BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Bartoli M, Barat M, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Chassagnon G, Soyer P. CT and MRI of pancreatic tumors: an update in the era of radiomics. Jpn J Radiol 2020;38:1111-24. [PMID: 33085029 DOI: 10.1007/s11604-020-01057-6] [Cited by in Crossref: 11] [Cited by in F6Publishing: 10] [Article Influence: 5.5] [Reference Citation Analysis]
Number Citing Articles
1 Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2021. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
2 de la Pinta C. Radiomics in pancreatic cancer for oncologist: Present and future. Hepatobiliary Pancreat Dis Int 2021:S1499-3872(21)00231-9. [PMID: 34961674 DOI: 10.1016/j.hbpd.2021.12.006] [Reference Citation Analysis]
3 Do RKG, Kambadakone A. Radiomics for CT Assessment of Vascular Contact in Pancreatic Adenocarcinoma. Radiology 2021;301:623-4. [PMID: 34491133 DOI: 10.1148/radiol.2021211635] [Reference Citation Analysis]
4 Liang L, Ding Y, Yu Y, Liu K, Rao S, Ge Y, Zeng M. Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study. BMC Med Imaging 2021;21:75. [PMID: 33902469 DOI: 10.1186/s12880-021-00605-4] [Reference Citation Analysis]
5 Barat M, Hoeffel C, Aissaoui M, Dohan A, Oudjit A, Dautry R, Paisant A, Malgras B, Cottereau AS, Soyer P. Focal splenic lesions: Imaging spectrum of diseases on CT, MRI and PET/CT. Diagn Interv Imaging 2021;102:501-13. [PMID: 33965354 DOI: 10.1016/j.diii.2021.03.006] [Reference Citation Analysis]
6 Pantelis AG, Panagopoulou PA, Lapatsanis DP. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review. Diagnostics 2022;12:874. [DOI: 10.3390/diagnostics12040874] [Reference Citation Analysis]
7 Virarkar M, Wong VK, Morani AC, Tamm EP, Bhosale P. Update on quantitative radiomics of pancreatic tumors. Abdom Radiol (NY) 2021. [PMID: 34292365 DOI: 10.1007/s00261-021-03216-3] [Reference Citation Analysis]
8 Casà C, Piras A, D’aviero A, Preziosi F, Mariani S, Cusumano D, Romano A, Boskoski I, Lenkowicz J, Dinapoli N, Cellini F, Gambacorta MA, Valentini V, Mattiucci GC, Boldrini L. The impact of radiomics in diagnosis and staging of pancreatic cancer. Clin Med Insights Gastroenterol 2022;15:263177452210815. [DOI: 10.1177/26317745221081596] [Reference Citation Analysis]
9 Satake H, Ishigaki S, Ito R, Naganawa S. Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence. Radiol Med 2021. [PMID: 34704213 DOI: 10.1007/s11547-021-01423-y] [Reference Citation Analysis]
10 Pellat A, Cottereau AS, Terris B, Coriat R. Neuroendocrine Carcinomas of the Digestive Tract: What Is New? Cancers (Basel) 2021;13:3766. [PMID: 34359666 DOI: 10.3390/cancers13153766] [Reference Citation Analysis]
11 Hirata K, Sugimori H, Fujima N, Toyonaga T, Kudo K. Artificial intelligence for nuclear medicine in oncology. Ann Nucl Med 2022. [PMID: 35028877 DOI: 10.1007/s12149-021-01693-6] [Reference Citation Analysis]
12 Bian Y, Jiang H, Zheng J, Shao C, Lu J. Basic pancreatic lesions: Radiologic-pathologic correlation. Journal of Translational Internal Medicine 2022;10:18-27. [DOI: 10.2478/jtim-2022-0003] [Reference Citation Analysis]
13 Barat M, Chassagnon G, Dohan A, Gaujoux S, Coriat R, Hoeffel C, Cassinotto C, Soyer P. Artificial intelligence: a critical review of current applications in pancreatic imaging. Jpn J Radiol 2021;39:514-23. [PMID: 33550513 DOI: 10.1007/s11604-021-01098-5] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
14 Klein AP. Pancreatic cancer epidemiology: understanding the role of lifestyle and inherited risk factors. Nat Rev Gastroenterol Hepatol 2021;18:493-502. [DOI: 10.1038/s41575-021-00457-x] [Cited by in Crossref: 7] [Cited by in F6Publishing: 5] [Article Influence: 7.0] [Reference Citation Analysis]
15 D'Onofrio M, De Robertis R, Aluffi G, Cadore C, Beleù A, Cardobi N, Malleo G, Manfrin E, Bassi C. CT Simplified Radiomic Approach to Assess the Metastatic Ductal Adenocarcinoma of the Pancreas. Cancers (Basel) 2021;13:1843. [PMID: 33924363 DOI: 10.3390/cancers13081843] [Reference Citation Analysis]
16 Hirata K, Tamaki N. Quantitative FDG PET Assessment for Oncology Therapy. Cancers (Basel) 2021;13:869. [PMID: 33669531 DOI: 10.3390/cancers13040869] [Reference Citation Analysis]
17 Pellat A, Cottereau AS, Palmieri LJ, Soyer P, Marchese U, Brezault C, Coriat R. Digestive Well-Differentiated Grade 3 Neuroendocrine Tumors: Current Management and Future Directions. Cancers (Basel) 2021;13:2448. [PMID: 34070035 DOI: 10.3390/cancers13102448] [Reference Citation Analysis]
18 Shi Z, Ma C, Huang X, Cao D. Magnetic Resonance Imaging Radiomics-Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study. J Magn Reson Imaging 2022. [PMID: 34997795 DOI: 10.1002/jmri.28048] [Reference Citation Analysis]
19 Li W, Xu C, Ye Z. Prediction of Pancreatic Neuroendocrine Tumor Grading Risk Based on Quantitative Radiomic Analysis of MR. Front Oncol 2021;11:758062. [PMID: 34868970 DOI: 10.3389/fonc.2021.758062] [Reference Citation Analysis]
20 Yoshikawa T, Takenaka D, Ohno Y. Editorial for “ MRI Radiomics‐Based Nomogram From Primary Tumor for Pretreatment Prediction of Peripancreatic Lymph Node Metastasis in Pancreatic Ductal Adenocarcinoma: A Multicenter Study”. Magnetic Resonance Imaging. [DOI: 10.1002/jmri.28090] [Reference Citation Analysis]