A primer on artificial intelligence in pancreatic imaging.
暂无分享,去创建一个
L. Chu | E. Fishman | R. Hruban | S. Kawamoto | P. Soyer | T. M. Ahmed | Taha M. Ahmed
[1] A. Luciani,et al. Synthetic MR image generation of macrotrabecular-massive hepatocellular carcinoma using generative adversarial networks. , 2023, Diagnostic and interventional imaging.
[2] A. Feydy,et al. Artificial intelligence in diagnostic and interventional radiology: Where are we now? , 2022, Diagnostic and interventional imaging.
[3] C. Fiorino,et al. Prediction of the characteristics of aggressiveness of pancreatic neuroendocrine neoplasms (PanNENs) based on CT radiomic features , 2022, European Radiology.
[4] Jeffrey W. Clark,et al. Tolerability, Attrition Rates, and Survival Outcomes of Neoadjuvant FOLFIRINOX for Non-Metastatic Pancreatic Adenocarcinoma: Intent-to-Treat Analysis. , 2022, Journal of the American College of Surgeons.
[5] X. Bai,et al. Classification prediction of pancreatic cystic neoplasms based on radiomics deep learning models , 2022, BMC Cancer.
[6] Dexin Yu,et al. Development and external validation of a radiomics combined with clinical nomogram for preoperative prediction prognosis of resectable pancreatic ductal adenocarcinoma patients , 2022, Frontiers in Oncology.
[7] Debiao Li,et al. Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images , 2022, Frontiers in Oncology.
[8] A. Ejaz,et al. Surgical resection rates after neoadjuvant therapy for localized pancreatic ductal adenocarcinoma: meta-analysis. , 2022, The British journal of surgery.
[9] R. Cuocolo,et al. Systematic review of the radiomics quality score applications: an EuSoMII Radiomics Auditing Group Initiative , 2022, European Radiology.
[10] K. Chiba,et al. Focal pancreatic parenchyma atrophy is a harbinger of pancreatic cancer and a clue to the intraductal spreading subtype. , 2022, Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.].
[11] Wei-Chih Liao,et al. Pancreatic Cancer Detection on CT Scans with Deep Learning: A Nationwide Population-based Study. , 2022, Radiology.
[12] Seyoun Park,et al. Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists , 2022, Abdominal Radiology.
[13] L. Chu,et al. Imaging of Pancreatic Ductal Adenocarcinoma: An Update on Recent Advances , 2022, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.
[14] J. Byun,et al. Deep Learning-based Detection of Solid and Cystic Pancreatic Neoplasms at Contrast-enhanced CT. , 2022, Radiology.
[15] S. Busoni,et al. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade , 2022, La radiologia medica.
[16] E. Fishman,et al. Does artificial intelligence surpass the radiologist? , 2022, Diagnostic and interventional imaging.
[17] Le Lu,et al. Deep Learning for Fully Automated Prediction of Overall Survival in Patients Undergoing Resection for Pancreatic Cancer , 2022, Annals of surgery.
[18] J. Fletcher,et al. Radiomics-Based Machine Learning Models Can Detect Pancreatic Cancer on Prediagnostic CTs at a Substantial Lead Time Prior to Clinical Diagnosis. , 2022, Gastroenterology.
[19] R. Schmid,et al. Accurate prediction of histological grading of intraductal papillary mucinous neoplasia using deep learning , 2022, Endoscopy.
[20] E. Fishman,et al. The future of radiology: What if artificial intelligence is really as good as predicted? , 2022, Diagnostic and interventional imaging.
[21] Yun Bian,et al. Two nomograms for differentiating mass-forming chronic pancreatitis from pancreatic ductal adenocarcinoma in patients with chronic pancreatitis , 2022, European Radiology.
[22] Jonathan I. Tamir,et al. Implicit data crimes: Machine learning bias arising from misuse of public data , 2022, Proceedings of the National Academy of Sciences of the United States of America.
[23] Jie Tian,et al. Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis , 2022, BMC Medicine.
[24] Bechien U. Wu,et al. Quantitative Radiomic Features From Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis , 2022, medRxiv.
[25] J. Mezrich. Is Artificial Intelligence (AI) a Pipe Dream? Why Legal Issues Present Significant Hurdles to AI Autonomy. , 2022, AJR. American journal of roentgenology.
[26] Yingsheng Cheng,et al. Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis , 2022, Translational oncology.
[27] Huiyan Jiang,et al. Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma , 2022, Cancer Imaging.
[28] Bechien U. Wu,et al. Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images. , 2022, Cancer biomarkers : section A of Disease markers.
