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Park YJ, Park YS, Kim ST, Hyun SH. A Machine Learning Approach Using [ 18F]FDG PET-Based Radiomics for Prediction of Tumor Grade and Prognosis in Pancreatic Neuroendocrine Tumor. Mol Imaging Biol 2023; 25:897-910. [PMID: 37395887 DOI: 10.1007/s11307-023-01832-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 05/30/2023] [Accepted: 06/19/2023] [Indexed: 07/04/2023]
Abstract
PURPOSE We sought to develop and validate machine learning (ML) models for predicting tumor grade and prognosis using 2-[18F]fluoro-2-deoxy-D-glucose ([18F]FDG) positron emission tomography (PET)-based radiomics and clinical features in patients with pancreatic neuroendocrine tumors (PNETs). PROCEDURES A total of 58 patients with PNETs who underwent pretherapeutic [18F]FDG PET/computed tomography (CT) were retrospectively enrolled. PET-based radiomics extracted from segmented tumor and clinical features were selected to develop prediction models by the least absolute shrinkage and selection operator feature selection method. The predictive performances of ML models using neural network (NN) and random forest algorithms were compared by the areas under the receiver operating characteristic curves (AUROCs) and validated by stratified five-fold cross validation. RESULTS We developed two separate ML models for predicting high-grade tumors (Grade 3) and tumors with poor prognosis (disease progression within two years). The integrated models consisting of clinical and radiomic features with NN algorithm showed the best performances than the other models (stand-alone clinical or radiomics models). The performance metrics of the integrated model by NN algorithm were AUROC of 0.864 in the tumor grade prediction model and AUROC of 0.830 in the prognosis prediction model. In addition, AUROC of the integrated clinico-radiomics model with NN was significantly higher than that of tumor maximum standardized uptake model in predicting prognosis (P < 0.001). CONCLUSIONS Integration of clinical features and [18F]FDG PET-based radiomics using ML algorithms improved the prediction of high-grade PNET and poor prognosis in a non-invasive manner.
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Affiliation(s)
- Yong-Jin Park
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea
- Department of Nuclear Medicine, Ajou University Medical Center, Ajou University School of Medicine, 164, Worldcup-ro, Yeongtong-gu, Suwon, 16499, South Korea
| | - Young Suk Park
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Tae Kim
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, South Korea
| | - Seung Hyup Hyun
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, South Korea.
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Ren S, Guo K, Li Y, Cao YY, Wang ZQ, Tian Y. Diagnostic accuracy of apparent diffusion coefficient to differentiate intrapancreatic accessory spleen from pancreatic neuroendocrine tumors. World J Gastrointest Oncol 2023; 15:1051-1061. [PMID: 37389113 PMCID: PMC10302999 DOI: 10.4251/wjgo.v15.i6.1051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/01/2023] [Accepted: 04/19/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Intrapancreatic accessory spleen (IPAS) shares similar imaging findings with hypervascular pancreatic neuroendocrine tumors (PNETs), which may lead to unnecessary surgery.
AIM To investigate and compare the diagnostic performance of absolute apparent diffusion coefficient (ADC) and normalized ADC (lesion-to-spleen ADC ratios) in the differential diagnosis of IPAS from PNETs.
METHODS A retrospective study consisting of 29 patients (16 PNET patients vs 13 IPAS patients) who underwent preoperative contrast-enhanced magnetic resonance imaging together with diffusion-weighted imaging/ADC maps between January 2017 and July 2020 was performed. Two independent reviewers measured ADC on all lesions and spleens, and normalized ADC was calculated for further analysis. The receiver operating characteristics analysis was carried out for evaluating the diagnostic performance of both absolute ADC and normalized ADC values in the differential diagnosis between IPAS and PNETs by clarifying sensitivity, specificity, and accuracy. Inter-reader reliability for the two methods was evaluated.
