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Bing X, Wang N, Li Y, Sun H, Yao J, Li R, Li Z, Ouyang A. The Value of Dual-Energy Computed Tomography-Based Radiomics in the Evaluation of Interstitial Fibers of Clear Cell Renal Carcinoma. Technol Cancer Res Treat 2024; 23:15330338241235554. [PMID: 38404055 PMCID: PMC10896050 DOI: 10.1177/15330338241235554] [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: 08/17/2023] [Revised: 12/27/2023] [Accepted: 02/09/2024] [Indexed: 02/27/2024] Open
Abstract
OBJECTIVE We investigated the potential of dual-energy computed tomography (DECT) radiomics in assessing cancer-associated fibroblasts in clear cell renal carcinoma (ccRCC). METHODS A retrospective analysis was conducted on 132 patients with ccRCC. The arterial and venous phase iodine-based material decomposition images (IMDIs), virtual non-contrast images, 70 keV, 100 keV, and 150 keV virtual monoenergetic images, and mixed energy images (MEIs) were obtained from the DECT datasets. On the Radcloud platform, radiomics feature extraction, feature selection, and model establishment were performed. Seven radiomics models were established using the support vector machine. The predictive performance was evaluated by utilizing receiver operating characteristic and the area under the curve (AUC) was calculated. Nomograms were constructed. RESULTS The combined model demonstrated high efficiency in evaluating pseudocapsule thickness with AUC, specificity, and sensitivity of 0.833, 0.870, and 0.750, respectively in the validation set, surpassing those of other models. The precision, F1-score, and Youden index were also higher for the combined model. For evaluating the number of collagen fibers, the combined model exhibited the highest AUC (0.741) among all models, with a specificity of 0.830 and a sensitivity of 0.330. The AUC in the 150 kv model and IMDI model were slightly lower than those in the combined model (0.728 and 0.710, respectively), with corresponding sensitivity and specificity of 0.560/0.780 and 0.670/0.830. The nomogram exhibited that Rad-score had good prediction efficiency. CONCLUSION DECT radiomics features have significant value in evaluating the interstitial fibers of ccRCC. The combined model of IMDI + MEI exhibits superior performance in assessing the thickness of the pseudocapsule, while the combined, 150 keV, and IMDI models demonstrate higher efficacy in evaluating collagen fiber number. Radiomics, combined with imaging features and clinical features, has excellent predictive performance. These findings offer crucial support for the clinical diagnosis, treatment, and prognosis of ccRCC and provide valuable insights into the application of DECT.
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Affiliation(s)
- Xue Bing
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan Central Hospital, Jinan, P.R. China
| | - Ning Wang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan Central Hospital, Jinan, P.R. China
| | - Yuhan Li
- Department of Radiology, Longkou Traditional Chinese Medicine Hospital, Yantai, P.R. China
| | - Haitao Sun
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan Central Hospital, Jinan, P.R. China
| | - Jian Yao
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan Central Hospital, Jinan, P.R. China
| | - Ruobing Li
- Department of Radiology, Shandong First Medical University, Jinan, P.R. China
| | - Zhongyuan Li
- School of Medical Imaging, Weifang Medical University, Weifang, P.R. China
| | - Aimei Ouyang
- Department of Radiology, Central Hospital Affiliated to Shandong First Medical University, Jinan Central Hospital, Jinan, P.R. China
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Liao H, Yuan J, Liu C, Zhang J, Yang Y, Liang H, Jiang S, Chen S, Li Y, Liu Y. Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma. Insights Imaging 2023; 14:223. [PMID: 38129708 PMCID: PMC10739634 DOI: 10.1186/s13244-023-01553-z] [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: 08/23/2023] [Accepted: 10/28/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVE This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC). METHODS A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups. RESULTS To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival. CONCLUSIONS Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis. CRITICAL RELEVANCE STATEMENT The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models. KEY POINTS • Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.
