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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; 34:6738-6750. [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] [MESH Headings] [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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Khayat S, Choudhary K, Claude Nshimiyimana J, Gurav J, Hneini A, Nazir A, Chaito H, Wojtara M, Uwishema O. Pancreatic cancer: from early detection to personalized treatment approaches. Ann Med Surg (Lond) 2024; 86:2866-2872. [PMID: 38694319 PMCID: PMC11060269 DOI: 10.1097/ms9.0000000000002011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 03/19/2024] [Indexed: 05/04/2024] Open
Abstract
Pancreatic cancer is notorious for its persistently poor prognosis and health outcomes, so some of the questions that may be begged are "Why is it mostly diagnosed at end stage?", "What could we possibly do with the advancing technology in today's world to detect early pancreatic cancer and intervene?", and "Are there any implementation of the existing novel imaging technologies?". Well, to start with, this is in part because the majority of patients presented would already have reached a locally advanced or metastatic stage at the time of diagnosis due to its highly aggressive characteristics and lack of symptoms. Due to this striking disparity in survival, advancements in early detection and intervention are likely to significantly increase patients' survival. Presently, screening is frequently used in high-risk individuals in order to obtain an early pancreatic cancer diagnosis. Having a thorough understanding of the pathogenesis and risk factors of pancreatic cancer may enable us to identify individuals at high risk, diagnose the disease early, and begin treatment promptly. In this review, the authors outline the clinical hurdles to early pancreatic cancer detection, describe high-risk populations, and discuss current screening initiatives for high-risk individuals. The ultimate goal of this current review is to study the roles of both traditional and novel imaging modalities for early pancreatic cancer detection. A lot of the novel imaging techniques mentioned seem promising, but they need to be put to the test on a large scale and may need to be combined with other non-invasive biomarkers before they can be widely used.
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Affiliation(s)
| | | | | | | | - Asmaa Hneini
- Faculty of Medicine, Beirut Arab University, Beirut, Lebanon
| | - Abubakar Nazir
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- Department of Medicine, King Edward Medical University, Lahore, Pakistan
| | - Hassan Chaito
- Faculty of Medicine, Beirut Arab University, Beirut, Lebanon
| | - Magda Wojtara
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
| | - Olivier Uwishema
- Oli Health Magazine Organization, Research and Education, Kigali, Rwanda
- Faculty of Medicine, Karadeniz Technical University, Trabzon, Turkey
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3
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Mak AL, Wassenaar N, van Dijk AM, Troelstra M, Houttu V, van Son K, Driessen S, Zwirs D, van den Berg-Faay S, Shumbayawonda E, Runge J, Doukas M, Verheij J, Beuers U, Nieuwdorp M, Cahen DL, Nederveen A, Gurney-Champion O, Holleboom A. Intrapancreatic fat deposition is unrelated to liver steatosis in metabolic dysfunction-associated steatotic liver disease. JHEP Rep 2024; 6:100998. [PMID: 38379586 PMCID: PMC10877191 DOI: 10.1016/j.jhepr.2023.100998] [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: 06/08/2023] [Revised: 11/21/2023] [Accepted: 12/21/2023] [Indexed: 02/22/2024] Open
Abstract
Background & Aims Individuals with obesity may develop intrapancreatic fat deposition (IPFD) and fatty pancreas disease (FPD). Whether this causes inflammation and fibrosis and leads to pancreatic dysfunction is less established than for liver damage in metabolic dysfunction-associated steatotic liver disease (MASLD). Moreover, the interrelations of FPD and MASLD are poorly understood. Therefore, we aimed to assess IPFD and fibro-inflammation in relation to pancreatic function and liver disease severity in individuals with MASLD. Methods Seventy-six participants from the Amsterdam MASLD-MASH cohort (ANCHOR) study underwent liver biopsy and multiparametric MRI of the liver and pancreas, consisting of proton-density fat fraction sequences, T1 mapping and intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). Results The prevalence of FPD was 37.3%. There was a clear correlation between pancreatic T1 relaxation time, which indicates fibro-inflammation, and parameters of glycemic dysregulation, namely HbA1c (R = 0.59; p <0.001), fasting glucose (R = 0.51; p <0.001) and the presence of type 2 diabetes (mean 802.0 ms vs. 733.6 ms; p <0.05). In contrast, there was no relation between IPFD and hepatic fat content (R = 0.03; p = 0.80). Pancreatic IVIM diffusion (IVIM-D) was lower in advanced liver fibrosis (p <0.05) and pancreatic perfusion (IVIM-f), reflecting vessel density, inversely correlated to histological MASLD activity (p <0.05). Conclusions Consistent relations exist between pancreatic fibro-inflammation on MRI and endocrine function in individuals with MASLD. However, despite shared dysmetabolic drivers, our study suggests IPFD is a separate pathophysiological process from MASLD. Impact and implications Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide and 68% of people with type 2 diabetes have MASLD. However, fat infiltration and inflammation in the pancreas are understudied in individuals with MASLD. In this cross-sectional MRI study, we found no relationship between fat accumulation in the pancreas and liver in a cohort of patients with MASLD. However, our results show that inflammatory and fibrotic processes in the pancreas may be interrelated to features of type 2 diabetes and to the severity of liver disease in patients with MASLD. Overall, the results suggest that pancreatic endocrine dysfunction in individuals with MASLD may be more related to glucotoxicity than to lipotoxicity. Clinical trial number NTR7191 (Dutch Trial Register).
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Affiliation(s)
- Anne Linde Mak
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Nienke Wassenaar
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Anne-Marieke van Dijk
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Marian Troelstra
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Veera Houttu
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Koen van Son
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Gastroenterology and Hepatology, Radboudumc, Nijmegen, The Netherlands
| | - Stan Driessen
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Diona Zwirs
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Sandra van den Berg-Faay
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | | | - Jurgen Runge
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Michail Doukas
- Department of Pathology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Joanne Verheij
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
- Department of Pathology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Ulrich Beuers
- Department of Gastroenterology and Hepatology, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Max Nieuwdorp
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Djuna L. Cahen
- Department of Gastroenterology and Hepatology, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Aart Nederveen
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
| | - Oliver Gurney-Champion
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Cancer Center Amsterdam, Imaging and Biomarkers, Amsterdam, The Netherlands
| | - Adriaan Holleboom
- Department of Vascular Medicine, Amsterdam University Medical Centers, Amsterdam, The Netherlands
- Amsterdam Gastroenterology Endocrinology Metabolism (AGEM) Institute, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
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Fukukura Y, Kanki A. Quantitative Magnetic Resonance Imaging for the Pancreas: Current Status. Invest Radiol 2024; 59:69-77. [PMID: 37433065 DOI: 10.1097/rli.0000000000001002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2023]
Abstract
ABSTRACT Magnetic resonance imaging (MRI) is important for evaluating pancreatic disorders, and anatomical landmarks play a major role in the interpretation of results. Quantitative MRI is an effective diagnostic modality for various pathologic conditions, as it allows the investigation of various physical parameters. Recent advancements in quantitative MRI techniques have significantly improved the accuracy of pancreatic MRI. Consequently, this method has become an essential tool for the diagnosis, treatment, and monitoring of pancreatic diseases. This comprehensive review article presents the currently available evidence on the clinical utility of quantitative MRI of the pancreas.