RESULTS IPAS had a significantly lower absolute ADC (0.931 ± 0.773 × 10-3 mm2/s vs 1.254 ± 0.219 × 10-3 mm2/s) and normalized ADC value (1.154 ± 0.167 vs 1.591 ± 0.364) compared to PNET. A cutoff value of 1.046 × 10-3 mm2/s for absolute ADC was associated with 81.25% sensitivity, 100% specificity, and 89.66% accuracy with an area under the curve of 0.94 (95% confidence interval: 0.8536-1.000) for the differential diagnosis of IPAS from PNET. Similarly, a cutoff value of 1.342 for normalized ADC was associated with 81.25% sensitivity, 92.31% specificity, and 86.21% accuracy with an area under the curve of 0.91 (95% confidence interval: 0.8080-1.000) for the differential diagnosis of IPAS from PNET. Both methods showed excellent inter-reader reliability with intraclass correlation coefficients for absolute ADC and ADC ratio being 0.968 and 0.976, respectively.
CONCLUSION Both absolute ADC and normalized ADC values can facilitate the differentiation between IPAS and PNET.
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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, Jiangsu Province, China
| | - Kai Guo
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Yuan Li
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Ying-Ying Cao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Zhong-Qiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Ying Tian
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
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3
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Lu J, Jiang N, Zhang Y, Li D. A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma. Front Oncol 2023; 13:979437. [PMID: 36937433 PMCID: PMC10014827 DOI: 10.3389/fonc.2023.979437] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 02/20/2023] [Indexed: 03/05/2023] Open
Abstract
Objectives The purpose of this study was to develop and validate an CT-based radiomics nomogram for the preoperative differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma. Methods 96 patients with focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma have been enrolled in the study (32 and 64 cases respectively). All cases have been confirmed by imaging, clinical follow-up and/or pathology. The imaging data were considered as: 70% training cohort and 30% test cohort. Pancreatic lesions have been manually delineated by two radiologists and image segmentation was performed to extract radiomic features from the CT images. Independent-sample T tests and LASSO regression were used for feature selection. The training cohort was classified using a variety of machine learning-based classifiers, and 5-fold cross-validation has been performed. The classification performance was evaluated using the test cohort. Multivariate logistic regression analysis was then used to develop a radiomics nomogram model, containing the CT findings and Rad-Score. Calibration curves have been plotted showing the agreement between the predicted and actual probabilities of the radiomics nomogram model. Different patients have been selected to test and evaluate the model prediction process. Finally, receiver operating characteristic curves and decision curves were plotted, and the radiomics nomogram model was compared with a single model to visually assess its diagnostic ability. Results A total of 158 radiomics features were extracted from each image. 7 features were selected to construct the radiomics model, then a variety of classifiers were used for classification and multinomial logistic regression (MLR) was selected to be the optimal classifier. Combining CT findings with radiomics model, a prediction model based on CT findings and radiomics was finally obtained. The nomogram model showed a good sensitivity and specificity with AUCs of 0.87 and 0.83 in training and test cohorts, respectively. The areas under the curve and decision curve analysis showed that the radiomics nomogram model may provide better diagnostic performance than the single model and achieve greater clinical net benefits than the CT finding model and radiomics signature model individually. Conclusions The CT image-based radiomics nomogram model can accurately distinguish between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma patients and provide additional clinical benefits.
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Affiliation(s)
- Jia Lu
- Department of Radiology, The People’s Hospital of China Medical University and The People’s Hospital of Liaoning Province, Shenyang, China
| | - Nannan Jiang
- Department of Radiology, The People’s Hospital of Liaoning Province, Shenyang, China
| | - Yuqing Zhang
- Department of Radiology, The People’s Hospital of China Medical University and The People’s Hospital of Liaoning Province, Shenyang, China
| | - Daowei Li
- Department of Radiology, The People’s Hospital of China Medical University and The People’s Hospital of Liaoning Province, Shenyang, China
- *Correspondence: Daowei Li,
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4
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Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2022; 47:3118-3160. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 07/09/2021] [Accepted: 07/12/2021] [Indexed: 02/07/2023]
Abstract
Radiomics is a newer approach for analyzing radiological images obtained from conventional imaging modalities such as computed tomography, magnetic resonance imaging, endoscopic ultrasonography, and positron emission tomography. Radiomics involves extracting quantitative data from the images and assessing them to identify diagnostic or prognostic features such as tumor grade, resectability, tumor response to neoadjuvant therapy, and survival. The purpose of this review is to discuss the basic principles of radiomics and provide an overview of the current clinical applications of radiomics in the field of pancreatic tumors.
