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Katz G, Hernandez-Barco Y, Palumbo D, Guy TV, Dong L, Perugino CA. Proliferative features of IgG4-related disease. THE LANCET. RHEUMATOLOGY 2024; 6:e481-e492. [PMID: 38574744 DOI: 10.1016/s2665-9913(24)00022-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 04/06/2024]
Abstract
IgG4-related disease is an immune-mediated disease that can lead to substantial morbidity and organ damage. Capable of affecting nearly any organ system or anatomic site, and showing considerable overlap in clinical presentation with various other diseases, IgG4-related disease often poses a diagnostic challenge for clinicians. Furthermore, there are no diagnostic biomarkers with high specificity for IgG4-related disease, and histopathological examination is nuanced and requires clinical correlation for accurate diagnosis. Therefore, it is crucial for clinicians to recognise the clinical phenotypes of IgG4-related disease. The disease is generally considered to have predominantly fibrotic and proliferative (or inflammatory) manifestations, with distinct clinical, serological and histopathological findings associated with each manifestation. However, the fibrotic and proliferative manifestations of this disease frequently occur together, thereby blurring this dichotomous distinction. In this Series paper, we provide a detailed overview of the clinical manifestations typical of the proliferative features of IgG4-related disease, with an emphasis on the diagnostic evaluation and differential diagnosis of each proliferative disease manifestation. In addition, we summarise the immune mechanisms underlying IgG4-related disease, suggest a framework for how to approach management and monitoring after the diagnosis is established, and highlight current unmet needs for patient care surrounding this disease.
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Affiliation(s)
- Guy Katz
- Rheumatology Unit, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Yasmin Hernandez-Barco
- Pancreatology Unit, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Diego Palumbo
- San Raffaele Scientific Institute, Radiology, Milan, Italy
| | - Thomas V Guy
- Royal Prince Alfred Hospital, Camperdown, NSW, Australia; School of Medical Sciences, The University of Sydney, Camperdown, NSW, Australia; Ragon Institute of Massachusetts Gneral Hospital, Massachusetts Institute of Technology, and Harvard University, Cambridge, MA, USA
| | - Lingli Dong
- Department of Rheumatology and Immunology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cory A Perugino
- Rheumatology Unit, Massachusetts General Hospital, Boston, MA, USA; Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
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2
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Wang ZH, Zhu L, Xue HD, Jin ZY. Quantitative MR imaging biomarkers for distinguishing inflammatory pancreatic mass and pancreatic cancer-a systematic review and meta-analysis. Eur Radiol 2024:10.1007/s00330-024-10720-9. [PMID: 38639911 DOI: 10.1007/s00330-024-10720-9] [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: 10/12/2023] [Revised: 02/09/2024] [Accepted: 03/14/2024] [Indexed: 04/20/2024]
Abstract
OBJECTIVES To evaluate the diagnostic performance of quantitative magnetic resonance (MR) imaging biomarkers in distinguishing between inflammatory pancreatic masses (IPM) and pancreatic cancer (PC). METHODS A literature search was conducted using PubMed, Embase, the Cochrane Library, and Web of Science through August 2023. Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) was used to evaluate the risk of bias and applicability of the studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated using the DerSimonian-Laird method. Univariate meta-regression analysis was used to identify the potential factors of heterogeneity. RESULTS Twenty-four studies were included in this meta-analysis. The two main types of IPM, mass-forming pancreatitis (MFP) and autoimmune pancreatitis (AIP), differ in their apparent diffusion coefficient (ADC) values. Compared with PC, the ADC value was higher in MFP but lower in AIP. The pooled sensitivity/specificity of ADC were 0.80/0.85 for distinguishing MFP from PC and 0.82/0.84 for distinguishing AIP from PC. The pooled sensitivity/specificity for the maximal diameter of the upstream main pancreatic duct (dMPD) was 0.86/0.74, with a cutoff of dMPD ≤ 4 mm, and 0.97/0.52, with a cutoff of dMPD ≤ 5 mm. The pooled sensitivity/specificity for perfusion fraction (f) was 0.82/0.68, and 0.82/0.77 for mass stiffness values. CONCLUSIONS Quantitative MR imaging biomarkers are useful in distinguishing between IPM and PC. ADC values differ between MFP and AIP, and they should be separated for consideration in future studies. CLINICAL RELEVANCE STATEMENT Quantitative MR parameters could serve as non-invasive imaging biomarkers for differentiating malignant pancreatic neoplasms from inflammatory masses of the pancreas, and hence help to avoid unnecessary surgery. KEY POINTS • Several quantitative MR imaging biomarkers performed well in differential diagnosis between inflammatory pancreatic mass and pancreatic cancer. • The ADC value could discern pancreatic cancer from mass-forming pancreatitis or autoimmune pancreatitis, if the two inflammatory mass types are not combined. • The diameter of main pancreatic duct had the highest specificity for differentiating autoimmune pancreatitis from pancreatic cancer.