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Affiliation(s)
- Yoshihiko Fukukura
- From the Department of Radiology, Kawasaki Medical School, Kurashiki City, Okayama, Japan
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5
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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: 1] [Impact Index Per Article: 1.0] [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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Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis. Bioengineering (Basel) 2023; 10:bioengineering10010083. [PMID: 36671655 PMCID: PMC9854749 DOI: 10.3390/bioengineering10010083] [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: 12/19/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.
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7
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Gupta P, Rana P, Marodia Y, Samanta J, Sharma V, Sinha SK, Singh H, Gupta V, Yadav TD, Sreenivasan R, Vaiphei K, Rajwanshi A, Kochhar R, Sandhu M. Contrast-enhanced ultrasound of solid pancreatic head lesions: a prospective study. Eur Radiol 2022; 32:6668-6677. [PMID: 35587829 DOI: 10.1007/s00330-022-08854-9] [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: 12/18/2021] [Revised: 04/26/2022] [Accepted: 04/30/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE To evaluate the role of contrast-enhanced ultrasound (CEUS) in the differential diagnosis of solid pancreatic head lesions (SPHL). METHODS This prospective study comprised consecutive patients with SPHL who underwent CEUS evaluation of the pancreas. Findings recorded at CEUS were enhancement patterns (degree, completeness, centripetal enhancement, and percentage enhancement) and presence of central vessels. In addition, time to peak (TTP) and washout time (WT) were recorded. The final diagnosis was based on histopathology or cytology. Multivariate analysis was performed to identify parameters that were significantly associated with pancreatic ductal adenocarcinoma (PDAC). RESULTS Ninety-eight patients (median age 53.8 years, 59 males) were evaluated. The final diagnosis was PDAC (n = 64, 65.3%), inflammatory mass (n = 16, 16.3%), neuroendocrine tumor (NET, n = 14, 14.3%), and other tumors (n = 4, 4.1%). Hypoenhancement, incomplete enhancement, and centripetal enhancement were significantly more common in PDAC than non-PDAC lesions (p = 0.001, p = 0.031, and p = 0.002, respectively). Central vessels were present in a significantly greater number of non-PDAC lesions (p = 0.0001). Hypoenhancement with < 30% enhancement at CEUS had sensitivity and specificity of 80.6% and 67.7%, respectively, for PDAC. There was no significant difference in the TTP and WT between PDAC and non - PDAC lesions. However, the WT was significantly shorter in PDAC compared to NET (p = 0.011). In multivariate analysis, lack of central vessels was significantly associated with a PDAC diagnosis. CONCLUSION CEUS is a useful tool for the evaluation of SPHL. CEUS can be incorporated into the diagnostic algorithm to differentiate PDAC from non-PDAC lesions. KEY POINTS • Hypoenhancement and incomplete enhancement at CEUS were significantly more common in PDAC than in non-PDAC. • Central vessels at CEUS were significantly associated with PDAC. • There was no difference in TTP and WT between PDAC and non-PDAC lesions.
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Affiliation(s)
- Pankaj Gupta
- Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India.
| | - Pratyaksha Rana
- Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India
| | - Yashi Marodia
- Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India
| | | | - Vishal Sharma
- Department of Gastroenterology, PGIMER, Chandigarh, India
| | - Saroj K Sinha
- Department of Gastroenterology, PGIMER, Chandigarh, India
| | - Harjeet Singh
- Department of Surgical Gastroenterology, PGIMER, Chandigarh, India
| | - Vikas Gupta
- Department of Surgical Gastroenterology, PGIMER, Chandigarh, India
| | | | | | - Kim Vaiphei
- Department of Histopathology, PGIMER, Chandigarh, India
| | | | - Rakesh Kochhar
- Department of Gastroenterology, PGIMER, Chandigarh, India
| | - Manavjit Sandhu
- Department of Radiodiagnosis and Imaging, PGIMER, Chandigarh, India
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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: 6] [Impact Index Per Article: 3.0] [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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Cervelli R, Cencini M, Cacciato Insilla A, Aringhieri G, Boggi U, Campani D, Tosetti M, Crocetti L. Ex-vivo human pancreatic specimen evaluation by 7 Tesla MRI: a prospective radiological-pathological correlation study. Radiol Med 2022; 127:950-959. [DOI: 10.1007/s11547-022-01533-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 07/25/2022] [Indexed: 10/15/2022]
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10
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Yang H, Ge X, Zheng X, Li X, Li J, Liu M, Zhu J, Qin J. Predicting Grade of Esophageal Squamous Carcinoma: Can Stretched Exponential Model-Based DWI Perform Better Than Bi-Exponential and Mono-Exponential Model? Front Oncol 2022; 12:904625. [PMID: 35912203 PMCID: PMC9329622 DOI: 10.3389/fonc.2022.904625] [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: 04/20/2022] [Accepted: 06/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background To evaluate and compare the potential performance of various diffusion parameters obtained from mono-exponential model (MEM)-, bi-exponential model (BEM)-, and stretched exponential model (SEM)-based diffusion-weighted imaging (DWI) in grading of esophageal squamous carcinoma (ESC). Methods Eighty-two patients with pathologically confirmed ESC without treatment underwent multi-b-value DWI scan with 13 b values (0~12,00 s/mm2). The apparent diffusion coefficient (ADC) deriving from the MEM; the pure molecular diffusion (ADCslow), pseudo-diffusion coefficient (ADCfast), perfusion, and fraction (f) deriving from the BEM; and the distributed diffusion coefficient (DDC) and water molecular diffusion heterogeneity index (α) deriving from the SEM were calculated and compared between poorly differentiated and well/moderately differentiated ESC, respectively. The prediction parameters and diagnostic efficiency were compared by drawing receiver operating characteristic (ROC) curves. Results The ADC, ADCslow, ADCfast, and DDC in poorly ESC were significantly lower than those in well/moderately differentiated ones. By using only one parameter, ADCslow, DDC had the moderate diagnostic efficiency and the areas under the curve (AUC) were 0.758 and 0.813 in differentiating ESC. The DDC had the maximum AUC with sensitivity (88.00%) and specificity (68.42%). Combining ADC with ADCfast, ADCslow, and DDC and combining ADCslow with ADCfast can provide a higher diagnostic accuracy with AUC ranging from 0.756, 0.771, 0.816, and 0.793, respectively. Conclusion Various parameters derived from different DWI models including MEM, BEM, and SEM were potentially helpful in grading ESC. DDC obtained from SEM was the most promising diffusion parameter for predicting the grade of ESC.
