Literature DB >> 31280350

Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications.

Dimitris Visvikis1, Catherine Cheze Le Rest2,3, Vincent Jaouen2, Mathieu Hatt2.   

Abstract

Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstruction, processing (denoising, segmentation), analysis and predictive modelling. In this review we introduce and define these key concepts and discuss how the techniques from this field can be applied to nuclear medicine imaging applications with a particular focus on radio(geno)mics.

Keywords:  Artificial intelligence; Deep learning; Machine learning; Radiogenomics; Radiomics

Year:  2019        PMID: 31280350     DOI: 10.1007/s00259-019-04373-w

Source DB:  PubMed          Journal:  Eur J Nucl Med Mol Imaging        ISSN: 1619-7070            Impact factor:   9.236


  52 in total

Review 1.  Innovations in Instrumentation for Positron Emission Tomography.

Authors:  Eric Berg; Simon R Cherry
Journal:  Semin Nucl Med       Date:  2018-03-12       Impact factor: 4.446

2.  Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211.

Authors:  Mathieu Hatt; John A Lee; Charles R Schmidtlein; Issam El Naqa; Curtis Caldwell; Elisabetta De Bernardi; Wei Lu; Shiva Das; Xavier Geets; Vincent Gregoire; Robert Jeraj; Michael P MacManus; Osama R Mawlawi; Ursula Nestle; Andrei B Pugachev; Heiko Schöder; Tony Shepherd; Emiliano Spezi; Dimitris Visvikis; Habib Zaidi; Assen S Kirov
Journal:  Med Phys       Date:  2017-05-18       Impact factor: 4.071

3.  Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study.

Authors:  Sydney Kaplan; Yang-Ming Zhu
Journal:  J Digit Imaging       Date:  2019-10       Impact factor: 4.056

4.  Automated classification of benign and malignant lesions in 18F-NaF PET/CT images using machine learning.

Authors:  Timothy Perk; Tyler Bradshaw; Song Chen; Hyung-Jun Im; Steve Cho; Scott Perlman; Glenn Liu; Robert Jeraj
Journal:  Phys Med Biol       Date:  2018-11-20       Impact factor: 3.609

5.  The biology underlying molecular imaging in oncology: from genome to anatome and back again.

Authors:  R J Gillies; A R Anderson; R A Gatenby; D L Morse
Journal:  Clin Radiol       Date:  2010-07       Impact factor: 2.350

6.  The first MICCAI challenge on PET tumor segmentation.

Authors:  Mathieu Hatt; Baptiste Laurent; Anouar Ouahabi; Hadi Fayad; Shan Tan; Laquan Li; Wei Lu; Vincent Jaouen; Clovis Tauber; Jakub Czakon; Filip Drapejkowski; Witold Dyrka; Sorina Camarasu-Pop; Frédéric Cervenansky; Pascal Girard; Tristan Glatard; Michael Kain; Yao Yao; Christian Barillot; Assen Kirov; Dimitris Visvikis
Journal:  Med Image Anal       Date:  2017-12-09       Impact factor: 8.545

7.  Machine Learning methods for Quantitative Radiomic Biomarkers.

Authors:  Chintan Parmar; Patrick Grossmann; Johan Bussink; Philippe Lambin; Hugo J W L Aerts
Journal:  Sci Rep       Date:  2015-08-17       Impact factor: 4.379

8.  Automatic lesion detection and segmentation of 18F-FET PET in gliomas: A full 3D U-Net convolutional neural network study.

Authors:  Paul Blanc-Durand; Axel Van Der Gucht; Niklaus Schaefer; Emmanuel Itti; John O Prior
Journal:  PLoS One       Date:  2018-04-13       Impact factor: 3.240

9.  Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study.

Authors:  Ahmed Hosny; Chintan Parmar; Thibaud P Coroller; Patrick Grossmann; Roman Zeleznik; Avnish Kumar; Johan Bussink; Robert J Gillies; Raymond H Mak; Hugo J W L Aerts
Journal:  PLoS Med       Date:  2018-11-30       Impact factor: 11.069

Review 10.  Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats.