[29] Wei Li,et al. Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR , 2021, Frontiers in Oncology.
[30] M. Haider,et al. Pre-operative radiomics model for prognostication in resectable pancreatic adenocarcinoma with external validation , 2021, European Radiology.
[31] F. Alongi,et al. Radiomic analysis to predict local response in locally advanced pancreatic cancer treated with stereotactic body radiation therapy , 2021, La radiologia medica.
[32] Haigen Hu,et al. CT classification model of pancreatic serous cystic neoplasms and mucinous cystic neoplasms based on a deep neural network , 2021, Abdominal Radiology.
[33] Jocelyn Hoye,et al. CT Radiomic Features of Superior Mesenteric Artery Involvement in Pancreatic Ductal Adenocarcinoma: A Pilot Study. , 2021, Radiology.
[34] P. Cheng,et al. Deep Learning: An Update for Radiologists. , 2021, Radiographics : a review publication of the Radiological Society of North America, Inc.
[35] Wei-Chih Liao,et al. Radiomic Features at CT Can Distinguish Pancreatic Cancer from Noncancerous Pancreas. , 2021, Radiology. Imaging cancer.
[36] Jun Xu,et al. Preoperative Prediction of G1 and G2/3 Grades in Patients With Nonfunctional Pancreatic Neuroendocrine Tumors Using Multimodality Imaging. , 2021, Academic radiology.
[37] Haibin Shi,et al. Radiomics Analysis for Predicting Malignant Potential of Intraductal Papillary Mucinous Neoplasms of the Pancreas: Comparison of CT and MRI. , 2021, Academic radiology.
[38] Huiyan Jiang,et al. CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis. , 2021, Academic radiology.
[39] Amber L. Simpson,et al. Quantitative Computed Tomography Image Analysis to Predict Pancreatic Neuroendocrine Tumor Grade. , 2021, JCO Clinical Cancer Informatics.
[40] S. Choi,et al. Radiomics-based neural network predicts recurrence patterns in glioblastoma using dynamic susceptibility contrast-enhanced MRI , 2021, Scientific Reports.
[41] B. Stieltjes,et al. Automated Detection of Pancreatic Cystic Lesions on CT Using Deep Learning , 2021, Diagnostics.
[42] A. Bevilacqua,et al. A [68Ga]Ga-DOTANOC PET/CT Radiomic Model for Non-Invasive Prediction of Tumour Grade in Pancreatic Neuroendocrine Tumours , 2021, Diagnostics.
[43] I. Išgum,et al. Generative Adversarial Networks: A Primer for Radiologists. , 2021, Radiographics : a review publication of the Radiological Society of North America, Inc.
[44] L. Coombs,et al. 2020 ACR Data Science Institute Artificial Intelligence Survey. , 2021, Journal of the American College of Radiology : JACR.
[45] Zhenyu Shu,et al. Radiomic nomogram based on MRI to predict grade of branching type intraductal papillary mucinous neoplasms of the pancreas: a multicenter study , 2021, Cancer Imaging.
[46] K. Jiang,et al. Survival prediction after upfront surgery in patients with pancreatic ductal adenocarcinoma: Radiomic, clinic-pathologic and body composition analysis. , 2021, Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.].
[47] S. Ren,et al. Preoperative differentiation of serous cystic neoplasms from mucin-producing pancreatic cystic neoplasms using a CT-based radiomics nomogram , 2021, Abdominal Radiology.
[48] A. Dohan,et al. Artificial intelligence: a critical review of current applications in pancreatic imaging , 2021, Japanese Journal of Radiology.
[49] M. Manzoni,et al. CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors , 2021, La radiologia medica.
[50] T. Liang,et al. Fully end-to-end deep-learning-based diagnosis of pancreatic tumors , 2021, Theranostics.
[51] D. Iannitti,et al. Pure and Hybrid Deep Learning Models can Predict Pathologic Tumor Response to Neoadjuvant Therapy in Pancreatic Adenocarcinoma: A Pilot Study , 2020, The American surgeon.
[52] L. Wood,et al. Desmin and CD31 immunolabeling for detecting venous invasion of the pancreatobiliary tract cancers , 2020, PloS one.
[53] M. Ronot,et al. CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas , 2020, Cancers.
[54] Y. Nagakawa,et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study , 2020, Journal of hepato-biliary-pancreatic sciences.
[55] R. Hruban,et al. Comprehensive histological evaluation with clinical analysis of venous invasion in pancreatic ductal adenocarcinoma: From histology to clinical implications. , 2020, Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.].