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Affiliation(s)
- Hui Yang
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Xubo Ge
- Department of Radiology, The Fourth People’s Hospital of Taian, Tai’an, China
| | - Xiuzhu Zheng
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Xiaoqian Li
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Jiang Li
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Min Liu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Jianzhong Zhu
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
| | - Jian Qin
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Tai’an, China
- *Correspondence: Jian Qin,
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11
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Ha J, Choi SH, Kim KW, Kim JH, Kim HJ. MRI features for differentiation of autoimmune pancreatitis from pancreatic ductal adenocarcinoma: A systematic review and meta-analysis. Dig Liver Dis 2022; 54:849-856. [PMID: 34903501 DOI: 10.1016/j.dld.2021.11.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 11/12/2021] [Accepted: 11/15/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND AND AIMS The accurate differential diagnosis between autoimmune pancreatitis (AIP) and pancreatic ductal adenocarcinoma (PDAC) is clinically important. We aimed to determine significant MRI features for differentiating AIP from PDAC, including assessment of diffusion-weighted imaging (DWI). METHODS We performed a systematic search using three databases. The pooled diagnostic odds ratio was calculated using a bivariate random effects model to determine significant MRI features for differentiating AIP from PDAC. The pooled sensitivity and specificity were calculated. The qualitative systematic review for DWI assessment was performed. RESULTS Of nine studies (775 patients), multiple main pancreatic duct (MPD) strictures, absence of upstream marked MPD dilatation, peripancreatic rim, and duct penetration sign were significant MRI features for differentiating AIP from PDAC. Absence of MPD dilatation had the highest pooled sensitivity (87%, 95% CI=68-96%), whereas peripancreatic rim had the highest pooled specificity (100%, 95% CI=88-100%). Of 12 studies evaluating DWI, seven reported statistically significant differences in apparent diffusion coefficient (ADC) values between AIP and PDAC; however, four reported lower ADC values in AIP than in PDAC, but three reported the opposite result. CONCLUSION The four significant MRI features can be useful to differentiate AIP from PDAC, but DWI assessment might be limited.
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Affiliation(s)
- Jiyeon Ha
- Department of Radiology, Kangdong Seong-Sim Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sang Hyun Choi
- Department of Radiology and the Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Korea.
| | - Kyung Won Kim
- Department of Radiology and the Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Korea
| | - Jin Hee Kim
- Department of Radiology and the Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Korea
| | - Hyoung Jung Kim
- Department of Radiology and the Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Korea
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Farr KP, Moses D, Haghighi KS, Phillips PA, Hillenbrand CM, Chua BH. Imaging Modalities for Early Detection of Pancreatic Cancer: Current State and Future Research Opportunities. Cancers (Basel) 2022; 14:cancers14102539. [PMID: 35626142 PMCID: PMC9139708 DOI: 10.3390/cancers14102539] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 05/18/2022] [Accepted: 05/19/2022] [Indexed: 02/06/2023] Open
Abstract
Simple Summary While survival rates for many cancers have improved dramatically over the last 20 years, patients with pancreatic cancer have persistently poor outcomes. The majority of patients with pancreatic cancer are not suitable for potentially curative surgery due to locally advanced or metastatic disease stage at diagnosis. Therefore, early detection would potentially improve survival of pancreatic cancer patients through earlier intervention. Here, we present clinical challenges in the early detection of pancreatic cancer, characterise high risk groups for pancreatic cancer and current screening programs in high-risk individuals. The aim of this scoping review is to investigate the role of both established and novel imaging modalities for early detection of pancreatic cancer. Furthermore, we investigate innovative imaging techniques for early detection of pancreatic cancer, but its widespread application requires further investigation and potentially a combination with other non-invasive biomarkers. Abstract Pancreatic cancer, one of the most lethal malignancies, is increasing in incidence. While survival rates for many cancers have improved dramatically over the last 20 years, people with pancreatic cancer have persistently poor outcomes. Potential cure for pancreatic cancer involves surgical resection and adjuvant therapy. However, approximately 85% of patients diagnosed with pancreatic cancer are not suitable for potentially curative therapy due to locally advanced or metastatic disease stage. Because of this stark survival contrast, any improvement in early detection would likely significantly improve survival of patients with pancreatic cancer through earlier intervention. This comprehensive scoping review describes the current evidence on groups at high risk for developing pancreatic cancer, including individuals with inherited predisposition, pancreatic cystic lesions, diabetes, and pancreatitis. We review the current roles of imaging modalities focusing on early detection of pancreatic cancer. Additionally, we propose the use of advanced imaging modalities to identify early, potentially curable pancreatic cancer in high-risk cohorts. We discuss innovative imaging techniques for early detection of pancreatic cancer, but its widespread application requires further investigation and potentially a combination with other non-invasive biomarkers.
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Affiliation(s)
- Katherina P. Farr
- School of Clinical Medicine, Faculty of Medicine & Health, UNSW, Sydney, NSW 2052, Australia; (K.S.H.); (B.H.C.)
- Correspondence:
| | - Daniel Moses
- Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia;
| | - Koroush S. Haghighi
- School of Clinical Medicine, Faculty of Medicine & Health, UNSW, Sydney, NSW 2052, Australia; (K.S.H.); (B.H.C.)
- Department of General Surgery, Prince of Wales Hospital, Sydney, NSW 2052, Australia
| | - Phoebe A. Phillips
- Pancreatic Cancer Translational Research Group, School of Clinical Medicine, Lowy Cancer Research Centre, UNSW, Sydney, NSW 2052, Australia;
| | - Claudia M. Hillenbrand
- Research Imaging NSW, Division of Research & Enterprise, UNSW, Sydney, NSW 2052, Australia;
| | - Boon H. Chua
- School of Clinical Medicine, Faculty of Medicine & Health, UNSW, Sydney, NSW 2052, Australia; (K.S.H.); (B.H.C.)
