Literature DB >> 30374837

Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis.

Hidetaka Arimura1, Mazen Soufi2, Kenta Ninomiya3, Hidemi Kamezawa4, Masahiro Yamada3.   

Abstract

Computer-aided diagnosis (CAD) is a field that is essentially based on pattern recognition that improves the accuracy of a diagnosis made by a physician who takes into account the computer's "opinion" derived from the quantitative analysis of radiological images. Radiomics is a field based on data science that massively and comprehensively analyzes a large number of medical images to extract a large number of phenotypic features reflecting disease traits, and explores the associations between the features and patients' prognoses for precision medicine. According to the definitions for both, you may think that radiomics is not a paraphrase of CAD, but you may also think that these definitions are "image manipulation". However, there are common and different features between the two fields. This review paper elaborates on these common and different features and introduces the potential of radiomics for cancer diagnosis and treatment by comparing it with CAD.

Entities:  

Keywords:  Cancer diagnosis and treatment; Computer-aided diagnosis; Precision medicine; Radiomics

Mesh:

Year:  2018        PMID: 30374837     DOI: 10.1007/s12194-018-0486-x

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  51 in total

Review 1.  Computer-aided diagnosis and artificial intelligence in clinical imaging.

Authors:  Junji Shiraishi; Qiang Li; Daniel Appelbaum; Kunio Doi
Journal:  Semin Nucl Med       Date:  2011-11       Impact factor: 4.446

Review 2.  Current status and future potential of computer-aided diagnosis in medical imaging.

Authors:  K Doi
Journal:  Br J Radiol       Date:  2005       Impact factor: 3.039

3.  A comparison of wavelet, ridgelet, and curvelet-based texture classification algorithms in computed tomography.

Authors:  Lucia Dettori; Lindsay Semler
Journal:  Comput Biol Med       Date:  2006-10-18       Impact factor: 4.589

4.  Predicting hypoxia status using a combination of contrast-enhanced computed tomography and [18F]-Fluorodeoxyglucose positron emission tomography radiomics features.

Authors:  Mireia Crispin-Ortuzar; Aditya Apte; Milan Grkovski; Jung Hun Oh; Nancy Y Lee; Heiko Schöder; John L Humm; Joseph O Deasy
Journal:  Radiother Oncol       Date:  2017-12-19       Impact factor: 6.280

5.  Computer aided prognosis for cell death categorization and prediction in vivo using quantitative ultrasound and machine learning techniques.

Authors:  M J Gangeh; A Hashim; A Giles; L Sannachi; G J Czarnota
Journal:  Med Phys       Date:  2016-12       Impact factor: 4.071

Review 6.  Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment.

Authors:  Katja Pinker; Joanne Chin; Amy N Melsaether; Elizabeth A Morris; Linda Moy
Journal:  Radiology       Date:  2018-06       Impact factor: 11.105

7.  Intratumor heterogeneity and branched evolution revealed by multiregion sequencing.

Authors:  Marco Gerlinger; Andrew J Rowan; Stuart Horswell; James Larkin; David Endesfelder; Eva Gronroos; Pierre Martinez; Nicholas Matthews; Aengus Stewart; Charles Swanton; M Math; Patrick Tarpey; Ignacio Varela; Benjamin Phillimore; Sharmin Begum; Neil Q McDonald; Adam Butler; David Jones; Keiran Raine; Calli Latimer; Claudio R Santos; Mahrokh Nohadani; Aron C Eklund; Bradley Spencer-Dene; Graham Clark; Lisa Pickering; Gordon Stamp; Martin Gore; Zoltan Szallasi; Julian Downward; P Andrew Futreal
Journal:  N Engl J Med       Date:  2012-03-08       Impact factor: 91.245

8.  2D and 3D CT Radiomics Features Prognostic Performance Comparison in Non-Small Cell Lung Cancer.

Authors:  Chen Shen; Zhenyu Liu; Min Guan; Jiangdian Song; Yucheng Lian; Shuo Wang; Zhenchao Tang; Di Dong; Lingfei Kong; Meiyun Wang; Dapeng Shi; Jie Tian
Journal:  Transl Oncol       Date:  2017-09-18       Impact factor: 4.243

9.  Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia.

Authors:  Hubert S Gabryś; Florian Buettner; Florian Sterzing; Henrik Hauswald; Mark Bangert
Journal:  Front Oncol       Date:  2018-03-05       Impact factor: 6.244

Review 10.  Radiomics with artificial intelligence for precision medicine in radiation therapy.

Authors:  Hidetaka Arimura; Mazen Soufi; Hidemi Kamezawa; Kenta Ninomiya; Masahiro Yamada
Journal:  J Radiat Res       Date:  2019-01-01       Impact factor: 2.724

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  4 in total

1.  Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features.

Authors:  Claudia-Gabriela Moldovanu; Bianca Boca; Andrei Lebovici; Attila Tamas-Szora; Diana Sorina Feier; Nicolae Crisan; Iulia Andras; Mircea Marian Buruian
Journal:  J Pers Med       Date:  2020-12-23

2.  Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients.

Authors:  Quoc Cuong Le; Hidetaka Arimura; Kenta Ninomiya; Yutaro Kabata
Journal:  Sci Rep       Date:  2020-12-04       Impact factor: 4.379

Review 3.  Radiomics and Digital Image Texture Analysis in Oncology (Review).

Authors:  A A Litvin; D A Burkin; A A Kropinov; F N Paramzin
Journal:  Sovrem Tekhnologii Med       Date:  2021-01-01

Review 4.  Diffusion-weighted imaging of the breast: current status as an imaging biomarker and future role.

Authors:  Julia Camps-Herrero
Journal:  BJR Open       Date:  2019-03-08
  4 in total

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