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Affiliation(s)
- Hongfan Liao
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jiang Yuan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China
| | - Chunhua Liu
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Jiao Zhang
- Department of Radiology, the Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yaying Yang
- Department of Pathology, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Hongwei Liang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Song Jiang
- Department of Radiology, Chongqing Ping An Medical Imaging Diagnosis Center, Chongqing, China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China.
| | - Yongmei Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
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Wang H, Qiu J, Xie J, Lu W, Pan Y, Ma J, Jia M. Radiomics‑Clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients. J Cancer Res Clin Oncol 2023; 149:13353-13361. [PMID: 37491635 DOI: 10.1007/s00432-023-05162-7] [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: 06/05/2023] [Accepted: 07/09/2023] [Indexed: 07/27/2023]
Abstract
BACKGROUND To establish a radiomics-clinical model based on 99mTc-MDP SPECT/CT for distinguishing between bone metastasis and benign bone disease in tumor patients. METHODS We retrospectively analyzed 256 patients (122 with bone metastasis and 134 with benign bone disease) and randomized them in the ratio of 6:2:2 into training, test and validation sets. All patients underwent 99mTc-labeled methylene diphosphonate (99mTc-MDP) SPECT/CT. We manually outlined the volumes of interest (VOIs) of lesions using ITK-SNAP from SPECT and CT images. In the training set, radiomics features were extracted using PyRadiomics and selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we established three radiomics models (CT, SPECT and SPECT-CT models) using support vector machine (SVM). In addition, a radiomics-clinical model was constructed using multivariable logistic regression analysis. The four models' performance was assessed using the area under the receiver operating characteristic curve (AUC). Using DeLong test to make comparisons between the ROC (receiver operating characteristic) curves of different models. The clinical utility of the models was evaluated using decision curve analysis (DCA). RESULTS The radiomics-clinical displayed excellent performance, and its AUC was 0.941 and 0.879 in the training and test sets. The DCA of radiomics-clinical model showed the highest clinical utility. CONCLUSIONS The radiomics-clinical nomogram for identifying bone metastasis and benign bone disease in tumor patients was suitable to assist in clinical decision.
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Affiliation(s)
- Huili Wang
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250012, China
| | - Jianfeng Qiu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China
| | - Jindong Xie
- College of Preventive Medicine & Institute of Radiation Medicine, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250012, China
| | - Weizhao Lu
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China
| | - Yuteng Pan
- Medical Science and Technology Innovation Center, Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, 250000, China
| | - Junchi Ma
- School of Radiology, Shandong First Medical University & Shandong Academy of Medical Sciences, Taian, 271016, China.
| | - Mingsheng Jia
- Department of Nuclear Medicine, The Second Affiliated Hospital of Shandong First Medical University, Taishan Street, No.706, Taian, 271000, China.
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Kang W, Qiu X, Luo Y, Luo J, Liu Y, Xi J, Li X, Yang Z. Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis. J Transl Med 2023; 21:598. [PMID: 37674169 PMCID: PMC10481579 DOI: 10.1186/s12967-023-04437-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 08/12/2023] [Indexed: 09/08/2023] Open
Abstract
The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a "digital biopsy". As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.
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Affiliation(s)
- Wendi Kang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiang Qiu
- Obstetrics and Gynecology Hospital of, Fudan University, Shanghai, 200011, China
| | - Yingen Luo
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Jianwei Luo
- Department of Diagnostic Radiology, Hunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, 283 Tongzipo Road, Yuelu District, Changsha, 410013, Hunan, China
| | - Yang Liu
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Junqing Xi
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Xiao Li
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China
| | - Zhengqiang Yang
- Department of Interventional Therapy, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Panjiayuan Nanli 17# Chaoyang District, Beijing, 100021, China.
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Abbasian Ardakani A, Mohammadi A, Mirza-Aghazadeh-Attari M, Faeghi F, Vogl TJ, Acharya UR. Diagnosis of Metastatic Lymph Nodes in Patients With Papillary Thyroid Cancer: A Comparative Multi-Center Study of Semantic Features and Deep Learning-Based Models. JOURNAL OF ULTRASOUND IN MEDICINE : OFFICIAL JOURNAL OF THE AMERICAN INSTITUTE OF ULTRASOUND IN MEDICINE 2023; 42:1211-1221. [PMID: 36437513 DOI: 10.1002/jum.16131] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/01/2022] [Accepted: 11/06/2022] [Indexed: 05/18/2023]
Abstract
OBJECTIVES Deep learning algorithms have shown potential in streamlining difficult clinical decisions. In the present study, we report the diagnostic profile of a deep learning model in differentiating malignant and benign lymph nodes in patients with papillary thyroid cancer. METHODS An in-house deep learning-based model called "ClymphNet" was developed and tested using two datasets containing ultrasound images of 195 malignant and 178 benign lymph nodes. An expert radiologist also viewed these ultrasound images and extracted qualitative imaging features used in routine clinical practice. These signs were used to train three different machine learning algorithms. Then the deep learning model was compared with the machine learning models on internal and external validation datasets containing 22 and 82 malignant and 20 and 76 benign lymph nodes, respectively. RESULTS Among the three machine learning algorithms, the support vector machine model (SVM) outperformed the best, reaching a sensitivity of 91.35%, specificity of 88.54%, accuracy of 90.00%, and an area under the curve (AUC) of 0.925 in all cohorts. The ClymphNet performed better than the SVM protocol in internal and external validation, achieving a sensitivity of 93.27%, specificity of 92.71%, and an accuracy of 93.00%, and an AUC of 0.948 in all cohorts. CONCLUSION A deep learning model trained with ultrasound images outperformed three conventional machine learning algorithms fed with qualitative imaging features interpreted by radiologists. Our study provides evidence regarding the utility of ClymphNet in the early and accurate differentiation of benign and malignant lymphadenopathy.
