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Cited by in CrossRef
For: Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol 2023; 29(12): 1811-1823 [PMID: 37032728 DOI: 10.3748/wjg.v29.i12.1811]
URL: https://www.wjgnet.com/1007-9327/full/v29/i12/1811.htm
Number Citing Articles
1
冠宇 吴. To Compare the Stability of Different Clinical Model Frameworks in the Overall Survival Rate and Specific Survival Rate of Patients with Non-Metastatic Pancreatic Head Cancer, Pancreatic Body Cancer and Pancreatic Tail Cancer Based on Machine LearningAdvances in Clinical Medicine 2023; 13(11): 18150 doi: 10.12677/ACM.2023.13112547
2
Giulia Pacella, Maria Chiara Brunese, Eleonora D’Imperio, Marco Rotondo, Andrea Scacchi, Mattia Carbone, Germano Guerra. Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and PrognosisJournal of Clinical Medicine 2023; 12(23): 7380 doi: 10.3390/jcm12237380
3
Xing Ke, Xinyu Cai, Bingxian Bian, Yuanheng Shen, Yunlan Zhou, Wei Liu, Xu Wang, Lisong Shen, Junyao Yang. Predicting early gastric cancer risk using machine learning: A population-based retrospective studyDIGITAL HEALTH 2024; 10 doi: 10.1177/20552076241240905
4
Hardik Patel, Theodoros Zanos, D. Brock Hewitt. Deep Learning Applications in Pancreatic CancerCancers 2024; 16(2): 436 doi: 10.3390/cancers16020436
5
Cristian Anghel, Mugur Cristian Grasu, Denisa Andreea Anghel, Gina-Ionela Rusu-Munteanu, Radu Lucian Dumitru, Ioana Gabriela Lupescu. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI ImagesDiagnostics 2024; 14(4): 438 doi: 10.3390/diagnostics14040438