Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis

被引:19
作者
Yin, Hua [1 ,2 ]
Zhang, Feixiong [1 ]
Yang, Xiaoli [1 ]
Meng, Xiangkun [1 ]
Miao, Yu [1 ]
Hussain, Muhammad Saad Noor [3 ]
Yang, Li [1 ]
Li, Zhaoshen [2 ,4 ]
机构
[1] Ningxia Med Univ, Dept Gastroenterol, Gen Hosp, Yinchuan, Peoples R China
[2] Ningxia Med Univ, Postgrad Training Base Shanghai Gongli Hosp, Shanghai, Peoples R China
[3] Dar Ul Shafa Hosp Sialkot, Dept Anesthesia, Punjab, Pakistan
[4] Ningxia Med Univ, Clin Med Coll, Yinchuan, Peoples R China
关键词
Artificial intelligence; pancreatic cancer; AI; bibliometric; trends; DIGITAL-IMAGE-ANALYSIS; DIFFERENTIAL-DIAGNOSIS; NEURAL-NETWORKS; EUS; ADENOCARCINOMA; CLASSIFICATION; SEGMENTATION; VALIDATION; PREDICTION; DEATHS;
D O I
10.3389/fonc.2022.973999
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
PurposeWe evaluated the related research on artificial intelligence (AI) in pancreatic cancer (PC) through bibliometrics analysis and explored the research hotspots and current status from 1997 to 2021. MethodsPublications related to AI in PC were retrieved from the Web of Science Core Collection (WoSCC) during 1997-2021. Bibliometrix package of R software 4.0.3 and VOSviewer were used to bibliometrics analysis. ResultsA total of 587 publications in this field were retrieved from WoSCC database. After 2018, the number of publications grew rapidly. The United States and Johns Hopkins University were the most influential country and institution, respectively. A total of 2805 keywords were investigated, 81 of which appeared more than 10 times. Co-occurrence analysis categorized these keywords into five types of clusters: (1) AI in biology of PC, (2) AI in pathology and radiology of PC, (3) AI in the therapy of PC, (4) AI in risk assessment of PC and (5) AI in endoscopic ultrasonography (EUS) of PC. Trend topics and thematic maps show that keywords " diagnosis ", "survival", "classification", and "management" are the research hotspots in this field. ConclusionThe research related to AI in pancreatic cancer is still in the initial stage. Currently, AI is widely studied in biology, diagnosis, treatment, risk assessment, and EUS of pancreatic cancer. This bibliometrics study provided an insight into AI in PC research and helped researchers identify new research orientations.
引用
收藏
页数:13
相关论文
共 65 条
[1]   Development and validation of a pancreatic cancer risk model for the general population using electronic health records: An observational study [J].
Appelbaum, Limor ;
Cambronero, Jose P. ;
Stevens, Jennifer P. ;
Horng, Steven ;
Pollick, Karla ;
Silva, George ;
Haneuse, Sebastien ;
Piatkowski, Gail ;
Benhaga, Nordine ;
Duey, Stacey ;
Stevenson, Mary A. ;
Mamon, Harvey ;
Kaplan, Irving D. ;
Rinard, Martin C. .
EUROPEAN JOURNAL OF CANCER, 2021, 143 :19-30
[2]   Artificial intelligence: a critical review of current applications in pancreatic imaging [J].
Barat, Maxime ;
Chassagnon, Guillaume ;
Dohan, Anthony ;
Gaujoux, Sebastien ;
Coriat, Romain ;
Hoeffel, Christine ;
Cassinotto, Christophe ;
Soyer, Philippe .
JAPANESE JOURNAL OF RADIOLOGY, 2021, 39 (06) :514-523
[3]   Fully Convolutional Neural Networks Improve Abdominal Organ Segmentation [J].
Bobo, Meg F. ;
Bao, Shunxing ;
Huo, Yuankai ;
Yao, Yuang ;
Virostko, Jack ;
Plassard, Andrew J. ;
Lyu, Ilwoo ;
Assad, Albert ;
Abramson, Richard G. ;
Hilmes, Melissa A. ;
Landman, Bennett A. .
MEDICAL IMAGING 2018: IMAGE PROCESSING, 2018, 10574
[4]   Fast and robust online adaptive planning in stereotactic MR-guided adaptive radiation therapy (SMART) for pancreatic cancer [J].
Bohoudi, O. ;
Bruynzeel, A. M. E. ;
Senan, S. ;
Cuijpers, J. P. ;
Slotman, B. J. ;
Lagerwaard, F. J. ;
Palacios, M. A. .
RADIOTHERAPY AND ONCOLOGY, 2017, 125 (03) :439-444
[5]   A clinical prediction model to assess risk for pancreatic cancer among patients with prediabetes [J].
Boursi, Ben ;
Finkelman, Brian ;
Giantonio, Bruce J. ;
Haynes, Kevin ;
Rustgi, Anil K. ;
Rhim, Andrew D. ;
Mamtani, Ronac ;
Yang, Yu-Xiao .
EUROPEAN JOURNAL OF GASTROENTEROLOGY & HEPATOLOGY, 2022, 34 (01) :33-38
[6]   A Clinical Prediction Model to Assess Risk for Pancreatic Cancer Among Patients With New-Onset Diabetes [J].
Boursi, Ben ;
Finkelman, Brian ;
Giantonio, Bruce J. ;
Haynes, Kevin ;
Rustgi, Anil K. ;
Rhim, Andrew D. ;
Mamtani, Ronac ;
Yang, Yu-Xiao .
GASTROENTEROLOGY, 2017, 152 (04) :840-+
[7]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[8]   Neural network dose models for knowledge-based planning in pancreatic SBRT [J].
Campbell, Warren G. ;
Miften, Moyed ;
Olsen, Lindsey ;
Stumpf, Priscilla ;
Schefter, Tracey ;
Goodman, Karyn A. ;
Jones, Bernard L. .
MEDICAL PHYSICS, 2017, 44 (12) :6148-6158
[9]   Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology [J].
Cheng, Jerome Y. ;
Abel, Jacob T. ;
Balis, Ulysses G. J. ;
McClintock, David S. ;
Pantanowitz, Liron .
AMERICAN JOURNAL OF PATHOLOGY, 2021, 191 (10) :1684-1692
[10]   Genome-wide cell-free DNA fragmentation in patients with cancer [J].
Cristiano, Stephen ;
Leal, Alessandro ;
Phallen, Jillian ;
Fiksel, Jacob ;
Adleff, Vilmos ;
Bruhm, Daniel C. ;
Jensen, Sarah Ostrup ;
Medina, Jamie E. ;
Hruban, Carolyn ;
White, James R. ;
Palsgrove, Doreen N. ;
Niknafs, Noushin ;
Anagnostou, Valsamo ;
Forde, Patrick ;
Naidoo, Jarushka ;
Marrone, Kristen ;
Brahmer, Julie ;
Woodward, Brian D. ;
Husain, Hatim ;
van Rooijen, Karlijn L. ;
Orntoft, Mai-Britt Worm ;
Madsen, Anders Husted ;
van de Velde, Cornelis J. H. ;
Verheij, Marcel ;
Cats, Annemieke ;
Punt, Cornelis J. A. ;
Vink, Geraldine R. ;
van Grieken, Nicole C. T. ;
Koopman, Miriam ;
Fijneman, Remond J. A. ;
Johansen, Julia S. ;
Nielsen, Hans Jorgen ;
Meijer, Gerrit A. ;
Andersen, Claus Lindbjerg ;
Scharpf, Robert B. ;
Velculescu, Victor E. .
NATURE, 2019, 570 (7761) :385-+