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Affiliation(s)
- Ali Abbasian Ardakani
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Afshin Mohammadi
- Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran
| | - Mohammad Mirza-Aghazadeh-Attari
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Fariborz Faeghi
- Department of Radiology Technology, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Thomas J Vogl
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt am Main, Germany
| | - U Rajendra Acharya
- Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, Singapore, Singapore
- Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
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Xue C, Zhou Q, Xi H, Zhou J. Radiomics: A review of current applications and possibilities in the assessment of tumor microenvironment. Diagn Interv Imaging 2023; 104:113-122. [PMID: 36283933 DOI: 10.1016/j.diii.2022.10.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 10/06/2022] [Accepted: 10/13/2022] [Indexed: 12/24/2022]
Abstract
With the recent success in the application of immunotherapy for treating various advanced cancers, the tumor microenvironment has rapidly become an important field of research. The tumor microenvironment is complex and its characteristics strongly influence disease biology and potentially responses to systemic therapy. Accurate preoperative assessment of tumor microenvironment is of great significance for the formulation of an immunotherapy strategy and evaluation of patient prognosis. As a research hotspot in medical image analysis technology, radiomics has been applied in the auxiliary diagnosis of the tumor microenvironment. This article reviews the current status of radiomics in the elective application on tumor microenvironment and discusses potential prospects.
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Affiliation(s)
- Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Qing Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Huaze Xi
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Lanzhou, 730030, China; Second Clinical School, Lanzhou University, Lanzhou, 730030, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, 730030, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, 730030, China.
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Radiomics Combined with Multiple Machine Learning Algorithms in Differentiating Pancreatic Ductal Adenocarcinoma from Pancreatic Neuroendocrine Tumor: More Hands Produce a Stronger Flame. J Clin Med 2022; 11:jcm11226789. [PMID: 36431266 PMCID: PMC9697420 DOI: 10.3390/jcm11226789] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Revised: 11/04/2022] [Accepted: 11/15/2022] [Indexed: 11/19/2022] Open
Abstract
The aim of this study was to assess the diagnostic ability of radiomics combined with multiple machine learning algorithms to differentiate pancreatic ductal adenocarcinoma (PDAC) from pancreatic neuroendocrine tumor (pNET). This retrospective study included a total of 238 patients diagnosed with PDAC or pNET. Using specialized software, radiologists manually mapped regions of interest (ROIs) from computed tomography images and automatically extracted radiomics features. A total of 45 discriminative models were built by five selection algorithms and nine classification algorithms. The performances of the discriminative models were assessed by sensitivity, specificity and the area under receiver operating characteristic curve (AUC) in the training and validation datasets. Using the combination of Gradient Boosting Decision Tree (GBDT) as the selection algorithm and Random Forest (RF) as the classification algorithm, the optimal diagnostic ability with the highest AUC was presented in the training and validation datasets. The sensitivity, specificity and AUC of the model were 0.804, 0.973 and 0.971 in the training dataset and 0.742, 0.934 and 0.930 in the validation dataset, respectively. The combination of radiomics and multiple machine learning algorithms showed the potential ability to discriminate PDAC from pNET. We suggest that multi-algorithm modeling should be considered for similar studies in the future rather than using a single algorithm empirically.
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Abstract
The basic pancreatic lesions include location, size, shape, number, capsule, calcification/calculi, hemorrhage, cystic degeneration, fibrosis, pancreatic duct alterations, and microvessel. One or more basic lesions form a kind of pancreatic disease. As recognizing the characteristic imaging features of pancreatic basic lesions and their relationships with pathology aids in differentiating the variety of pancreatic diseases. The purpose of this study is to review the pathological and imaging features of the basic pancreatic lesions.
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