally, we included 95 AGC patients diagnosed pathologically between April 2018 and October 2021. They all underwent DCE-MRI scans, and the data were subsequently analyzed using the Omni kinetic software. HER2 expression was assessed using immunohistochemistry. RESULTS The meta-analysis revealed an overall odds ratio (OR) of .21 for HER2/ERBB2 overexpression in gastric cancer, with a 95% confidence interval of [.14, .30]. DCE-MRI results showed a significant association between high HER2 expression and poor tumor differentiation (P < .005). The extracellular volume fraction (Ecv) quantile, mean, relative deviation, median intensity, and difference entropy were significantly higher in the low HER2 expression group compared to the high HER2 expression group. Receiver operating characteristic (ROC) curve analysis demonstrated that the area under the curve (AUC) values of DCE-MRI radiomic parameters with significant differences were close to .7. CONCLUSION Overexpression of HER2/ERBB2 in gastric cancer is significantly associated with certain radiomic parameters of DCE-MRI, providing a valuable diagnostic tool for clinical practice. Furthermore, the meta-analysis further confirmed the critical role of HER2/ERBB2 in the development of gastric cancer.
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Affiliation(s)
- Liyijing Shen
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, P. R. China
| | - Yaoqing Li
- Department of Gastrointestinal Surgery, Shaoxing People's Hospital, Shaoxing, P. R. China
| | - Huizhen Huang
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, P. R. China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, P. R. China
| | - Bo Chen
- Department of Radiology, Shaoxing People's Hospital, Shaoxing, P. R. China
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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: 7] [Impact Index Per Article: 7.0] [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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Huang H, Li Z, Xia Y, Zhao Z, Wang D, Jin H, Liu F, Yang Y, Shen L, Lu Z. Association between radiomics features of DCE-MRI and CD8 + and CD4 + TILs in advanced gastric cancer. Pathol Oncol Res 2023; 29:1611001. [PMID: 37342362 PMCID: PMC10277864 DOI: 10.3389/pore.2023.1611001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 05/24/2023] [Indexed: 06/22/2023]
Abstract
Objective: The aim of this investigation was to explore the correlation between the levels of tumor-infiltrating CD8+ and CD4+ T cells and the quantitative pharmacokinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in patients with advanced gastric cancer. Methods: We retrospectively analyzed the data of 103 patients with histopathologically confirmed advanced gastric cancer (AGC). Three pharmacokinetic parameters, Kep, Ktrans, and Ve, and their radiomics characteristics were obtained by Omni Kinetics software. Immunohistochemical staining was used to determine CD4+ and CD8+ TILs. Statistical analysis was subsequently performed to assess the correlation between radiomics characteristics and CD4+ and CD8+ TIL density. Results: All patients included in this study were finally divided into either a CD8+ TILs low-density group (n = 51) (CD8+ TILs < 138) or a high-density group (n = 52) (CD8+ TILs ≥ 138), and a CD4+ TILs low-density group (n = 51) (CD4+ TILs < 87) or a high-density group (n = 52) (CD4+ TILs ≥ 87). ClusterShade and Skewness based on Kep and Skewness based on Ktrans both showed moderate negative correlation with CD8+ TIL levels (r = 0.630-0.349, p < 0.001), with ClusterShade based on Kep having the highest negative correlation (r = -0.630, p < 0.001). Inertia-based Kep showed a moderate positive correlation with the CD4+ TIL level (r = 0.549, p < 0.001), and the Correlation based on Kep showed a moderate negative correlation with the CD4+ TIL level, which also had the highest correlation coefficient (r = -0.616, p < 0.001). The diagnostic efficacy of the above features was assessed by ROC curves. For CD8+ TILs, ClusterShade of Kep had the highest mean area under the curve (AUC) (0.863). For CD4+ TILs, the Correlation of Kep had the highest mean AUC (0.856). Conclusion: The radiomics features of DCE-MRI are associated with the expression of tumor-infiltrating CD8+ and CD4+ T cells in AGC, which have the potential to noninvasively evaluate the expression of CD8+ and CD4+ TILs in AGC patients.