Authors:  Sandy Napel; Wei Mu; Bruna V Jardim-Perassi; Hugo J W L Aerts; Robert J Gillies
Journal:  Cancer       Date:  2018-11-01       Impact factor: 6.860

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  22 in total

1.  EJNMMI supplement: bringing AI and radiomics to nuclear medicine.

Authors:  Patrick Veit-Haibach; Irène Buvat; Ken Herrmann
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-12       Impact factor: 9.236

Review 2.  Applications of artificial intelligence in nuclear medicine image generation.

Authors:  Zhibiao Cheng; Junhai Wen; Gang Huang; Jianhua Yan
Journal:  Quant Imaging Med Surg       Date:  2021-06

Review 3.  Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.

Authors:  Dimitris Visvikis; Philippe Lambin; Kim Beuschau Mauridsen; Roland Hustinx; Michael Lassmann; Christoph Rischpler; Kuangyu Shi; Jan Pruim
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-07-09       Impact factor: 9.236

Review 4.  Machine Learning Algorithms for Predicting Surgical Outcomes after Colorectal Surgery: A Systematic Review.

Authors:  Mustafa Bektaş; Jurriaan B Tuynman; Jaime Costa Pereira; George L Burchell; Donald L van der Peet
Journal:  World J Surg       Date:  2022-09-15       Impact factor: 3.282

5.  A deep learning model combining multimodal radiomics, clinical and imaging features for differentiating ocular adnexal lymphoma from idiopathic orbital inflammation.

Authors:  Xiaoyang Xie; Lijuan Yang; Fengjun Zhao; Dong Wang; Hui Zhang; Xuelei He; Xin Cao; Huangjian Yi; Xiaowei He; Yuqing Hou
Journal:  Eur Radiol       Date:  2022-06-08       Impact factor: 7.034

6.  A Prognostic Model of Non-Small Cell Lung Cancer With a Radiomics Nomogram in an Eastern Chinese Population.

Authors:  Lijie Wang; Ailing Liu; Zhiheng Wang; Ning Xu; Dandan Zhou; Tao Qu; Guiyuan Liu; Jingtao Wang; Fujun Yang; Xiaolei Guo; Weiwei Chi; Fuzhong Xue
Journal:  Front Oncol       Date:  2022-06-14       Impact factor: 5.738

Review 7.  Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Authors:  Sanjay Saxena; Biswajit Jena; Neha Gupta; Suchismita Das; Deepaneeta Sarmah; Pallab Bhattacharya; Tanmay Nath; Sudip Paul; Mostafa M Fouda; Manudeep Kalra; Luca Saba; Gyan Pareek; Jasjit S Suri
Journal:  Cancers (Basel)       Date:  2022-06-09       Impact factor: 6.575

8.  18F-FDOPA PET for the Noninvasive Prediction of Glioma Molecular Parameters: A Radiomics Study.

Authors:  Timothée Zaragori; Julien Oster; Véronique Roch; Gabriela Hossu; Mohammad B Chawki; Rachel Grignon; Celso Pouget; Guillaume Gauchotte; Fabien Rech; Marie Blonski; Luc Taillandier; Laëtitia Imbert; Antoine Verger
Journal:  J Nucl Med       Date:  2021-05-20       Impact factor: 11.082

9.  Quantitative PET in the 2020s: a roadmap.

Authors:  Steven R Meikle; Vesna Sossi; Emilie Roncali; Simon R Cherry; Richard Banati; David Mankoff; Terry Jones; Michelle James; Julie Sutcliffe; Jinsong Ouyang; Yoann Petibon; Chao Ma; Georges El Fakhri; Suleman Surti; Joel S Karp; Ramsey D Badawi; Taiga Yamaya; Go Akamatsu; Georg Schramm; Ahmadreza Rezaei; Johan Nuyts; Roger Fulton; André Kyme; Cristina Lois; Hasan Sari; Julie Price; Ronald Boellaard; Robert Jeraj; Dale L Bailey; Enid Eslick; Kathy P Willowson; Joyita Dutta
Journal:  Phys Med Biol       Date:  2021-03-12       Impact factor: 4.174

Review 10.  Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Eur J Hybrid Imaging       Date:  2020-09-23
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