- Nelune Comprehensive Cancer Centre, Prince of Wales Hospital, Sydney, NSW 2052, Australia
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Zeng P, Ma L, Liu J, Song Z, Liu J, Yuan H. The diagnostic value of intravoxel incoherent motion diffusion-weighted imaging for distinguishing nonhypervascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. Eur J Radiol 2022; 150:110261. [PMID: 35316674 DOI: 10.1016/j.ejrad.2022.110261] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 02/19/2022] [Accepted: 03/14/2022] [Indexed: 12/12/2022]
Abstract
PURPOSE To primarily evaluate the diagnostic performance of the monoexponential and intravoxel incoherent motion (IVIM) diffusion weighted imaging (DWI) models for differentiating between nonhypervascular pancreatic neuroendocrine tumors (PNETs) and pancreatic ductal adenocarcinomas (PDACs). METHODS 63 patients with PNETs (35 nonhypervascular PNETs and 28 hypervascular PNETs) and 164 patients with PDACs were retrospectively enrolled in the study and underwent multiple b-value DWI. Intraobserver and interobserver reliabilities of DWI parameters were assessed by using the intraclass correlation coefficient (ICC). The parameters of apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) of nonhypervascular PNETs were compared with PDACs and hypervascular PNETs using the independent sample t test or the Mann-Whitney U test. The diagnostic performance was evaluated using receiver operating characteristic (ROC) analysis. RESULTS All DWI parameters values showed good to excellent intra- and interobserver agreements (ICC = 0.743-0.873). Nonhypervascular PNETs had significantly lower ADC and D, but significantly higher f than PDACs (P = 0.005, P < 0.001 and P < 0.001, respectively). ADC, D and f of nonhypervascular PNETs were lower than hypervascular PNETs (P = 0.001, <0.001 and 0.093, respectively). D* of nonhypervascular PNETs showed no statistically significant differences with PDACs and hypervascular PNETs (P = 0.809 and 0.420). D showed a higher area under the curve (AUC), followed by ADC and f (AUC = 0.885, 0.665 and 0.740, respectively) in differentiating nonhypervascular PNETs from PDACs. CONCLUSION Monoexponential and IVIM diffusion models are valuable to differentiate nonhypervascular PNETs from PDACs. D showed better performance than f and ADC.
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Affiliation(s)
- Piaoe Zeng
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Lu Ma
- Department of Radiology, Tsinghua University Hospital, 30 Shuangqing Road, Beijing 100084, Beijing, China
| | - Jianfang Liu
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Zixiu Song
- Department of Pathology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Jianyu Liu
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District, Beijing 100191, Beijing, China.
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Liu J, Hu L, Zhou B, Wu C, Cheng Y. Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis. Transl Oncol 2022; 18:101357. [PMID: 35114568 PMCID: PMC8818577 DOI: 10.1016/j.tranon.2022.101357] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Revised: 12/14/2021] [Accepted: 01/24/2022] [Indexed: 12/12/2022] Open
Abstract
A novel model incorporating multiparametric MRI-based radiomic signature with clinically independent risk factors can greatly improve the non-invasive diagnostic accuracy in differentiating PC from MFCP. The nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. The mixed model may aid in formulating treatment strategies and help to avoid unnecessary surgical operations for doctors.
Purpose It is difficult to make a clear differential diagnosis of pancreatic carcinoma (PC) and mass-forming chronic pancreatitis (MFCP) via conventional examinations. We aimed to develop a novel model incorporating an MRI-based radiomics signature with clinical biomarkers for distinguishing the two lesions. Methods A total of 102 patients were retrospectively enrolled and randomly divided into the training and validation cohorts. Radiomics features were extracted from four different sequences. Individual imaging modality radiomics signature, multiparametric MRI (mp-MRI) radiomics signature, and a final mixed model based on mp-MRI and clinically independent risk factors were established to discriminate between PC and MFCP. The diagnostic performance of each model and model discrimination were assessed in both the training and validation cohorts. Results ADC had the best predictive performance among the four individual radiomics models, but there were no significant differences between the pairs of models (all p > 0.05). Six potential radiomics features were finally selected from the 960 texture features to formulate the radiomics score (rad-score) of the mp-MRI model. In addition, the boxplot results of the distributions of rad-scores identified the rad-score as an independent predictive factor for the differentiation of PC and MFCP (p< 0.001). Notably, the nomogram integrating rad-score and clinically independent risk factors had a better diagnostic performance than the mp-MRI and clinical models. These results were further confirmed by the validation group. Conclusion The mixed model was developed and preliminarily validated to distinguish PC from MFCP, which may benefit the formulation of treatment strategies and nonsurgical procedures.
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Affiliation(s)
- Jingjing Liu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, People's Republic of China
| | - Lei Hu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, People's Republic of China
| | - Bi Zhou
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, People's Republic of China.
| | - Chungen Wu
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, People's Republic of China
| | - Yingsheng Cheng
- Department of Radiology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, People's Republic of China
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Ren S, Tang HJ, Zhao R, Duan SF, Chen R, Wang ZQ. Application of Unenhanced Computed Tomography Texture Analysis to Differentiate Pancreatic Adenosquamous Carcinoma from Pancreatic Ductal Adenocarcinoma. Curr Med Sci 2022; 42:217-225. [PMID: 35089491 DOI: 10.1007/s11596-022-2535-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 06/28/2021] [Indexed: 12/16/2022]
Abstract
OBJECTIVE The objective of this study was to investigate the application of unenhanced computed tomography (CT) texture analysis in differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC). METHODS Preoperative CT images of 112 patients (31 with PASC, 81 with PDAC) were retrospectively reviewed. A total of 396 texture parameters were extracted from AnalysisKit software for further texture analysis. Texture features were selected for the differentiation of PASC and PDAC by the Mann-Whitney U test, univariate logistic regression analysis, and the minimum redundancy maximum relevance algorithm. Furthermore, receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the texture feature-based model by the random forest (RF) method. Finally, the robustness and reproducibility of the predictive model were assessed by the 10-times leave-group-out cross-validation (LGOCV) method. RESULTS In the present study, 10 texture features to differentiate PASC from PDAC were eventually retained for RF model construction after feature selection. The predictive model had a good classification performance in differentiating PASC from PDAC, with the following characteristics: sensitivity, 95.7%; specificity, 92.5%; accuracy, 94.3%; positive predictive value (PPV), 94.3%; negative predictive value (NPV), 94.3%; and area under the ROC curve (AUC), 0.98. Moreover, the predictive model was proved to be robust and reproducible using the 10-times LGOCV algorithm (sensitivity, 90.0%; specificity, 71.3%; accuracy, 76.8%; PPV, 59.0%; NPV, 95.2%; and AUC, 0.80). CONCLUSION The unenhanced CT texture analysis has great potential for differentiating PASC from PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA.
| | - Hui-Juan Tang
- Department of Oncology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
- Department of Clinical and Molecular Sciences, Marche Polytechnic University, Ancona, 60126, Italy
| | - Rui Zhao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China
| | | | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, School of Medicine, University of Maryland, Baltimore, MD, 21201, USA
| | - Zhong-Qiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, China.