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de la Pinta C. Radiomics in pancreatic cancer for oncologist: Present and future. Hepatobiliary Pancreat Dis Int 2022; 21:356-361. [PMID: 34961674 DOI: 10.1016/j.hbpd.2021.12.006] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 12/07/2021] [Indexed: 02/05/2023]
Abstract
Radiomics is changing the world of medicine and more specifically the world of oncology. Early diagnosis and treatment improve the prognosis of patients with cancer. After treatment, the evaluation of the response will determine future treatments. In oncology, every change in treatment means a loss of therapeutic options and this is key in pancreatic cancer. Radiomics has been developed in oncology in the early diagnosis and differential diagnosis of benign and malignant lesions, in the evaluation of response, in the prediction of possible side effects, marking the risk of recurrence, survival and prognosis of the disease. Some studies have validated its use to differentiate normal tissues from tumor tissues with high sensitivity and specificity, and to differentiate cystic lesions and pancreatic neuroendocrine tumor grades with texture parameters. In addition, these parameters have been related to survival in patients with pancreatic cancer and to response to radiotherapy and chemotherapy. This review aimed to establish the current status of the use of radiomics in pancreatic cancer and future perspectives.
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Affiliation(s)
- Carolina de la Pinta
- Radiation Oncology Department, Ramón y Cajal University Hospital, IRYCIS, Alcalá University, 28034 Madrid, Spain.
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6
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Staal FCR, Aalbersberg EA, van der Velden D, Wilthagen EA, Tesselaar MET, Beets-Tan RGH, Maas M. GEP-NET radiomics: a systematic review and radiomics quality score assessment. Eur Radiol 2022; 32:7278-7294. [PMID: 35882634 DOI: 10.1007/s00330-022-08996-w] [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: 02/21/2022] [Revised: 05/25/2022] [Accepted: 06/26/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVE The number of radiomics studies in gastroenteropancreatic neuroendocrine tumours (GEP-NETs) is rapidly increasing. This systematic review aims to provide an overview of the available evidence of radiomics for clinical outcome measures in GEP-NETs, to understand which applications hold the most promise and which areas lack evidence. METHODS PubMed, Embase, and Wiley/Cochrane Library databases were searched and a forward and backward reference check of the identified studies was executed. Inclusion criteria were (1) patients with GEP-NETs and (2) radiomics analysis on CT, MRI or PET. Two reviewers independently agreed on eligibility and assessed methodological quality with the radiomics quality score (RQS) and extracted outcome data. RESULTS In total, 1364 unique studies were identified and 45 were included for analysis. Most studies focused on GEP-NET grade and differential diagnosis of GEP-NETs from other neoplasms, while only a minority analysed treatment response or long-term outcomes. Several studies were able to predict tumour grade or to differentiate GEP-NETs from other lesions with a good performance (AUCs 0.74-0.96 and AUCs 0.80-0.99, respectively). Only one study developed a model to predict recurrence in pancreas NETs (AUC 0.77). The included studies reached a mean RQS of 18%. CONCLUSION Although radiomics for GEP-NETs is still a relatively new area, some promising models have been developed. Future research should focus on developing robust models for clinically relevant aims such as prediction of response or long-term outcome in GEP-NET, since evidence for these aims is still scarce. KEY POINTS • The majority of radiomics studies in gastroenteropancreatic neuroendocrine tumours is of low quality. • Most evidence for radiomics is available for the identification of tumour grade or differentiation of gastroenteropancreatic neuroendocrine tumours from other neoplasms. • Radiomics for the prediction of response or long-term outcome in gastroenteropancreatic neuroendocrine tumours warrants further research.