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Affiliation(s)
- Zi-He Wang
- School of Medicine, Anhui Medical University, Hefei, China
| | - Liang Zhu
- Department of Radiology, Peking Union Medical College Hospital, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China.
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China.
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Shuaifuyuan No. 1, Dongcheng District, Beijing, 100730, China
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Morana G, Beleù A, Geraci L, Tomaiuolo L, Venturini S. Imaging of the Liver and Pancreas: The Added Value of MRI. Diagnostics (Basel) 2024; 14:693. [PMID: 38611607 PMCID: PMC11011374 DOI: 10.3390/diagnostics14070693] [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: 02/09/2024] [Revised: 03/19/2024] [Accepted: 03/23/2024] [Indexed: 04/14/2024] Open
Abstract
MR is a powerful diagnostic tool in the diagnosis and management of most hepatic and pancreatic diseases. Thanks to its multiple sequences, the use of dedicated contrast media and special techniques, it allows a multiparametric approach able to provide both morphological and functional information for many pathological conditions. The knowledge of correct technique is fundamental in order to obtain a correct diagnosis. In this paper, different MR sequences will be illustrated in the evaluation of liver and pancreatic diseases, especially those sequences which provide information not otherwise obtainable with other imaging techniques. Practical MR protocols with the most common indications of MR in the study of the liver and pancreas are provided.
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Affiliation(s)
- Giovanni Morana
- Radiological Department, General Hospital Treviso, 31100 Treviso, Italy; (A.B.); (L.G.); (L.T.)
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Gallo C, Dispinzieri G, Zucchini N, Invernizzi P, Massironi S. Autoimmune pancreatitis: Cornerstones and future perspectives. World J Gastroenterol 2024; 30:817-832. [PMID: 38516247 PMCID: PMC10950636 DOI: 10.3748/wjg.v30.i8.817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 12/18/2023] [Accepted: 01/25/2024] [Indexed: 02/26/2024] Open
Abstract
Autoimmune pancreatitis (AIP) is an autoimmune subtype of chronic pancreatitis resulting from the aberrant immune response against the pancreas, leading to inflammation and fibrosis. Although AIP is rare, its incidence is increasing and is often misdiagnosed as other pancreatic diseases. AIP is commonly classified into two types. Type 1 AIP (AIP-1) is typically associated with elevated serum immunoglobulin G4 (IgG4) levels and systemic manifestations, while type 2 AIP is typically a more localized form of the disease, and may coexist with other autoimmune disorders, especially inflammatory bowel diseases. Additionally, there is emerging recognition of a third type (type 3 AIP), which refers to immunotherapy-triggered AIP, although this classification is still gaining acceptance in medical literature. The clinical manifestations of AIP mainly include painless jaundice and weight loss. Elevated serum IgG4 levels are particularly characteristic of AIP-1. Diagnosis relies on a combination of clinical, laboratory, radiological, and histological findings, given the similarity of AIP symptoms to other pancreatic disorders. The mainstay of treatment for AIP is steroid therapy, which is effective in most cases. Severe cases might require additional imm-unosuppressive agents. This review aims to summarize the current knowledge of AIP, encompassing its epidemiology, etiology, clinical presentation, diagnosis, and treatment options. We also address the challenges and controversies in diagnosing and treating AIP, such as distinguishing it from pancreatic cancer and managing long-term treatment, highlighting the need for increased awareness and knowledge of this complex disease.