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Effects of different breathing techniques on the IVIM-derived quantitative parameters of the normal pancreas. Eur J Radiol 2021; 143:109892. [PMID: 34388419 DOI: 10.1016/j.ejrad.2021.109892] [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: 04/01/2021] [Revised: 07/29/2021] [Accepted: 07/30/2021] [Indexed: 11/21/2022]
Abstract
PURPOSE To prospectively compare the differences in intravoxel incoherent motion (IVIM)-derived quantitative parameters in different anatomic locations of the normal pancreas with different breathing techniques in a healthy population. METHOD Twenty-six volunteers successfully underwent pancreas axial IVIM imaging with a 3.0-T MR system using 11 b-values (from 0 to 1000 sec/mm2) with three different breathing techniques: free breath (FB), liver dome scout (LDS), and phase scout (PS). The IVIM-derived quantitative parameters in three anatomic locations (head, body, and tail of the pancreas) were calculated. The intra-, inter-, and short-term consistency of IVIM-derived quantitative parameters were assessed by comparing 95% confidence interval (CI) of limits of agreement (LOA) of difference between measurements and clinical maximum allowed difference using the Bland-Altman method. The Kruskal-Wallis test was used to compare pancreatic IVIM-derived parameters. RESULTS In Bland-Altman graph, the maximum values of the 95% CIs of LOAs of Dslow, Dfast, and f were (0.123 ± 0.022) × 10-3 mm2/sec, (22.093 ± 4.997) × 10-3 mm2/sec, and (3.942 ± 0.621)%, and the consistency of Dslow and f was good and that of Dfast was poor overall. The Dslow, Dfast, and f values of normal pancreas were (1.056 ± 0.121) × 10-3 mm2/sec, (55.755 ± 13.011) × 10-3 mm2/sec, and (26.036 ± 2.361)%, respectively, and there aren't any breathing technique (P > 0.05) or location (P > 0.05) dependent differences. CONCLUSIONS Our study shows that IVIM-derived quantitative parameters of the pancreas may not be affected by breathing techniques and anatomic locations. The f and Dslow values have good repeated measurement consistency under different breathing techniques.
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Shi YJ, Li XT, Zhang XY, Zhu HT, Liu YL, Wei YY, Sun YS. Non-gaussian models of 3-Tesla diffusion-weighted MRI for the differentiation of pancreatic ductal adenocarcinomas from neuroendocrine tumors and solid pseudopapillary neoplasms. Magn Reson Imaging 2021; 83:68-76. [PMID: 34314825 DOI: 10.1016/j.mri.2021.07.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/23/2021] [Accepted: 07/20/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE To assess the MRI performance in differentiating pancreatic ductal adenocarcinomas (PDACs), from solid pseudopapillary neoplasms (SPNs) and pancreatic neuroendocrine tumors (PNETs) using non-gaussian diffusion-weighted imaging models. METHODS This was a retrospective study of patients diagnosed with PDACs (01/2015-06/2019) or with PNETs or SPNs diagnosed (01/2011-12/2019) at our hospital. The lesions were randomized 1:1 to the primary and validation cohorts. The regions of interest (ROIs) were manually drawn on each slice at DWI (b = 1500 s/mm2) from 3 T MRI. D (diffusion coefficient), D* (pseudodiffusion coefficient), f (perfusion fraction), distributed diffusion coefficient (DDC), α (diffusion heterogeneity index), mean diffusivity (MD) and mean kurtosis (MK) were obtained. The parameters with largest performance for differentiation were used to establish a diagnostic model. RESULTS There were 148, 56, and 60 patients with PDAC, PNET, and SPN, respectively. For differentiating PDACs from SPNs, f and MK values were used to establish a diagnostic model with areas under the receiver operating characteristic curves (AUCs) of 0.92 and 0.89 in the primary and validation groups, respectively. For distinguishing PDACs from PNETs, α and MK values were used to establish a diagnostic model with AUCs of 0.87 and 0.86 in the primary and validation groups, respectively. The accuracy rate of the subjective evaluation with the assistance of non-gaussian DWI models for differentiating PDAC from SPNs and PNETs were higher than that of subjective diagnosis alone (P < 0.05). CONCLUSIONS The non-gaussian DWI models could assist radiologists in accurately differentiating PDACs from PNETs and SPNs.
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Affiliation(s)
- Yan-Jie Shi
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Xiao-Ting Li
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Xiao-Yan Zhang
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Hai-Tao Zhu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Yu-Liang Liu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Yi-Yuan Wei
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China
| | - Ying-Shi Sun
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Radiology, Peking University Cancer Hospital & Institute, No.52 Fu Cheng Road, Hai Dian District, Beijing 100142, China.
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Xu W, Zhang H, Feng G, Zheng Q, Shang R, Liu X. The value of MRI in identifying pancreatic neuroendocrine tumour G3 and carcinoma G3. Clin Radiol 2021; 76:551.e1-551.e9. [PMID: 33902887 DOI: 10.1016/j.crad.2021.02.031] [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: 09/26/2020] [Accepted: 02/11/2021] [Indexed: 11/17/2022]
Abstract
AIM To explore the magnetic resonance imaging (MRI) differences between pancreatic neuroendocrine tumour grade 3 (pNET-G3) and pancreatic neuroendocrine carcinoma grade 3 (pNEC-G3). MATERIALS AND METHODS Between 2009 and 2019, 31 patients underwent pNEN-G3 resection with preoperative MRI in two local hospitals in China. The 31 patients were assigned to a pNET-G3 group (n=13) or a pNEC-G3 group (n=18). The MRI findings between the groups were compared. RESULTS There was no statistically significant difference between the two groups in lesion size, clinical characteristics, or laboratory indexes. The lesions showed high or slightly higher signal on diffusion-weighted imaging and decreased apparent diffusion coefficient (ADC) values, which differed between the two groups (p=0.013). The difference between the groups regarding positive enhancement integral, arterial phase and portal phase signal enhancement ratio were statistically significant; however, the delayed phase signal enhancement ratio was not significantly different. CONCLUSIONS pNET-G3 and pNEC-G3 showed different characteristics on MRI. In particular, the ADC value and dynamic enhanced imaging could have an important role in distinguishing between the two.
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Affiliation(s)
- W Xu
- Department of Radiology, Dezhou People's Hospital, 1166 Dong Fang Hong West Road, Dezhou, Shandong 253000, China
| | - H Zhang
- Department of Radiology, Dezhou People's Hospital, 1166 Dong Fang Hong West Road, Dezhou, Shandong 253000, China
| | - G Feng
- Department of Radiology, Yucheng People's Hospital, 753 Pioneer Road, Yucheng, Shandong 251200, China
| | - Q Zheng
- Department of Radiology, Dezhou People's Hospital, 1166 Dong Fang Hong West Road, Dezhou, Shandong 253000, China
| | - R Shang
- Department of Radiology, Dezhou People's Hospital, 1166 Dong Fang Hong West Road, Dezhou, Shandong 253000, China
| | - X Liu
- Department of Pharmacy, Dezhou People's Hospital, 1166 Dong Fang Hong West Road, Dezhou, Shandong 253000, China.