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Affiliation(s)
- Femke C R Staal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands
| | - Else A Aalbersberg
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Nuclear Medicine, The Netherlands Cancer Institute Amsterdam, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Daphne van der Velden
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Erica A Wilthagen
- Scientific Information Service, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Margot E T Tesselaar
- The Netherlands Cancer Institute/University Medical Center Utrecht Center for Neuroendocrine Tumors, ENETS Center of Excellence, Amsterdam/Utrecht, The Netherlands.,Department of Medical Oncology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.,GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.,Faculty of Health Sciences, University of Southern Denmark, J. B. Winsløws Vej 19, 3, 5000, Odense, Denmark
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
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7
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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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Karmazanovsky G, Gruzdev I, Tikhonova V, Kondratyev E, Revishvili A. Computed tomography-based radiomics approach in pancreatic tumors characterization. LA RADIOLOGIA MEDICA 2021; 126:10.1007/s11547-021-01405-0. [PMID: 34386897 DOI: 10.1007/s11547-021-01405-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/27/2021] [Indexed: 12/26/2022]
Abstract
Radiomics (or texture analysis) is a new imaging analysis technique that allows calculating the distribution of texture features of pixel and voxel values depend on the type of ROI (3D or 2D), their relationships in the image. Depending on the software, up to several thousand texture elements can be obtained. Radiomics opens up wide opportunities for differential diagnosis and prognosis of pancreatic neoplasias. The aim of this review was to highlight the main diagnostic advantages of texture analysis in different pancreatic tumors. The review describes the diagnostic performance of radiomics in different pancreatic tumor types, application methods, and problems. Texture analysis in PDAC is able to predict tumor grade and associates with lymphovascular invasion and postoperative margin status. In pancreatic neuroendocrine tumors, texture features strongly correlate with differentiation grade and allows distinguishing it from the intrapancreatic accessory spleen. In pancreatic cystic lesions, radiomics is able to accurately differentiate MCN from SCN and distinguish clinically insignificant lesions from IPMNs with advanced neoplasia. In conclusion, the use of the CT radiomics approach provides a higher diagnostic performance of CT imaging in pancreatic tumors differentiation and prognosis. Future studies should be carried out to improve accuracy and facilitate radiomics workflow in pancreatic imaging.
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Affiliation(s)
- Grigory Karmazanovsky
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
- Radiology Department, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ivan Gruzdev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia.
| | - Valeriya Tikhonova
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Evgeny Kondratyev
- Deparment of Radiology, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
| | - Amiran Revishvili
- Arrhythmology Department, A.V. Vishnevsky National Medical Research Centre of Surgery, Bolshaya Serpukhovskaya str. 27, 117997, Moscow, Russia
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Han X, Yang J, Luo J, Chen P, Zhang Z, Alu A, Xiao Y, Ma X. Application of CT-Based Radiomics in Discriminating Pancreatic Cystadenomas From Pancreatic Neuroendocrine Tumors Using Machine Learning Methods. Front Oncol 2021; 11:606677. [PMID: 34367940 PMCID: PMC8339967 DOI: 10.3389/fonc.2021.606677] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Accepted: 07/05/2021] [Indexed: 02/05/2023] Open
Abstract
Objectives The purpose of this study aimed at investigating the reliability of radiomics features extracted from contrast-enhanced CT in differentiating pancreatic cystadenomas from pancreatic neuroendocrine tumors (PNETs) using machine-learning methods. Methods In this study, a total number of 120 patients, including 66 pancreatic cystadenomas patients and 54 PNETs patients were enrolled. Forty-eight radiomic features were extracted from contrast-enhanced CT images using LIFEx software. Five feature selection methods were adopted to determine the appropriate features for classifiers. Then, nine machine learning classifiers were employed to build predictive models. The performance of the forty-five models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, and F1 score in the testing group. Results The predictive models exhibited reliable ability of differentiating pancreatic cystadenomas from PNETs when combined with suitable selection methods. A combination of DC as the selection method and RF as the classifier, as well as Xgboost+RF, demonstrated the best discriminative ability, with the highest AUC of 0.997 in the testing group. Conclusions Radiomics-based machine learning methods might be a noninvasive tool to assist in differentiating pancreatic cystadenomas and PNETs.