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Affiliation(s)
- Camilla Gallo
- Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, Fondazione IRCCS San Gerardo dei Tintori; University of Milano-Bicocca, Monza 20900, Italy
| | - Giulia Dispinzieri
- Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, Fondazione IRCCS San Gerardo dei Tintori; University of Milano-Bicocca, Monza 20900, Italy
| | - Nicola Zucchini
- Department of Pathology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Pietro Invernizzi
- Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, Fondazione IRCCS San Gerardo dei Tintori; University of Milano-Bicocca, Monza 20900, Italy
| | - Sara Massironi
- Division of Gastroenterology and Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, Fondazione IRCCS San Gerardo dei Tintori; University of Milano-Bicocca, Monza 20900, Italy
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Wessling J, Juchems M, Grenacher L, Schreyer AG. [Autoimmune pancreatitis versus pancreatic cancer]. RADIOLOGIE (HEIDELBERG, GERMANY) 2023; 63:886-893. [PMID: 37947862 DOI: 10.1007/s00117-023-01240-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 10/27/2023] [Indexed: 11/12/2023]
Abstract
CLINICAL ISSUE Autoimmune pancreatitis (AIP) is classified as a distinct form of pancreatitis according to the guidelines. It is characterized by imaging morphologic and histologic features and is associated with extrapancreatic manifestations in type 1 IgG 4-associated disease. Symptoms and findings almost always improve with administration of steroids. Differentiation from pancreatic ductal adenocarcinoma is required, particularly in the presence of AIP with focal parenchymal involvement. STANDARD RADIOLOGIC PROCEDURES If AIP is suspected, abdominal ultrasound and/or endosonography, computed tomography (CT), and preferably magnetic resonance imaging (MRI) are indicated. A distinction is made between parenchymal and ductal changes that specifically indicate the presence of AIP. METHODOLOGICAL INNOVATIONS AND EVALUATION The diagnosis of autoimmune pancreatitis should be made based on the International Consensus Criteria (ICDC), in which the five main features (imaging, serology, histology, other organ involvement, response to steroid medication) are assessed. In type 1 AIP, typical imaging changes are sufficient to establish the diagnosis even with negative histology, whereas for type 2 AIP, histologic evidence is required. Imaging changes help in the differential diagnosis from pancreatic cancer. PRACTICAL RECOMMENDATIONS The following article addresses and evaluates crucial imaging diagnostic CT and MRI criteria for correct classification of findings, description of results, and differentiation of autoimmune pancreatitis from pancreatic cancer.
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Affiliation(s)
- J Wessling
- Zentrum für Radiologie, Neuroradiologie und Nuklearmedizin, Clemenshospital, Raphaelsklinik und EVK Münster, Düesbergweg 124, 48153, Münster, Deutschland.