[56] X. Qin,et al. Radiomics Analysis Based on Diffusion Kurtosis Imaging and T2 Weighted Imaging for Differentiation of Pancreatic Neuroendocrine Tumors From Solid Pseudopapillary Tumors , 2020, Frontiers in Oncology.
[57] Y. Balagurunathan,et al. Multiphase computed tomography radiomics of pancreatic intraductal papillary mucinous neoplasms to predict malignancy , 2020, World journal of gastroenterology.
[58] Y. Hirooka,et al. Usefulness of endoscopic ultrasound-guided fine-needle biopsy for the diagnosis of autoimmune pancreatitis using a 22-gauge Franseen needle: a prospective multicenter study , 2020, Endoscopy.
[59] Jun Xu,et al. Noncontrast Radiomics Approach for Predicting Grades of Nonfunctional Pancreatic Neuroendocrine Tumors , 2020, Journal of magnetic resonance imaging : JMRI.
[60] N. Dogan,et al. Predictive Value of 0.35T Magnetic Resonance Imaging Radiomic Features in Stereotactic Ablative Body Radiotherapy of Pancreatic Cancer: A Pilot Study. , 2020, Medical physics.
[61] A. Yuille,et al. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features. , 2020, Diagnostic and interventional imaging.
[62] A. Madabhushi,et al. CT-Radiomic Approach to Predict G1/2 Nonfunctional Pancreatic Neuroendocrine Tumor. , 2020, Academic radiology.
[63] Xuanyi Wang,et al. Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection , 2020, European Radiology.
[64] M. Bitzer,et al. Complementary role of computed tomography texture analysis for differentiation of pancreatic ductal adenocarcinoma from pancreatic neuroendocrine tumors in the portal-venous enhancement phase , 2020, Abdominal Radiology.
[65] Yun Bian,et al. Performance of CT-based radiomics in diagnosis of superior mesenteric vein resection margin in patients with pancreatic head cancer , 2020, Abdominal Radiology.
[66] S. Park,et al. A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features , 2020, BMC Cancer.
[67] Xiao Chen,et al. Differentiating hypovascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinoma based on CT texture analysis , 2020, Acta radiologica.
[68] Vikesh K. Singh,et al. Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. , 2019, European journal of radiology.
[69] Sharon A. Lawrence,et al. Intraductal Papillary Mucinous Neoplasms: Have IAP Consensus Guidelines Changed our Approach? , 2019, Annals of surgery.
[70] Xiaoying Wang,et al. Preoperative differentiation of pancreatic mucinous cystic neoplasm from macrocystic serous cystic adenoma using radiomics: Preliminary findings and comparison with radiological model. , 2019, European journal of radiology.
[71] Fanny Orlhac,et al. The Dark Side of Radiomics: On the Paramount Importance of Publishing Negative Results , 2019, The Journal of Nuclear Medicine.
[72] Haidy Nasief,et al. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer , 2019, npj Precision Oncology.
[73] Huadan Xue,et al. Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics. , 2019, European journal of radiology.
[74] David L. Masica,et al. A multimodality test to guide the management of patients with a pancreatic cyst , 2019, Science Translational Medicine.
[75] Dongsheng Gu,et al. CT radiomics may predict the grade of pancreatic neuroendocrine tumors: a multicenter study , 2019, European Radiology.
[76] Xuelei Ma,et al. Discrimination of Pancreatic Serous Cystadenomas From Mucinous Cystadenomas With CT Textural Features: Based on Machine Learning , 2019, Front. Oncol..
[77] L. Wood,et al. Why is pancreatic cancer so deadly? The pathologist's view , 2019, The Journal of pathology.
[78] Takamichi Kuwahara,et al. Usefulness of Deep Learning Analysis for the Diagnosis of Malignancy in Intraductal Papillary Mucinous Neoplasms of the Pancreas , 2019, Clinical and translational gastroenterology.
[79] A. Yuille,et al. Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue. , 2019, AJR. American journal of roentgenology.
[80] Zheng-yu Jin,et al. Unresectable pancreatic ductal adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy. , 2019, European journal of radiology.
[81] Jayasree Chakraborty,et al. Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis. , 2019, HPB : the official journal of the International Hepato Pancreato Biliary Association.
[82] Yi Guo,et al. Computer-Aided Diagnosis of Pancreas Serous Cystic Neoplasms: A Radiomics Method on Preoperative MDCT Images , 2019, Technology in cancer research & treatment.
[83] Ji Li,et al. Differential Diagnosis for Pancreatic Cysts in CT Scans Using Densely-Connected Convolutional Networks , 2018, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[84] Yi Wei,et al. Two-dimensional Texture Analysis Based on CT Images to Differentiate Pancreatic Lymphoma and Pancreatic Adenocarcinoma: A Preliminary Study. , 2019, Academic radiology.