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Affiliation(s)
- Xuejiao Han
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Yang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.,Melanoma and Sarcoma Medical Oncology Unit, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jingwen Luo
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Pengan Chen
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Zilong Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Aqu Alu
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yinan Xiao
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
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10
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Batur A, Kılınçer A, Ateş F, Aktuğ Demir N, Ergün R. Evaluation of systemic involvement of Coronavirus disease 2019 through spleen; size and texture analysis. Turk J Med Sci 2021; 51:972-980. [PMID: 33421971 PMCID: PMC8283514 DOI: 10.3906/sag-2009-270] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Accepted: 01/07/2021] [Indexed: 01/08/2023] Open
Abstract
Background/aim To investigate the changes in the spleen size, parenchymal heterogeneity, and computed tomography (CT) texture analysis features of patients diagnosed with Coronavirus disease 2019 (COVID-19) Materials and methods The size and parenchymal structure of the spleen in 91 patients who underwent thoracic CT examination due to COVID-19 were evaluated. For the evaluation of parenchymal heterogeneity, CT texture analysis was performed using dedicated software (Olea Medical, France). The texture analysis of each case consisted of 15 first-order intensity-based features, 17 gray level co-occurrence matrix-based features, and 9 gray level run length matrix-based features. Results A total of 91 patients (45 males, 46 females) with a mean age of 54.31 ± 16.33 years (range: 18–81) were included in the study. A statistically significant decrease in spleen size was seen in the follow-up CT examinations (p < 0.001) whereas no statistically significant difference was found between the Hounsfield unit (HU) values. The radiomics consisted of first-order intensity-based features such as 90th percentile, maximum, interquartile range, range, mean absolute deviation, standard deviation, and variance, all of which showed statistically significant differences (p
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values: < 0.001, < 0.001, 0.001, 0.003, 0.001, 0.001, and 0.004, respectively). “Correlation” as a gray level co-occurrence matrix-based feature and “gray level nonuniformity” as a gray level run length matrix-based feature showed statistically differences (p-values: 0.033 and < 0.001, respectively). Conclusions Although COVID-19 manifests with lung involvement in the early stage, it can also cause systemic involvement, and the spleen may be one of its target organs. A decrease in the spleen size and parenchymal microstructure changes can be observed in the short follow-up time. It is hoped that the changes in the parenchymal microstructure will be demonstrated by a noninvasive method: texture analysis.
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Affiliation(s)
- Abdussamet Batur
- Department of Radiology, School of Medicine, Selcuk University, Konya, Turkey
| | - Abidin Kılınçer
- Department of Radiology, School of Medicine, Selcuk University, Konya, Turkey
| | - Fatih Ateş
- Department of Radiology, School of Medicine, Selcuk University, Konya, Turkey
| | - Nazlım Aktuğ Demir
- Department of Infectious Disease and Clinical Microbiology, School of Medicine, Selcuk University, Konya, Turkey
| | - Recai Ergün
- Department of Chest Diseases, School of Medicine, Selcuk University, Konya, Turkey
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11
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Bezzi C, Mapelli P, Presotto L, Neri I, Scifo P, Savi A, Bettinardi V, Partelli S, Gianolli L, Falconi M, Picchio M. Radiomics in pancreatic neuroendocrine tumors: methodological issues and clinical significance. Eur J Nucl Med Mol Imaging 2021; 48:4002-4015. [PMID: 33835220 DOI: 10.1007/s00259-021-05338-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 03/24/2021] [Indexed: 12/19/2022]
Abstract
PURPOSE To present the state-of-art of radiomics in the context of pancreatic neuroendocrine tumors (PanNETs), with a focus on the methodological and technical approaches used, to support the search of guidelines for optimal applications. Furthermore, an up-to-date overview of the current clinical applications of radiomics in the field of PanNETs is provided. METHODS Original articles were searched on PubMed and Science Direct with specific keywords. Evaluations of the selected studies have been focused mainly on (i) the general radiomic workflow and the assessment of radiomic features robustness/reproducibility, as well as on the major clinical applications and investigations accomplished so far with radiomics in the field of PanNETs: (ii) grade prediction, (iii) differential diagnosis from other neoplasms, (iv) assessment of tumor behavior and aggressiveness, and (v) treatment response prediction. RESULTS Thirty-one articles involving PanNETs radiomic-related objectives were selected. In regard to the grade differentiation task, yielded AUCs are currently in the range of 0.7-0.9. For differential diagnosis, the majority of studies are still focused on the preliminary identification of discriminative radiomic features. Limited information is known on the prediction of tumors aggressiveness and of treatment response. CONCLUSIONS Radiomics is recently expanding in the setting of PanNETs. From the analysis of the published data, it is emerging how, prior to clinical application, further validations are necessary and methodological implementations require optimization. Nevertheless, this new discipline might have the potential in assisting the current urgent need of improving the management strategies in PanNETs patients.