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Accuracy of quantitative diffusion-weighted imaging for differentiating benign and malignant pancreatic lesions: a systematic review and meta-analysis. Eur Radiol 2021; 31:7746-7759. [PMID: 33847811 DOI: 10.1007/s00330-021-07880-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 02/19/2021] [Accepted: 03/12/2021] [Indexed: 12/16/2022]
Abstract
BACKGROUND A variety of imaging techniques can be used to evaluate diffusion characteristics to differentiate malignant and benign pancreatic lesions. The diagnostic performance of diffusion parameters has not been systematic assessed. PURPOSE We aimed to investigate the diagnostic efficacy of quantitative diffusion-weighted imaging (DWI) for pancreatic lesions. METHODS A literature search was conducted using the PubMed, Embase, and Cochrane Library databases for studies from inception to March 30, 2020, which involves the quantitative diagnostic performance of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) in the pancreas. Studies were reviewed according to inclusion and exclusion criteria. The quality of articles was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 (QUATAS-2). A bivariate random-effects model was used to evaluate pooled sensitivities and specificities. Univariable meta-regression analysis was used to test the effects of factors that contributed to the heterogeneity. RESULTS A total of 31 studies involving 1558 patients were ultimately eligible for data extraction. The lowest heterogeneity was found in specificity of perfusion fraction (f) with the I2 value was 17.97% and Cochran p value was 0.28. However, high heterogeneities were found for the other parameters (all I2 > 50%). There was no publication bias found in funnel plot (p = 0.30) for the apparent diffusion coefficient (ADC) parameter. The pooled sensitivities for ADC, f, pure diffusion coefficient (D), and pseudo diffusivity coefficient (D*) were 83%, 81%, 76%, and 84%, respectively. The pooled specificities for ADC, f, D, and D* were 87%, 83%, 69%, and 81% respectively. The areas under the curves for ADC, f, D, and D* were 0.92, 0.87, 0.79, and 0.87 respectively. CONCLUSION Quantitative DWI and IVIM have a good diagnostic performance for differentiating malignant and benign pancreatic lesions. KEY POINTS • IVIM has high sensitivity and specificity (84% and 83%, respectively) for differential diagnosis of pancreatic lesions, which is comparable to that of the ADC (83% and 87%, respectively). • The ADC has an excellent diagnostic performance for differentiating malignant from benign IPMNs (sensitivity, 0.83; specificity, 0.92); the f has the best diagnostic performance for differentiating pancreatic carcinoma from PNET (sensitivity, 0.85; specificity, 0.85). • For the ADC, using a maximal b value < 800 s/mm2 has a higher diagnostic accuracy than ≥ 800 s/mm2; performing in a high field strength (3.0 T) system has a higher diagnostic accuracy than a low field strength (1.5 T) for pancreatic lesions.
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Effective apparent diffusion coefficient parameters for differentiation between mass-forming autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2021; 46:1640-1647. [PMID: 33037891 DOI: 10.1007/s00261-020-02795-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 09/14/2020] [Accepted: 09/27/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To evaluate the diagnostic performance of apparent diffusion coefficient (ADC) parameters by region of interest (ROI) methods in differentiating mass-forming autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). METHODS The institutional review board approved this retrospective study and the requirement for informed consent was waived. Twenty-three patients with mass-forming AIP and 144 patients with PDAC underwent diffusion-weighted imaging with b-values of 0 s/mm2 and 800 s/mm2. The minimum, maximum, and mean ADC values obtained by placing ROIs within lesions and percentile ADC values (10th, 25th, 50th, 75th, and 90th) from entire-lesion histogram analysis were compared between the two groups by using Mann-Whitney U tests. The diagnostic performance was evaluated by receiver operating characteristic (ROC) curve analysis. RESULTS The minimum, maximum, and mean ADC values were significantly different between mass-forming AIP and PDAC groups. ROC curve analysis showed that the maximum ADC had the highest diagnostic performance (0.92), while the minimum ADC value had the lowest diagnostic performance (0.72). The AUC of minimum ADC was significantly lower than that of maximum or mean ADC (P < 0.0001, P < 0.0001). The AUC was lowest in 10th percentile ADC value and highest in 90th percentile value. The AUC increased along with the increase of percentile values. CONCLUSION Either the maximum or mean ADC value was effective in differentiating mass-forming AIP from the PDAC group, while the minimum ADC value might not be recommended.
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Chen J, Liu S, Tang Y, Zhang X, Cao M, Xiao Z, Ren M, Chen X. Diagnostic performance of diffusion MRI for pancreatic ductal adenocarcinoma characterisation: A meta-analysis. Eur J Radiol 2021; 139:109672. [PMID: 33819806 DOI: 10.1016/j.ejrad.2021.109672] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2021] [Revised: 03/13/2021] [Accepted: 03/18/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To assess the diagnostic performance of intravoxel incoherent motion (IVIM) and diffusion-weighted imaging (DWI) for characterising pancreatic ductal adenocarcinoma (PDAC). METHOD A literature search was performed through PubMed, Web of Science, the Cochrane Library, and Embase databases. The search date was updated to extend until 28 October 2020, with no starting time limitation. The pooled sensitivity and specificity were calculated using a bivariate random effects model. Summary receiver operating characteristic curves were constructed, and area under the curve (AUC) of each diffusion parameter was calculated. Subgroup and meta-regression analyses were performed to assess for heterogeneity. Study quality was assessed. RESULTS Twenty-nine studies involving 1579 participants were included, of which 26 evaluated the apparent diffusion coefficient (ADC) and eight evaluated IVIM, with five evaluating both ADC and IVIM. Pooled sensitivity and specificity of ADC were 83 % (95 % CI, 76 %-88 %, I2 = 86 %) and 85 % (95 % CI, 79 %-90 %, I2 = 77 %), respectively, and AUC was 0.91 (95 % CI, 0.88-0.93). The perfusion fraction had the highest diagnostic accuracy in the IVIM model; the pooled sensitivity, specificity, and AUC were 87 % (95 % CI, 81 %-92 %, I2 = 45 %), 88 % (95 % CI, 77 %-94 %, I2 = 57 %), and 0.93 (95 % CI, 0.91-0.95), respectively. The pooled sensitivity, specificity and AUC for the tissue diffusion coefficient were 74 % (95 % CI, 55 %-87 %, I2 = 87 %), 69 % (95 % CI, 52 %-82 %, I2 = 73 %), and 0.77 (95 % CI, 0.73-0.81), respectively. And the pooled sensitivity, specificity, and AUC for the pseudodiffusion coefficient were 89 % (95 % CI, 77 %-96 %, I2 = 79 %), 74 % (95 % CI, 60 %-84 %, I2 = 78 %), and 0.88(95 %CI,0.85-0.91), respectively. Meta-regression analyses revealed that study design (specificity, P<0.01), region-of-interest delineation (sensitivity, P = 0.02;specificity, P = 0.03), field strength (sensitivity, P<0.01), and thickness (sensitivity, P<0.01; specificity, P = 0.01) were sources of ADC heterogeneity. CONCLUSIONS DWI and IVIM have comparable diagnostic power and good diagnostic performance for characterising PDAC.