[85] P. Williams,et al. Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features , 2019, Abdominal Radiology.
[86] Amber L. Simpson,et al. CT radiomics to predict high‐risk intraductal papillary mucinous neoplasms of the pancreas , 2018, Medical physics.
[87] Mark Tann,et al. CT texture analysis of pancreatic cancer , 2018, European Radiology.
[88] Philippe Lambin,et al. Pre-treatment CT radiomics to predict 3-year overall survival following chemoradiotherapy of esophageal cancer , 2018, Acta oncologica.
[89] Amber L. Simpson,et al. Survival Prediction in Pancreatic Ductal Adenocarcinoma by Quantitative Computed Tomography Image Analysis , 2018, Annals of Surgical Oncology.
[90] P. Lambin,et al. Radiomics: the bridge between medical imaging and personalized medicine , 2017, Nature Reviews Clinical Oncology.
[91] Joel H. Saltz,et al. Classification of Pancreatic Cysts in Computed Tomography Images Using a Random Forest and Convolutional Neural Network Ensemble , 2017, MICCAI.
[92] Jin‐Young Jang,et al. of the pancreas , 2017 .
[93] Masoom A. Haider,et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma – a quantitative analysis , 2017, BMC Medical Imaging.
[94] B. Weynand,et al. Accuracy of Pancreatic Neuroendocrine Tumour Grading by Endoscopic Ultrasound-Guided Fine Needle Aspiration: Analysis of a Large Cohort and Perspectives for Improvement , 2017, Neuroendocrinology.
[95] K. Ohtomo,et al. Development of pancreatic cancer is predictable well in advance using contrast-enhanced CT: a case–cohort study , 2017, European Radiology.
[96] B. Erickson,et al. Machine Learning for Medical Imaging. , 2017, Radiographics : a review publication of the Radiological Society of North America, Inc.
[97] Y. Hirooka,et al. Prospective multicenter study on the usefulness of EUS-guided FNA biopsy for the diagnosis of autoimmune pancreatitis. , 2016, Gastrointestinal endoscopy.
[98] O. Kocaman,et al. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images , 2016, Endoscopic ultrasound.
[99] J. Cameron,et al. Intraductal papillary mucinous neoplasm (IPMN) with high-grade dysplasia is a risk factor for the subsequent development of pancreatic ductal adenocarcinoma. , 2016, HPB : the official journal of the International Hepato Pancreato Biliary Association.
[100] R. Jensen,et al. ENETS Consensus Guidelines Update for the Management of Patients with Functional Pancreatic Neuroendocrine Tumors and Non-Functional Pancreatic Neuroendocrine Tumors , 2016, Neuroendocrinology.
[101] Paul Kinahan,et al. Radiomics: Images Are More than Pictures, They Are Data , 2015, Radiology.
[102] P. Lambin,et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach , 2014, Nature Communications.
[103] N. McGranahan,et al. The causes and consequences of genetic heterogeneity in cancer evolution , 2013, Nature.
[104] Andre Dekker,et al. Radiomics: the process and the challenges. , 2012, Magnetic resonance imaging.
[105] K. Miles,et al. Tumour heterogeneity in oesophageal cancer assessed by CT texture analysis: preliminary evidence of an association with tumour metabolism, stage, and survival. , 2012, Clinical radiology.
[106] P. Vilmann,et al. Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses. , 2012, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.
[107] M. Nowak,et al. Distant Metastasis Occurs Late during the Genetic Evolution of Pancreatic Cancer , 2010, Nature.
[108] P. Maisonneuve,et al. Pancreatic cancer in chronic pancreatitis; aetiology, incidence, and early detection. , 2010, Best practice & research. Clinical gastroenterology.
[109] P. Vilmann,et al. Neural network analysis of dynamic sequences of EUS elastography used for the differential diagnosis of chronic pancreatitis and pancreatic cancer. , 2008, Gastrointestinal endoscopy.
[110] Baoxin Li,et al. Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue. , 2008, Gastrointestinal endoscopy.
[111] J G Fletcher,et al. Time interval between abnormalities seen on CT and the clinical diagnosis of pancreatic cancer: retrospective review of CT scans obtained before diagnosis. , 2004, AJR. American journal of roentgenology.
[112] Christoph Bobrowski,et al. Comparison of endoscopic ultrasound-guided fine needle aspiration for focal pancreatic lesions in patients with normal parenchyma and chronic pancreatitis , 2002, American Journal of Gastroenterology.