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Affiliation(s)
- C Bezzi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy
| | - P Mapelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - L Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - I Neri
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - P Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - A Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - V Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - S Partelli
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - L Gianolli
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy
| | - M Falconi
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy.,Pancreatic Surgery Unit, Pancreas Translational & Clinical Research Centre, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - M Picchio
- Vita-Salute San Raffaele University, Via Olgettina 58, Milan, 20132, Italy. .,Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, 20132, Milan, Italy.
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12
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Abunahel BM, Pontre B, Kumar H, Petrov MS. Pancreas image mining: a systematic review of radiomics. Eur Radiol 2020; 31:3447-3467. [PMID: 33151391 DOI: 10.1007/s00330-020-07376-6] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/25/2020] [Accepted: 10/05/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVES To systematically review published studies on the use of radiomics of the pancreas. METHODS The search was conducted in the MEDLINE database. Human studies that investigated the applications of radiomics in diseases of the pancreas were included. The radiomics quality score was calculated for each included study. RESULTS A total of 72 studies encompassing 8863 participants were included. Of them, 66 investigated focal pancreatic lesions (pancreatic cancer, precancerous lesions, or benign lesions); 4, pancreatitis; and 2, diabetes mellitus. The principal applications of radiomics were differential diagnosis between various types of focal pancreatic lesions (n = 19), classification of pancreatic diseases (n = 23), and prediction of prognosis or treatment response (n = 30). Second-order texture features were most useful for the purpose of differential diagnosis of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature), whereas filtered image features were most useful for the purpose of classification of diseases of the pancreas and prediction of diseases of the pancreas (with 100% of studies investigating them found a statistically significant feature). The median radiomics quality score of the included studies was 28%, with the interquartile range of 22% to 36%. The radiomics quality score was significantly correlated with the number of extracted radiomics features (r = 0.52, p < 0.001) and the study sample size (r = 0.34, p = 0.003). CONCLUSIONS Radiomics of the pancreas holds promise as a quantitative imaging biomarker of both focal pancreatic lesions and diffuse changes of the pancreas. The usefulness of radiomics features may vary depending on the purpose of their application. Standardisation of image acquisition protocols and image pre-processing is warranted prior to considering the use of radiomics of the pancreas in routine clinical practice. KEY POINTS • Methodologically sound studies on radiomics of the pancreas are characterised by a large sample size and a large number of extracted features. • Optimisation of the radiomics pipeline will increase the clinical utility of mineable pancreas imaging data. • Radiomics of the pancreas is a promising personalised medicine tool in diseases of the pancreas.
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Affiliation(s)
| | - Beau Pontre
- School of Medical Sciences, University of Auckland, Auckland, New Zealand
| | - Haribalan Kumar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Maxim S Petrov
- School of Medicine, University of Auckland, Auckland, New Zealand.
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13
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CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol 2020; 38:1111-1124. [PMID: 33085029 DOI: 10.1007/s11604-020-01057-6] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023]
Abstract
Radiomics is a relatively new approach for image analysis. As a part of radiomics, texture analysis, which consists in extracting a great amount of quantitative data from original images, can be used to identify specific features that can help determining the actual nature of a pancreatic lesion and providing other information such as resectability, tumor grade, tumor response to neoadjuvant therapy or survival after surgery. In this review, the basic of radiomics, recent developments and the results of texture analysis using computed tomography and magnetic resonance imaging in the field of pancreatic tumors are presented. Future applications of radiomics, such as artificial intelligence, are discussed.