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Affiliation(s)
- Jing Chen
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China.
| | - Shuxue Liu
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China
| | - Yude Tang
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China
| | - Xiongbiao Zhang
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China
| | - Mingming Cao
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China
| | - Zheng Xiao
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China
| | - Mingda Ren
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China
| | - Xianteng Chen
- Department of Radiology, Zhongshan Affiliated Hospital, Guangzhou University of Traditional Chinese Medicine, Zhongshan, 528400, PR China
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Kovač JD, Daković M, Janković A, Mitrović M, Dugalić V, Galun D, Đurić-Stefanović A, Mašulović D. The role of quantitative diffusion-weighted imaging in characterization of hypovascular liver lesions: A prospective comparison of intravoxel incoherent motion derived parameters and apparent diffusion coefficient. PLoS One 2021; 16:e0247301. [PMID: 33606753 PMCID: PMC7894812 DOI: 10.1371/journal.pone.0247301] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 02/04/2021] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The utility of intravoxel incoherent motion (IVIM) related parameters in differentiation of hypovascular liver lesions is still unknown. PURPOSE The purpose of this study was to evaluate the value of IVIM related parameters in comparison to apparent diffusion coefficient (ADC) for differentiation among intrahepatic mass-forming cholangiocarcinoma (IMC), and hypovascular liver metastases (HLM). METHODS Seventy-four prospectively enrolled patients (21 IMC, and 53 HLM) underwent 1.5T magnetic resonance examination with IVIM diffusion-weighted imaging using seven b values (0-800 s/mm2). Two independent readers performed quantitative analysis of IVIM-related parameters and ADC. Interobserver reliability was tested using a intraclass correlation coefficient. ADC, true diffusion coefficient (D), perfusion-related diffusion coefficient (D*), and perfusion fraction (ƒ) were compared among the lesions using Kruskal-Wallis H test. The diagnostic accuracy of each parameter was assessed by receiver operating characteristic (ROC) curve analysis. RESULTS The interobserver agreement was good for ADC (0.802), and excellent for D, D*, and ƒ (0.911, 0.927, and 0.942, respectively). ADC, and D values were significantly different among IMC and HLM (both p < 0.05), while there was no significant difference among these lesions for ƒ and D* (p = 0.101, and p = 0.612, respectively). ROC analysis showed higher diagnostic performance of D in comparison to ADC (AUC = 0.879 vs 0.821). CONCLUSION IVIM-derived parameters in particular D, in addition to ADC, could help in differentiation between most common hypovascular malignant liver lesions, intrahepatic mass-forming cholangiocarcinoma and hypovascular liver metastases.
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Affiliation(s)
- Jelena Djokić Kovač
- Center for Radiology and Magnetic Resonance Imaging, Clinical Center of Serbia, Belgrade, Serbia
- School of Medicine, University of Belgrade, Belgrade, Serbia
- * E-mail:
| | - Marko Daković
- Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia
| | - Aleksandra Janković
- Center for Radiology and Magnetic Resonance Imaging, Clinical Center of Serbia, Belgrade, Serbia
| | - Milica Mitrović
- Center for Radiology and Magnetic Resonance Imaging, Clinical Center of Serbia, Belgrade, Serbia
| | - Vladimir Dugalić
- School of Medicine, University of Belgrade, Belgrade, Serbia
- First Surgical Clinic, Clinical Center of Serbia, Belgrade, Serbia
| | - Daniel Galun
- School of Medicine, University of Belgrade, Belgrade, Serbia
- First Surgical Clinic, Clinical Center of Serbia, Belgrade, Serbia
| | - Aleksandra Đurić-Stefanović
- Center for Radiology and Magnetic Resonance Imaging, Clinical Center of Serbia, Belgrade, Serbia
- School of Medicine, University of Belgrade, Belgrade, Serbia
| | - Dragan Mašulović
- Center for Radiology and Magnetic Resonance Imaging, Clinical Center of Serbia, Belgrade, Serbia
- School of Medicine, University of Belgrade, Belgrade, Serbia
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Intravoxel incoherent motion magnetic resonance imaging: basic principles and clinical applications. Pol J Radiol 2020; 85:e624-e635. [PMID: 33376564 PMCID: PMC7757509 DOI: 10.5114/pjr.2020.101476] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 06/03/2020] [Indexed: 12/26/2022] Open
Abstract
The purpose of this article was to show basic principles, acquisition, advantages, disadvantages, and clinical applications of intravoxel incoherent motion (IVIM) magnetic resonance imaging (MRI). IVIM MRI as a method was introduced in the late 1980s, but recently it started attracting more interest thanks to its applications in many fields, particularly in oncology and neuroradiology. This imaging technique has been developed with the objective of obtaining not only a functional analysis of different organs but also different types of lesions. Among many accessible tools in diagnostic imaging, IVIM MRI aroused the interest of many researchers in terms of studying its applicability in the evaluation of abdominal organs and diseases. The major conclusion of this article is that IVIM MRI seems to be a very auspicious method to investigate the human body, and that nowadays the most promising clinical application for IVIM perfusion MRI is oncology. However, due to lack of standardisation of image acquisition and analysis, further studies are needed to validate this method in clinical practice.
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24
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Schima W, Böhm G, Rösch CS, Klaus A, Függer R, Kopf H. Mass-forming pancreatitis versus pancreatic ductal adenocarcinoma: CT and MR imaging for differentiation. Cancer Imaging 2020; 20:52. [PMID: 32703312 PMCID: PMC7376657 DOI: 10.1186/s40644-020-00324-z] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 06/29/2020] [Indexed: 02/06/2023] Open
Abstract
Various inflammatory abnormalities of the pancreas can mimic pancreatic ductal adenocarcinoma (PDAC) at cross-sectional imaging. Misdiagnosis of PDAC at imaging may lead to unnecessary surgery. On the other hand, chronic pancreatitis (CP) bears a greater risk of developing PDAC during the course of the disease. Thus, differentiation between mass-forming chronic pancreatitis (MFCP) and PDAC is important to avoid unnecessary surgery and not to delay surgery of synchronous PDAC in CP. Imaging features such as the morphology of the mass including displacement of calcifications, presence of duct penetrating, sign appearance of duct stricturing, presence or absence of vessel encasement, apparent diffusion coefficient (ADC) value and intravoxel incoherent motion (IVIM) at diffusion-weighted imaging (DWI), fluorodeoxyglucose (FDG) uptake in PET/CT, and mass perfusion parameters can help to differentiate between PDAC and MFCP. Correct interpretation of imaging features can appropriately guide biopsy and surgery, if necessary. This review summarizes the relevant computed tomography (CT) and magnetic resonance imaging (MRI) features that can help the radiologist to come to a confident diagnosis and to guide further management in equivocal cases.
