open access

Vol 23, No 1 (2020)
Reviews
Published online: 2020-01-31
Submitted: 2019-09-20
Accepted: 2019-12-19
Get Citation

Radiomics and artificial Intelligence for PET imaging analysis

Andrea d'Amico, Damian Borys, Izabela Gorczewska
DOI: 10.5603/NMR.2020.0005
·
Pubmed: 32779173
·
Nucl. Med. Rev 2020;23(1):36-39.

open access

Vol 23, No 1 (2020)
Reviews
Published online: 2020-01-31
Submitted: 2019-09-20
Accepted: 2019-12-19

Abstract

In recent years, processing of the imaging signal derived from CT, MR or positron emission has proven to be able to predict outcome parameters in cancer patients. The processing techniques of the signal constitute the discipline of radiomics. The quantitative analysis of medical images outperform the information that can be obtained through traditional visual analysis. The recognition of neoplasm molecular and genetic characteristics in a non-invasive way, based on routine radiological examinations, potentially allow complete tumor profiling and subsequent treatment customization at practically zero costs. This process is further boosted with the availability of increased computing power and development of artificial intelligence approaches.

Abstract

In recent years, processing of the imaging signal derived from CT, MR or positron emission has proven to be able to predict outcome parameters in cancer patients. The processing techniques of the signal constitute the discipline of radiomics. The quantitative analysis of medical images outperform the information that can be obtained through traditional visual analysis. The recognition of neoplasm molecular and genetic characteristics in a non-invasive way, based on routine radiological examinations, potentially allow complete tumor profiling and subsequent treatment customization at practically zero costs. This process is further boosted with the availability of increased computing power and development of artificial intelligence approaches.

Get Citation

Keywords

Radiomics; Positron emission tomography; Artificial Intelligence

About this article
Title

Radiomics and artificial Intelligence for PET imaging analysis

Journal

Nuclear Medicine Review

Issue

Vol 23, No 1 (2020)

Pages

36-39

Published online

2020-01-31

DOI

10.5603/NMR.2020.0005

Pubmed

32779173

Bibliographic record

Nucl. Med. Rev 2020;23(1):36-39.

Keywords

Radiomics
Positron emission tomography
Artificial Intelligence

Authors

Andrea d'Amico
Damian Borys
Izabela Gorczewska

References (24)
  1. Ha S, Choi H, Paeng JC, et al. Radiomics in Oncological PET/CT: a Methodological Overview. Nucl Med Mol Imaging. 2019; 53(1): 14–29.
  2. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016; 278(2): 563–577.
  3. Scalco E, Rizzo G. Texture analysis of medical images for radiotherapy applications. Br J Radiol. 2017; 90(1070): 20160642.
  4. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017; 14(12): 749–762.
  5. Cook GJR, Azad G, Owczarczyk K, et al. Challenges and Promises of PET Radiomics. Int J Radiat Oncol Biol Phys. 2018; 102(4): 1083–1089.
  6. Papanikolaou N, Santinha J. An introduction to radiomics: capturing tumour biology in space and time. Hell J Radiol 2018; 3 (1): 61–71. http://www.hjradiology.org/index.php/HJR/article/view/210.
  7. Sanduleanu S, Woodruff HC, de Jong EEC, et al. Tracking tumor biology with radiomics: A systematic review utilizing a radiomics quality score. Radiother Oncol. 2018; 127(3): 349–360.
  8. http://earl.eanm.org/cms/website.php?id=/en/projects.htm.
  9. Papp L, Rausch I, Grahovac M, et al. Optimized Feature Extraction for Radiomics Analysis of F-FDG PET Imaging. J Nucl Med. 2019; 60(6): 864–872.
  10. Brooks FJ, Grigsby PW. Quantification of heterogeneity observed in medical images. BMC Med Imaging. 2013; 13: 7.
  11. Berthon B, Spezi E, Galavis P, et al. Toward a standard for the evaluation of PET-Auto-Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation. Med Phys. 2017; 44(8): 4098–4111.
  12. Hatt M, Lee JA, Schmidtlein CR, et al. Classification and evaluation strategies of auto-segmentation approaches for PET: Report of AAPM task group No. 211. Med Phys. 2017; 44(6): e1–e42.
  13. Lovinfosse P, Visvikis D, Hustinx R, et al. FDG PET radiomics: a review of the methodological aspects. Clinical and Translational Imaging. 2018; 6(5): 379–391.
  14. Yu H, Caldwell C, Mah K, et al. Automated radiation targeting in head-and-neck cancer using region-based texture analysis of PET and CT images. Int J Radiat Oncol Biol Phys. 2009; 75(2): 618–625.
  15. Yu H, Caldwell C, Mah K, et al. Coregistered FDG PET/CT-based textural characterization of head and neck cancer for radiation treatment planning. IEEE Trans Med Imaging. 2009; 28(3): 374–383.
  16. Vaidya M, Creach KM, Frye J, et al. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012; 102(2): 239–245.
  17. Eary JF, O'Sullivan F, O'Sullivan J, et al. Spatial heterogeneity in sarcoma 18F-FDG uptake as a predictor of patient outcome. J Nucl Med. 2008; 49(12): 1973–1979.
  18. El Naqa I, Grigsby P, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit. 2009; 42(6): 1162–1171.
  19. Cook GJR, Yip C, Siddique M, et al. Are pretreatment 18F-FDG PET tumor textural features in non-small cell lung cancer associated with response and survival after chemoradiotherapy? J Nucl Med. 2013; 54(1): 19–26.
  20. Miwa K, Inubushi M, Wagatsuma K, et al. FDG uptake heterogeneity evaluated by fractal analysis improves the differential diagnosis of pulmonary nodules. Eur J Radiol. 2014; 83(4): 715–719.
  21. van Helden P. Data-driven hypotheses. EMBO Rep. 2013; 14(2): 104.
  22. Allen JF. In silico veritas. Data-mining and automated discovery: the truth is in there. EMBO Rep. 2001; 2(7): 542–544.
  23. Osunwusi A. Aviation Automation and CNS/ATM-related Human-Technology Interface: ATSEP Competency Considerations. International Journal of Aviation, Aeronautics, and Aerospace. 2019.
  24. Coroller TP, Bi WL, Huynh E, et al. Radiographic prediction of meningioma grade by semantic and radiomic features. PLoS One. 2017; 12(11): e0187908.

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