Literature DB >> 33550513

Artificial intelligence: a critical review of current applications in pancreatic imaging.

Maxime Barat1,2, Guillaume Chassagnon1,2, Anthony Dohan1,2, Sébastien Gaujoux2,3, Romain Coriat2,4, Christine Hoeffel5, Christophe Cassinotto6, Philippe Soyer7,8.   

Abstract

The applications of artificial intelligence (AI), including machine learning and deep learning, in the field of pancreatic disease imaging are rapidly expanding. AI can be used for the detection of pancreatic ductal adenocarcinoma and other pancreatic tumors but also for pancreatic lesion characterization. In this review, the basic of radiomics, recent developments and current results of AI in the field of pancreatic tumors are presented. Limitations and future perspectives of AI are discussed.

Entities:  

Keywords:  Artificial intelligence; Pancreatic neoplasms; Radiomics; Texture analysis

Mesh：

Year:  2021        PMID： 33550513     DOI： 10.1007/s11604-021-01098-5

Source DB:  PubMed          Journal:  Jpn J Radiol        ISSN： 1867-1071            Impact factor:   2.374


  45 in total

1.  A logical calculus of the ideas immanent in nervous activity. 1943.

Authors:  W S McCulloch; W Pitts
Journal:  Bull Math Biol       Date:  1990       Impact factor: 1.758

Review 2.  Recent technical development of artificial intelligence for diagnostic medical imaging.

Authors:  Norio Nakata
Journal:  Jpn J Radiol       Date:  2019-01-31       Impact factor: 2.374

3.  A primer for understanding radiology articles about machine learning and deep learning.

Authors:  Takeshi Nakaura; Toru Higaki; Kazuo Awai; Osamu Ikeda; Yasuyuki Yamashita
Journal:  Diagn Interv Imaging       Date:  2020-10-26       Impact factor: 4.026

4.  Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features.

Authors:  S Park; L C Chu; R H Hruban; B Vogelstein; K W Kinzler; A L Yuille; D F Fouladi; S Shayesteh; S Ghandili; C L Wolfgang; R Burkhart; J He; E K Fishman; S Kawamoto
Journal:  Diagn Interv Imaging       Date:  2020-04-08       Impact factor: 4.026

5.  Deep lessons learned: Radiology, oncology, pathology, and computer science experts unite around artificial intelligence to strive for earlier pancreatic cancer diagnosis.

Authors:  E M Weisberg; L C Chu; S Park; A L Yuille; K W Kinzler; B Vogelstein; E K Fishman
Journal:  Diagn Interv Imaging       Date:  2019-10-16       Impact factor: 4.026

6.  Canadian Radiology in the Age of Artificial Intelligence: A Golden Opportunity.

Authors:  Christian B van der Pol; Michael N Patlas
Journal:  Can Assoc Radiol J       Date:  2020-02-27       Impact factor: 2.248

7.  Radiomics for classification of bone mineral loss: A machine learning study.

Authors:  S Rastegar; M Vaziri; Y Qasempour; M R Akhash; N Abdalvand; I Shiri; H Abdollahi; H Zaidi
Journal:  Diagn Interv Imaging       Date:  2020-02-04       Impact factor: 4.026

8.  Morphological imaging and CT histogram analysis to differentiate pancreatic neuroendocrine tumor grade 3 from neuroendocrine carcinoma.

Authors:  A Azoulay; J Cros; M-P Vullierme; L de Mestier; A Couvelard; O Hentic; P Ruszniewski; A Sauvanet; V Vilgrain; M Ronot
Journal:  Diagn Interv Imaging       Date:  2020-07-21       Impact factor: 4.026

9.  Performance of deep learning for differentiating pancreatic diseases on contrast-enhanced magnetic resonance imaging: A preliminary study.

Authors:  X Gao; X Wang
Journal:  Diagn Interv Imaging       Date:  2019-07-30       Impact factor: 4.026

10.  Pure and Hybrid Deep Learning Models can Predict Pathologic Tumor Response to Neoadjuvant Therapy in Pancreatic Adenocarcinoma: A Pilot Study.

Authors:  Michael D Watson; Maria R Baimas-George; Keith J Murphy; Ryan C Pickens; David A Iannitti; John B Martinie; Erin H Baker; Dionisios Vrochides; Lee M Ocuin
Journal:  Am Surg       Date:  2020-12-31       Impact factor: 0.688
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  6 in total

Review 1.  Radiomics in breast MRI: current progress toward clinical application in the era of artificial intelligence.

Authors:  Hiroko Satake; Satoko Ishigaki; Rintaro Ito; Shinji Naganawa
Journal:  Radiol Med       Date:  2021-10-26       Impact factor: 3.469

Review 2.  Artificial intelligence for the detection of pancreatic lesions.

Authors:  Julia Arribas Anta; Iván Martínez-Ballestero; Daniel Eiroa; Javier García; Júlia Rodríguez-Comas
Journal:  Int J Comput Assist Radiol Surg       Date:  2022-08-11       Impact factor: 3.421

3.  Three-dimensional conditional generative adversarial network-based virtual thin-slice technique for the morphological evaluation of the spine.

Authors:  Atsushi Nakamoto; Masatoshi Hori; Hiromitsu Onishi; Takashi Ota; Hideyuki Fukui; Kazuya Ogawa; Jun Masumoto; Akira Kudo; Yoshiro Kitamura; Shoji Kido; Noriyuki Tomiyama
Journal:  Sci Rep       Date:  2022-07-16       Impact factor: 4.996

4.  EASY-APP: An artificial intelligence model and application for early and easy prediction of severity in acute pancreatitis.

Authors:  Balázs Kui; József Pintér; Roland Molontay; Marcell Nagy; Nelli Farkas; Noémi Gede; Áron Vincze; Judit Bajor; Szilárd Gódi; József Czimmer; Imre Szabó; Anita Illés; Patrícia Sarlós; Roland Hágendorn; Gabriella Pár; Mária Papp; Zsuzsanna Vitális; György Kovács; Eszter Fehér; Ildikó Földi; Ferenc Izbéki; László Gajdán; Roland Fejes; Balázs Csaba Németh; Imola Török; Hunor Farkas; Artautas Mickevicius; Ville Sallinen; Shamil Galeev; Elena Ramírez-Maldonado; Andrea Párniczky; Bálint Erőss; Péter Jenő Hegyi; Katalin Márta; Szilárd Váncsa; Robert Sutton; Peter Szatmary; Diane Latawiec; Chris Halloran; Enrique de-Madaria; Elizabeth Pando; Piero Alberti; Maria José Gómez-Jurado; Alina Tantau; Andrea Szentesi; Péter Hegyi
Journal:  Clin Transl Med       Date:  2022-06

Review 5.  Adrenal Mass Characterization in the Era of Quantitative Imaging: State of the Art.

Authors:  Maxime Barat; Anne-Ségolène Cottereau; Sébastien Gaujoux; Florence Tenenbaum; Mathilde Sibony; Jérôme Bertherat; Rossella Libé; Martin Gaillard; Anne Jouinot; Guillaume Assié; Christine Hoeffel; Philippe Soyer; Anthony Dohan
Journal:  Cancers (Basel)       Date:  2022-01-23       Impact factor: 6.639

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

Authors:  Hua Yin; Feixiong Zhang; Xiaoli Yang; Xiangkun Meng; Yu Miao; Muhammad Saad Noor Hussain; Li Yang; Zhaoshen Li
Journal:  Front Oncol       Date:  2022-08-02       Impact factor: 5.738
  6 in total

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