Literature DB >> 33170144

Artificial intelligence in musculoskeletal oncological radiology.

Matjaz Vogrin1,2, Teodor Trojner1, Robi Kelc1,2.   

Abstract

Background Due to the rarity of primary bone tumors, precise radiologic diagnosis often requires an experienced musculoskeletal radiologist. In order to make the diagnosis more precise and to prevent the overlooking of potentially dangerous conditions, artificial intelligence has been continuously incorporated into medical practice in recent decades. This paper reviews some of the most promising systems developed, including those for diagnosis of primary and secondary bone tumors, breast, lung and colon neoplasms. Conclusions Although there is still a shortage of long-term studies confirming its benefits, there is probably a considerable potential for further development of computer-based expert systems aiming at a more efficient diagnosis of bone and soft tissue tumors.

Keywords:  artificial intelligence; cancer imaging; deep learning; image segmentation; tumor recognition

Year:  2020        PMID: 33170144     DOI: 10.2478/raon-2020-0068

Source DB:  PubMed          Journal:  Radiol Oncol        ISSN: 1318-2099            Impact factor:   2.991


  48 in total

1.  Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.

Authors:  Andrew H Beck; Ankur R Sangoi; Samuel Leung; Robert J Marinelli; Torsten O Nielsen; Marc J van de Vijver; Robert B West; Matt van de Rijn; Daphne Koller
Journal:  Sci Transl Med       Date:  2011-11-09       Impact factor: 17.956

Review 2.  CAD: how it works, how to use it, performance.

Authors:  Daniele Regge; Steve Halligan
Journal:  Eur J Radiol       Date:  2012-05-16       Impact factor: 3.528

3.  Application of an artificial neural network in radiographic diagnosis.

Authors:  D W Piraino; S C Amartur; B J Richmond; J P Schils; J M Thome; G H Belhobek; M D Schlucter
Journal:  J Digit Imaging       Date:  1991-11       Impact factor: 4.056

4.  Deep-Learning Detection of Cancer Metastases to the Brain on MRI.

Authors:  Min Zhang; Geoffrey S Young; Huai Chen; Jing Li; Lei Qin; J Ricardo McFaline-Figueroa; David A Reardon; Xinhua Cao; Xian Wu; Xiaoyin Xu
Journal:  J Magn Reson Imaging       Date:  2020-03-13       Impact factor: 4.813

Review 5.  Computer-aided diagnosis: A survey with bibliometric analysis.

Authors:  Ryohei Takahashi; Yuya Kajikawa
Journal:  Int J Med Inform       Date:  2017-02-17       Impact factor: 4.046

6.  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

Review 7.  Radiography in the initial diagnosis of primary bone tumors.

Authors:  Colleen M Costelloe; John E Madewell
Journal:  AJR Am J Roentgenol       Date:  2013-01       Impact factor: 3.959

8.  Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring.

Authors:  Michiel Kallenberg; Kersten Petersen; Mads Nielsen; Andrew Y Ng; Christian Igel; Celine M Vachon; Katharina Holland; Rikke Rass Winkel; Nico Karssemeijer; Martin Lillholm
Journal:  IEEE Trans Med Imaging       Date:  2016-02-18       Impact factor: 10.048

9.  Determining growth rates of focal lesions of bone from radiographs.

Authors:  G S Lodwick; A J Wilson; C Farrell; P Virtama; F Dittrich
Journal:  Radiology       Date:  1980-03       Impact factor: 11.105

Review 10.  Artificial intelligence in cancer imaging: Clinical challenges and applications.

Authors:  Wenya Linda Bi; Ahmed Hosny; Matthew B Schabath; Maryellen L Giger; Nicolai J Birkbak; Alireza Mehrtash; Tavis Allison; Omar Arnaout; Christopher Abbosh; Ian F Dunn; Raymond H Mak; Rulla M Tamimi; Clare M Tempany; Charles Swanton; Udo Hoffmann; Lawrence H Schwartz; Robert J Gillies; Raymond Y Huang; Hugo J W L Aerts
Journal:  CA Cancer J Clin       Date:  2019-02-05       Impact factor: 508.702

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