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Affiliation(s)
- Huizhen Huang
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhiheng Li
- Department of Radiology, Anhui Provincial Hospital, Hefei, China
| | - Yue Xia
- Shaoxing of Medicine, Shaoxing University, Shaoxing, China
| | - Zhenhua Zhao
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Dandan Wang
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Hongyan Jin
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Fang Liu
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Ye Yang
- Country Department of Pathology, Shaoxing People’s Hospital, Shaoxing, China
| | - Liyijing Shen
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
| | - Zengxin Lu
- Department of Radiology, Shaoxing People’s Hospital, Shaoxing, China
- The First Affiliated Hospital of Shaoxing University, Shaoxing, China
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9
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Agostini A, Borgheresi A, Mariotti F, Ottaviani L, Carotti M, Valenti M, Giovagnoni A. New frontiers in oncological imaging with Computed Tomography: from morphology to function. Semin Ultrasound CT MR 2023; 44:214-227. [PMID: 37245886 DOI: 10.1053/j.sult.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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10
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Grazzini I, Venezia D, Roscio DD, Chiarotti I, Mazzei MA, Cerase A. Morphological and functional neuroradiology of brain metastases. Semin Ultrasound CT MR 2023; 44:170-193. [DOI: 10.1053/j.sult.2023.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
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11
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Mourad C, Cosentino A, Nicod Lalonde M, Omoumi P. Advances in Bone Marrow Imaging: Strengths and Limitations from a Clinical Perspective. Semin Musculoskelet Radiol 2023; 27:3-21. [PMID: 36868241 PMCID: PMC9984270 DOI: 10.1055/s-0043-1761612] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Conventional magnetic resonance imaging (MRI) remains the modality of choice to image bone marrow. However, the last few decades have witnessed the emergence and development of novel MRI techniques, such as chemical shift imaging, diffusion-weighted imaging, dynamic contrast-enhanced MRI, and whole-body MRI, as well as spectral computed tomography and nuclear medicine techniques. We summarize the technical bases behind these methods, in relation to the common physiologic and pathologic processes involving the bone marrow. We present the strengths and limitations of these imaging methods and consider their added value compared with conventional imaging in assessing non-neoplastic disorders like septic, rheumatologic, traumatic, and metabolic conditions. The potential usefulness of these methods to differentiate between benign and malignant bone marrow lesions is discussed. Finally, we consider the limitations hampering a more widespread use of these techniques in clinical practice.
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Affiliation(s)
- Charbel Mourad
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.,Department of Diagnostic and Interventional Radiology, Hôpital Libanais Geitaoui- CHU, Beyrouth, Lebanon
| | - Aurelio Cosentino
- Department of Radiology, Hôpital Riviera-Chablais, Vaud-Valais, Rennaz, Switzerland
| | - Marie Nicod Lalonde
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Patrick Omoumi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
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Baba A, Kurokawa R, Kurokawa M, Ota Y, Srinivasan A. Dynamic Contrast-Enhanced MRI Parameters and Normalized ADC Values Could Aid Differentiation of Skull Base Osteomyelitis from Nasopharyngeal Cancer. AJNR Am J Neuroradiol 2023; 44:74-78. [PMID: 36521963 PMCID: PMC9835913 DOI: 10.3174/ajnr.a7740] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 11/15/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND AND PURPOSE The skull base osteomyelitis sometimes can be difficult to distinguish from nasopharyngeal cancer. This study aimed to investigate the differences between skull base osteomyelitis and nasopharyngeal cancer using dynamic contrast-enhanced MR imaging and normalized ADC values. MATERIALS AND METHODS This study included 8 and 12 patients with skull base osteomyelitis and nasopharyngeal