| | - M Juchems
- Diagnostische und Interventionelle Radiologie, Klinikum Konstanz, Konstanz, Deutschland
| | | | - A G Schreyer
- Institut für diagnostische und interventionelle Radiologie, Medizinische Hochschule Brandenburg Theodor Fontane Klinikum Brandenburg, Brandenburg, Deutschland
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Yoon SB, Jeon TY, Moon SH, Shin DW, Lee SM, Choi MH, Min JH, Kim MJ. Differentiation of autoimmune pancreatitis from pancreatic adenocarcinoma using CT characteristics: a systematic review and meta-analysis. Eur Radiol 2023; 33:9010-9021. [PMID: 37466708 DOI: 10.1007/s00330-023-09959-5] [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: 11/28/2022] [Revised: 05/15/2023] [Accepted: 05/23/2023] [Indexed: 07/20/2023]
Abstract
OBJECTIVES To determine informational CT findings for distinguishing autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC) and to review their diagnostic accuracy. METHODS A systematic and detailed literature review was performed through PubMed, EMBASE, and the Cochrane library. Similar descriptors to embody the identical image finding were labeled as a single CT characteristic. We calculated the pooled diagnostic odds ratios (DORs) of each CT characteristic using a bivariate random-effects model. RESULTS A total of 145 various descriptors from 15 studies (including 562 AIP and 869 PDAC patients) were categorized into 16 CT characteristics. According to the pooled DOR, 16 CT characteristics were classified into three groups (suggesting AIP, suggesting PDAC, and not informational). Seven characteristics suggesting AIP were diffuse pancreatic enlargement (DOR, 48), delayed homogeneous enhancement (DOR, 46), capsule-like rim (DOR, 34), multiple pancreatic masses (DOR, 16), renal involvement (DOR, 15), retroperitoneal fibrosis (DOR, 13), and bile duct involvement (DOR, 8). Delayed homogeneous enhancement showed a pooled sensitivity of 83% and specificity of 85%. The other six characteristics showed relatively low sensitivity (12-63%) but high specificity (93-99%). Four characteristics suggesting PDAC were discrete pancreatic mass (DOR, 23), pancreatic duct cutoff (DOR, 16), upstream main pancreatic duct dilatation (DOR, 8), and upstream parenchymal atrophy (DOR, 7). CONCLUSION Eleven CT characteristics were informational to distinguish AIP from PDAC. Diffuse pancreatic enlargement, delayed homogeneous enhancement, and capsule-like rim suggested AIP with the highest DORs, whereas discrete pancreatic mass suggested PDAC. However, pooled sensitivities of informational CT characteristics were moderate. CLINICAL RELEVANCE STATEMENT This meta-analysis underscores eleven distinctive CT characteristics that aid in differentiating autoimmune pancreatitis from pancreatic adenocarcinoma, potentially preventing misdiagnoses in patients presenting with focal/diffuse pancreatic enlargement. KEY POINTS • Diffuse pancreatic enlargement (pooled diagnostic odds ratio [DOR], 48), delayed homogeneous enhancement (46), and capsule-like rim (34) were CT characteristics suggesting autoimmune pancreatitis. • The CT characteristics suggesting autoimmune pancreatitis, except delayed homogeneous enhancement, had a general tendency to show relatively low sensitivity (12-63%) but high specificity (93-99%). • Discrete pancreatic mass (pooled diagnostic odds ratio, 23) was the CT characteristic suggesting pancreatic ductal adenocarcinoma with the highest pooled DORs.
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Affiliation(s)
- Seung Bae Yoon
- Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Tae Yeon Jeon
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Sung-Hoon Moon
- Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, 22 Gwanpyeong-ro 170 beon-gil, Dongan-gu, Anyang, Gyeonggi-do, 14068, South Korea.
| | - Dong Woo Shin
- Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, 22 Gwanpyeong-ro 170 beon-gil, Dongan-gu, Anyang, Gyeonggi-do, 14068, South Korea
| | - Sang Min Lee
- Department of Radiology, Cha Gangnam Medical Center, Seoul, South Korea
| | - Moon Hyung Choi
- Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Ji Hye Min
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
| | - Min-Jeong Kim
- Department of Radiology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang, South Korea
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Yao L, Zhang Z, Keles E, Yazici C, Tirkes T, Bagci U. A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging. Curr Opin Gastroenterol 2023; 39:436-447. [PMID: 37523001 PMCID: PMC10403281 DOI: 10.1097/mog.0000000000000966] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/01/2023]
Abstract
PURPOSE OF REVIEW Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). RECENT FINDINGS This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. SUMMARY Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.