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14
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Chu LC, Park S, Kawamoto S, Yuille AL, Hruban RH, Fishman EK. Pancreatic Cancer Imaging: A New Look at an Old Problem. Curr Probl Diagn Radiol 2020; 50:540-550. [PMID: 32988674 DOI: 10.1067/j.cpradiol.2020.08.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 08/21/2020] [Indexed: 12/18/2022]
Abstract
Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage. Several recent developments in pancreatic cancer imaging, including artificial intelligence and advanced visualization techniques, are rapidly changing the field. The purpose of this article is to review how these recent advances have the potential to revolutionize pancreatic cancer imaging.
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Affiliation(s)
- Linda C Chu
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD.
| | - Seyoun Park
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Satomi Kawamoto
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Alan L Yuille
- Department of Computer Science, Johns Hopkins University, Baltimore, MD
| | - Ralph H Hruban
- Sol Goldman Pancreatic Cancer Research Center, Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elliot K Fishman
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD
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15
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Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. Diagn Interv Imaging 2020; 101:555-564. [PMID: 32278586 DOI: 10.1016/j.diii.2020.03.002] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 03/03/2020] [Accepted: 03/10/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE The purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS Eighty-nine patients with AIP (65 men, 24 women; mean age, 59.7±13.9 [SD] years; range: 21-83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1±12.3 [SD] years; range: 36-86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5mm thickness/increment) were compared with thick-slices images (3 or 5mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing. RESULTS The pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8-100%), 83.9% (52:67; 95% CI: 74.7-93.0%) and 77.4% (48/62; 95% CI: 67.0-87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6-100%) and 100% specificity (33/33; 95% CI: 93-100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8-100%) and area under the curve of 0.975 (95% CI: 0.936-1.0). CONCLUSIONS Radiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.
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Abstract
Radiologic characterization of pancreatic lesions is currently limited. Computed tomography is insensitive in detecting and characterizing small pancreatic lesions. Moreover, heterogeneity of many pancreatic lesions makes determination of malignancy challenging. As a result, invasive diagnostic testing is frequently used to characterize pancreatic lesions but often yields indeterminate results. Computed tomography texture analysis (CTTA) is an emerging noninvasive computational tool that quantifies gray-scale pixels/voxels and their spatial relationships within a region of interest. In nonpancreatic lesions, CTTA has shown promise in diagnosis, lesion characterization, and risk stratification, and more recently, pancreatic applications of CTTA have been explored. This review outlines the emerging role of CTTA in identifying, characterizing, and risk stratifying pancreatic lesions. Although recent studies show the clinical potential of CTTA of the pancreas, a clear understanding of which specific texture features correlate with high-grade dysplasia and predict survival has not yet been achieved. Further multidisciplinary investigations using strong radiologic-pathologic correlation are needed to establish a role for this noninvasive diagnostic tool.
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17
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Zurek Munk-Madsen M, Zakarian K, Sandor Oturai P, Hansen CP, Federspiel B, Fallentin E, Linno Willemoe G. Intrapancreatic accessory spleen mimicking malignant tumor: three case reports. Acta Radiol Open 2019; 8:2058460119859347. [PMID: 31285851 PMCID: PMC6600498 DOI: 10.1177/2058460119859347] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 05/20/2019] [Indexed: 12/15/2022] Open
Abstract
Intrapancreatic hypervascular lesions may represent metastases, neuroendocrine
tumors, or intrapancreatic accessory spleens. The benign intrapancreatic
accessory spleen can be difficult to separate from a malignant neuroendocrine
tumor or metastasis. We report three cases of pancreatic lesions that underwent
pancreatic surgery due to suspicion of malignancy on imaging; all cases were
histologically intrapancreatic accessory spleens. Our cases point to the
importance of performing single-photon emission computed tomography with
heat-damaged Tc-99m-pertechnetate labelled erythrocytes to identify splenic
tissue, even though small lesions can show a false-negative result.
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Affiliation(s)
- Maria Zurek Munk-Madsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Kristine Zakarian
- Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Peter Sandor Oturai
- Department of Clinical Physiology, Nuclear Medicine & PET, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Carsten Palnæs Hansen
- Department of Surgical Gastroenterology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Birgitte Federspiel
- Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Eva Fallentin
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Gro Linno Willemoe
- Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
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