cancer, respectively, who underwent dynamic contrast-enhanced MR imaging and DWI before primary treatment. Quantitative dynamic contrast-enhanced MR imaging parameters and ADC values of the ROIs were analyzed. Normalized ADC parameters were calculated by dividing the ROIs of the lesion by that of the spinal cord. RESULTS The rate transfer constant between extravascular extracellular space and blood plasma per minute (Kep) was significantly lower in patients with skull base osteomyelitis than in those with nasopharyngeal cancer (median, 0.43 versus 0.57; P = .04). The optimal cutoff value of Kep was 0.48 (area under the curve, 0.78; 95% CI, 0.55-1). The normalized mean ADC was significantly higher in patients with skull base osteomyelitis than in those with nasopharyngeal cancer (median, 1.90 versus 0.87; P < .001). The cutoff value of normalized mean ADC was 1.55 (area under the curve, 0.96; 95% CI, 0.87-1). The area under the curve of the combination of dynamic contrast-enhanced MR imaging parameters (Kep and extravascular extracellular space volume per unit tissue volume) was 0.89 (95% CI, 0.73-1), and the area under the curve of the combination of dynamic contrast-enhanced MR imaging parameters and normalized mean ADC value was 0.98 (95% CI, 0.93-1). CONCLUSIONS Quantitative dynamic contrast-enhanced MR imaging parameters and normalized ADC values may be useful in differentiating skull base osteomyelitis and nasopharyngeal cancer. The combination of dynamic contrast-enhanced MR imaging parameters and normalized ADC values outperformed each measure in isolation.
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Affiliation(s)
- A Baba
- From the Division of Neuroradiology (A.B., R.K., M.K., Y.O., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
- Department of Radiology (A.B.), The Jikei University School of Medicine, Tokyo, Japan
| | - R Kurokawa
- From the Division of Neuroradiology (A.B., R.K., M.K., Y.O., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - M Kurokawa
- From the Division of Neuroradiology (A.B., R.K., M.K., Y.O., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Y Ota
- From the Division of Neuroradiology (A.B., R.K., M.K., Y.O., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - A Srinivasan
- From the Division of Neuroradiology (A.B., R.K., M.K., Y.O., A.S.), Department of Radiology, University of Michigan, Ann Arbor, Michigan
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Fukuda T, Kayama R, Ogiwara S, Yonenaga T, Ojiri H. Comparison of the axillary lymph node between rheumatoid arthritis and psoriatic arthritis with computed tomography. Acta Radiol Open 2022; 11:20584601221112616. [PMID: 35846390 PMCID: PMC9284224 DOI: 10.1177/20584601221112616] [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: 04/04/2022] [Accepted: 06/22/2022] [Indexed: 11/17/2022] Open
Abstract
Backgrounds There is a lack of universally available biomarker to differentiate rheumatoid arthritis (RA) and psoriatic arthritis (PsA). Purpose to see if the size of the axillary lymphnodes (ALNs) and the frequency of lymphadenopathy are useful biomarker to differentiate RA and PsA. Material and Methods Forty RA and 19 PsA patients without previous biologics usage were retrospectively included. Chest CT was assessed for the presence of lymphadenopathy and the size of the largest ALN. Frequency of lymphadenopathies was statistically compared between RA and PsA. The short axis and the long axis of the largest ALN were also compared and receiver operating characteristic (ROC) curve analysis was performed. Results Frequency of axillary lymphadenopathy was significantly higher in RA than in PsA (80% vs 31.6%, p < .001). Number of lymphadenopathies in each patient was also significantly higher in RA than in PsA (3.0 vs 1.2 per patient, p = .005). Sensitivity and specificity for differentiating RA from PsA by the presence of at least one axillary lymphadenopathy were 0.8 and 0.68, respectively. The short axis of the largest ALNs in RA was significantly longer than in PsA (6.5 ± 1.6 mm vs 4.7 ± 1.7 mm, p < .001). ROC curve analysis of the short axis showed AUC of 0.75 ( p = .002) and the cutoff value of 5.1 mm with a sensitivity of 0.83 and specificity of 0.74, when differentiating RA and PsA. Conclusion Presence of ALN lymphadenopathy and the short axis of the largest ALN may have a potential utility in differentiating RA and PsA.