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Affiliation(s)
- Lanhong Yao
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Zheyuan Zhang
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Elif Keles
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
| | - Cemal Yazici
- Division of Gastroentrrology and Hepatology, University of Illinois Chicago, Chicago, Illinois
| | - Temel Tirkes
- Department of Radiology & Imaging Sciences, Medicine and Urology, Indiana University School of Medicine, Indianapolis, Indianapolis, USA
| | - Ulas Bagci
- Machine & Hybrid Intelligence Lab, Department of Radiology, Northwestern University
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Histogram array and convolutional neural network of DWI for differentiating pancreatic ductal adenocarcinomas from solid pseudopapillary neoplasms and neuroendocrine neoplasms. Clin Imaging 2023; 96:15-22. [PMID: 36736182 DOI: 10.1016/j.clinimag.2023.01.008] [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: 10/11/2022] [Revised: 12/20/2022] [Accepted: 01/23/2023] [Indexed: 01/28/2023]
Abstract
PURPOSE This study aimed to investigate the diagnostic performance of the histogram array and convolutional neural network (CNN) based on diffusion-weighted imaging (DWI) with multiple b-values under magnetic resonance imaging (MRI) to distinguish pancreatic ductal adenocarcinomas (PDACs) from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine neoplasms (PNENs). METHODS This retrospective study consisted of patients diagnosed with PDACs (n = 132), PNENs (n = 45) and SPNs (n = 54). All patients underwent 3.0-T MRI including DWI with 10 b values. The regions of interest (ROIs) of pancreatic tumor were manually drawn using ITK-SNAP software, which included entire tumor at DWI (b = 1500 s/m2). The histogram array was obtained through the ROIs from multiple b-value data. PyTorch (version 1.11) was used to construct a CNN classifier to categorize the histogram array into PDACs, PNENs or SPNs. RESULTS The area under the curves (AUCs) of the histogram array and the CNN model for differentiating PDACs from PNENs and SPNs were 0.896, 0.846, and 0.839 in the training, validation and testing cohorts, respectively. The accuracy, sensitivity and specificity were 90.22%, 96.23%, and 82.05% in the training cohort, 84.78%, 96.15%, and 70.0% in the validation cohort, and 81.72%, 90.57%, and 70.0% in the testing cohort. The performance of CNN with AUC of 0.865 for this differentiation was significantly higher than that of f with AUC = 0.755 (P = 0.0057) and α with AUC = 0.776 (P = 0.0278) in all patients. CONCLUSION The histogram array and CNN based on DWI data with multiple b-values using MRI provided an accurate diagnostic performance to differentiate PDACs from PNENs and SPNs.
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Ha J, Kim DW, Choi SH. Author's reply: "ADC values from diffusion-weighted imaging may be lower for autoimmune pancreatitis than for pancreatic ductal adenocarcinoma". Dig Liver Dis 2022; 54:994-995. [PMID: 35614006 DOI: 10.1016/j.dld.2022.04.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 04/21/2022] [Accepted: 04/26/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Jiyeon Ha
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Dong Wook Kim
- Department of Radiology and the Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Korea
| | - Sang Hyun Choi
- Department of Radiology and the Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Korea.
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Moon SH, Yoon SB, Jeon TY. ADC values from diffusion-weighted imaging may be lower for autoimmune pancreatitis than for pancreatic ductal adenocarcinoma. Dig Liver Dis 2022; 54:992-993. [PMID: 35487851 DOI: 10.1016/j.dld.2022.04.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2022] [Accepted: 04/08/2022] [Indexed: 12/12/2022]
Affiliation(s)
- Sung-Hoon Moon
- Department of Internal Medicine, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, 22, Gwanpyeong-ro 170 beon-gil, Dongan-gu, Anyang, Gyeonggi-do 14068, South Korea.
| | - Seung Bae Yoon
- Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea
| | - Tae Yeon Jeon
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, School of Medicine, Sungkyunkwan University, Seoul, South Korea
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