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Affiliation(s)
- Wolfgang Schima
- Department of Diagnostic and Interventional Radiology, Goettlicher Heiland Krankenhaus, Barmherzige Schwestern Krankenhaus, 1170 Wien, Dornbacher Strasse 20-30, St. Josef-Krankenhaus, Vienna, Austria.
| | - Gernot Böhm
- Department of Radiology, Ordensklinikum, Linz, Austria
| | | | - Alexander Klaus
- Department of Surgery, Barmherzige Schwestern Krankenhaus, Vienna, Austria
| | | | - Helmut Kopf
- Department of Diagnostic and Interventional Radiology, Goettlicher Heiland Krankenhaus, Barmherzige Schwestern Krankenhaus, 1170 Wien, Dornbacher Strasse 20-30, St. Josef-Krankenhaus, Vienna, Austria
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25
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Ren S, Zhao R, Zhang J, Guo K, Gu X, Duan S, Wang Z, Chen R. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma. Abdom Radiol (NY) 2020; 45:1524-1533. [PMID: 32279101 DOI: 10.1007/s00261-020-02506-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
PURPOSE To investigate the value of texture analysis on unenhanced computed tomography (CT) to potentially differentiate mass-forming pancreatitis (MFP) from pancreatic ductal adenocarcinoma (PDAC). METHODS A retrospective study consisting of 109 patients (30 MFP patients vs 79 PDAC patients) who underwent preoperative unenhanced CT between January 2012 and December 2017 was performed. Synthetic minority oversampling technique (SMOTE) algorithm was adopted to reconstruct and balance MFP and PDAC samples. A total of 396 radiomic features were extracted from unenhanced CT images. Mann-Whitney U test and minimum redundancy maximum relevance (MRMR) methods were used for the purpose of dimension reduction. Predictive models were constructed using random forest (RF) method, and were validated using leave group out cross-validation (LGOCV) method. Diagnostic performance of the predictive model, including sensitivity, specificity, accuracy, positive predicting value (PPV), and negative predicting value (NPV), was recorded. RESULTS We applied 200% of SMOTE to MFP and PDAC patients, resulting in 90 MFP patients compared with 120 PDAC patients. Dimension reduction steps yielded 30 radiomic features using Mann-Whitney U test and MRMR methods. Ten radiomic features were retained using RF method. Four most predictive parameters, including GreyLevelNonuniformity_angle90_offset1, VoxelValueSum, HaraVariance, and ClusterProminence_AllDirection_offset1_SD, were used to generate the predictive model with preferable 92.2% sensitivity, 94.2% specificity, 93.3% accuracy, 92.2% PPV, and 94.2% NPV. Finally, in LGOCV analysis, a high pooled mean sensitivity, specificity, and accuracy (82.6%, 80.8%, and 82.1%, respectively) indicate a relatively reliable and stable predictive model. CONCLUSIONS Unenhanced CT texture analysis can be a promising noninvasive method in discriminating MFP from PDAC.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
- The First Clinical Medical College, Nanjing University of Chinese Medicine, Nanjing, 210023, Jiangsu Province, China
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Rui Zhao
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Jingjing Zhang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Kai Guo
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | - Xiaoyu Gu
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China
| | | | - Zhongqiu Wang
- Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Nanjing, 210029, Jiangsu Province, China.
| | - Rong Chen
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
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26
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Harrington KA, Shukla-Dave A, Paudyal R, Do RKG. MRI of the Pancreas. J Magn Reson Imaging 2020; 53:347-359. [PMID: 32302044 DOI: 10.1002/jmri.27148] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 02/06/2023] Open
Abstract
MRI has played a critical role in the evaluation of patients with pancreatic pathologies, from screening of patients at high risk for pancreatic cancer to the evaluation of pancreatic cysts and indeterminate pancreatic lesions. The high mortality associated with pancreatic adenocarcinomas has spurred much interest in developing effective screening tools, with MRI using magnetic resonance cholangiopancreatography (MRCP) playing a central role in the hopes of identifying cancers at earlier stages amenable to curative resection. Ongoing efforts to improve the resolution and robustness of imaging of the pancreas using MRI may thus one day reduce the mortality of this deadly disease. However, the increasing use of cross-sectional imaging has also generated a concomitant clinical conundrum: How to manage incidental pancreatic cystic lesions that are found in over a quarter of patients who undergo MRCP. Efforts to improve the specificity of MRCP for patients with pancreatic cysts and with indeterminate pancreatic masses may be achieved with continued technical advances in MRI, including diffusion-weighted and T1 -weighted dynamic contrast-enhanced MRI. However, developments in quantitative MRI of the pancreas remain challenging, due to the small size of the pancreas and its upper abdominal location, adjacent to bowel and below the diaphragm. Further research is needed to improve MRI of the pancreas as a clinical tool, to positively affect the lives of patients with pancreatic abnormalities. This review focuses on various MR techniques such as MRCP, quantitative imaging, and dynamic contrast-enhanced imaging and their clinical applicability in the imaging of the pancreas, with an emphasis on pancreatic malignant and premalignant lesions. Level of Evidence 5 Technical Efficacy Stage 3 J. MAGN. RESON. IMAGING 2021;53:347-359.
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Affiliation(s)
- Kate A Harrington
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Amita Shukla-Dave
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ramesh Paudyal
- Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Richard K G Do
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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27
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Kaissis G, Braren R. Pancreatic cancer detection and characterization-state of the art cross-sectional imaging and imaging data analysis. Transl Gastroenterol Hepatol 2019; 4:35. [PMID: 31231702 DOI: 10.21037/tgh.2019.05.04] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 05/07/2019] [Indexed: 12/12/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) represents a deadly disease, prognosticated to become the 2nd most common cause of cancer related death in the western world by 2030. State of the art radiologic high-resolution cross-sectional imaging by computed tomography (CT) and magnetic resonance imaging (MRI) represent advanced techniques for early lesion detection, pre-therapeutic patient staging and therapy response monitoring. In light of molecular taxonomies currently under development, the implementation of advanced imaging data post-processing pipelines and the integration of imaging and clinical data for the development of risk assessment and clinical decision support tools are required. This review will present the current state of cross-sectional radiologic imaging and image post-processing related to PDAC.
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Affiliation(s)
- Georgios Kaissis
- Institute of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Translational Oncology and Quantitative Imaging/Data Science Laboratory, Munich, Germany
| | - Rickmer Braren
- Institute of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich, Translational Oncology and Quantitative Imaging/Data Science Laboratory, Munich, Germany
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