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Affiliation(s)
- Takeshi Fukuda
- Department of Radiology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan
| | - Reina Kayama
- Department of Radiology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan
| | - Sho Ogiwara
- Department of Radiology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan
- Department of Radiology, Kawaguchi Municipal Medical Center, Kawaguchi-shi, Saitama, Japan
| | - Takenori Yonenaga
- Department of Radiology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan
| | - Hiroya Ojiri
- Department of Radiology, The Jikei University School of Medicine, Minato-ku, Tokyo, Japan
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Borgheresi A, De Muzio F, Agostini A, Ottaviani L, Bruno A, Granata V, Fusco R, Danti G, Flammia F, Grassi R, Grassi F, Bruno F, Palumbo P, Barile A, Miele V, Giovagnoni A. Lymph Nodes Evaluation in Rectal Cancer: Where Do We Stand and Future Perspective. J Clin Med 2022; 11:2599. [PMID: 35566723 PMCID: PMC9104021 DOI: 10.3390/jcm11092599] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 04/25/2022] [Accepted: 05/03/2022] [Indexed: 12/12/2022] Open
Abstract
The assessment of nodal involvement in patients with rectal cancer (RC) is fundamental in disease management. Magnetic Resonance Imaging (MRI) is routinely used for local and nodal staging of RC by using morphological criteria. The actual dimensional and morphological criteria for nodal assessment present several limitations in terms of sensitivity and specificity. For these reasons, several different techniques, such as Diffusion Weighted Imaging (DWI), Intravoxel Incoherent Motion (IVIM), Diffusion Kurtosis Imaging (DKI), and Dynamic Contrast Enhancement (DCE) in MRI have been introduced but still not fully validated. Positron Emission Tomography (PET)/CT plays a pivotal role in the assessment of LNs; more recently PET/MRI has been introduced. The advantages and limitations of these imaging modalities will be provided in this narrative review. The second part of the review includes experimental techniques, such as iron-oxide particles (SPIO), and dual-energy CT (DECT). Radiomics analysis is an active field of research, and the evidence about LNs in RC will be discussed. The review also discusses the different recommendations between the European and North American guidelines for the evaluation of LNs in RC, from anatomical considerations to structured reporting.
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Affiliation(s)
- Alessandra Borgheresi
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | - Andrea Agostini
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
| | - Letizia Ottaviani
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
| | - Alessandra Bruno
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale IRCCS di Napoli, 80131 Naples, Italy;
| | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy
| | - Ginevra Danti
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Federica Flammia
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Roberta Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Francesca Grassi
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Division of Radiology, Università degli Studi della Campania Luigi Vanvitelli, 80128 Naples, Italy
| | - Federico Bruno
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Pierpaolo Palumbo
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Abruzzo Health Unit 1, Department of Diagnostic Imaging, Area of Cardiovascular and Interventional Imaging, 67100 L’Aquila, Italy
| | - Antonio Barile
- Department of Biotechnological and Applied Clinical Sciences, University of L’Aquila, 67100 L’Aquila, Italy;
| | - Vittorio Miele
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy; (G.D.); (R.G.); (F.G.); (F.B.); (P.P.); (V.M.)
- Department of Radiology, Azienda Ospedaliero-Universitaria Careggi, Largo Brambilla 3, 50134 Florence, Italy;
| | - Andrea Giovagnoni
- Department of Clinical, Special and Dental Sciences, University Politecnica delle Marche, 60121 Ancona, Italy; (A.B.); (A.A.); (A.B.); (A.G.)
- Department of Radiological Sciences, University Hospital Ospedali Riuniti, 60126 Ancona, Italy;
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