Radiomics for Radiation Oncologists: Are We Ready to Go?

Overview

Journal BJR Open

Publisher Oxford University Press

Specialty Radiology

Date 2020 Nov 12

PMID 33178967

Citations 9

Authors

Loig Vaugier

Ludovic Ferrer

Laurence Mengue

Emmanuel Jouglar

Affiliations

Soon will be listed here.

Abstract

Radiomics have emerged as an exciting field of research over the past few years, with very wide potential applications in personalised and precision medicine of the future. Radiomics-based approaches are still however limited in daily clinical practice in oncology. This review focus on how radiomics could be incorporated into the radiation therapy pipeline, and globally help the radiation oncologist, from the tumour diagnosis to follow-up after treatment. Radiomics could impact on all steps of the treatment pipeline, once the limitations in terms of robustness and reproducibility are overcome. Major ongoing efforts should be made to collect and share data in the most standardised manner possible.

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References

van Griethuysen J, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V . Computational Radiomics System to Decode the Radiographic Phenotype. Cancer Res. 2017; 77(21):e104-e107. PMC: 5672828. DOI: 10.1158/0008-5472.CAN-17-0339. View

Teshima K, Murakami R, Tomitaka E, Nomura T, Toya R, Hiraki A . Radiation-induced parotid gland changes in oral cancer patients: correlation between parotid volume and saliva production. Jpn J Clin Oncol. 2009; 40(1):42-6. DOI: 10.1093/jjco/hyp113. View

Sala E, Mema E, Himoto Y, Veeraraghavan H, Brenton J, Snyder A . Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2016; 72(1):3-10. PMC: 5503113. DOI: 10.1016/j.crad.2016.09.013. View

Houweling A, Philippens M, Dijkema T, Roesink J, Terhaard C, Schilstra C . A comparison of dose-response models for the parotid gland in a large group of head-and-neck cancer patients. Int J Radiat Oncol Biol Phys. 2009; 76(4):1259-65. DOI: 10.1016/j.ijrobp.2009.07.1685. View

Yin Q, Hung S, Wang L, Lin W, Fielding J, Rathmell W . Associations between Tumor Vascularity, Vascular Endothelial Growth Factor Expression and PET/MRI Radiomic Signatures in Primary Clear-Cell-Renal-Cell-Carcinoma: Proof-of-Concept Study. Sci Rep. 2017; 7:43356. PMC: 5335708. DOI: 10.1038/srep43356. View

Lim-Reinders S, Keller B, Al-Ward S, Sahgal A, Kim A . Online Adaptive Radiation Therapy. Int J Radiat Oncol Biol Phys. 2017; 99(4):994-1003. DOI: 10.1016/j.ijrobp.2017.04.023. View

Briens A, Castelli J, Barateau A, Jaksic N, Gnep K, Simon A . [Adaptive radiotherapy: Strategies and benefits depending on tumor localization]. Cancer Radiother. 2019; 23(6-7):592-608. DOI: 10.1016/j.canrad.2019.07.135. View

Sheikh K, Lee S, Cheng Z, Lakshminarayanan P, Peng L, Han P . Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol. 2019; 14(1):131. PMC: 6664784. DOI: 10.1186/s13014-019-1339-4. View

Cihoric N, Tapia C, Kruger K, Aebersold D, Klaeser B, Lossl K . IMRT with ¹⁸FDG-PET\CT based simultaneous integrated boost for treatment of nodal positive cervical cancer. Radiat Oncol. 2014; 9:83. PMC: 4014138. DOI: 10.1186/1748-717X-9-83. View

10.

Fiorentino A, Caivano R, Metallo V, Chiumento C, Cozzolino M, Califano G . Parotid gland volumetric changes during intensity-modulated radiotherapy in head and neck cancer. Br J Radiol. 2012; 85(1018):1415-9. PMC: 3474039. DOI: 10.1259/bjr/30678306. View

11.

Hanania A, Bantis L, Feng Z, Wang H, Tamm E, Katz M . Quantitative imaging to evaluate malignant potential of IPMNs. Oncotarget. 2016; 7(52):85776-85784. PMC: 5349873. DOI: 10.18632/oncotarget.11769. View

12.

Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P . Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: Preliminary findings. Eur Radiol. 2016; 27(10):4188-4197. PMC: 5403632. DOI: 10.1007/s00330-016-4637-3. View

13.

Foy J, Robinson K, Li H, Giger M, Al-Hallaq H, Armato 3rd S . Variation in algorithm implementation across radiomics software. J Med Imaging (Bellingham). 2019; 5(4):044505. PMC: 6278846. DOI: 10.1117/1.JMI.5.4.044505. View

14.

Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi A, Mirabelli P . Radiogenomic Analysis of Oncological Data: A Technical Survey. Int J Mol Sci. 2017; 18(4). PMC: 5412389. DOI: 10.3390/ijms18040805. View

15.

Johnson W, Li C, Rabinovic A . Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2006; 8(1):118-27. DOI: 10.1093/biostatistics/kxj037. View

16.

Berwouts D, De Wolf K, Lambert B, Bultijnck R, De Neve W, De Lobel L . Biological 18[F]-FDG-PET image-guided dose painting by numbers for painful uncomplicated bone metastases: A 3-arm randomized phase II trial. Radiother Oncol. 2015; 115(2):272-8. DOI: 10.1016/j.radonc.2015.04.022. View

17.

Nyflot M, Thammasorn P, Wootton L, Ford E, Chaovalitwongse W . Deep learning for patient-specific quality assurance: Identifying errors in radiotherapy delivery by radiomic analysis of gamma images with convolutional neural networks. Med Phys. 2018; 46(2):456-464. DOI: 10.1002/mp.13338. View

18.

Galvin J, De Neve W . Intensity modulating and other radiation therapy devices for dose painting. J Clin Oncol. 2007; 25(8):924-30. DOI: 10.1200/JCO.2007.10.6716. View

19.

Gillies R, Kinahan P, Hricak H . Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2015; 278(2):563-77. PMC: 4734157. DOI: 10.1148/radiol.2015151169. View

20.

Shiradkar R, Podder T, Algohary A, Viswanath S, Ellis R, Madabhushi A . Radiomics based targeted radiotherapy planning (Rad-TRaP): a computational framework for prostate cancer treatment planning with MRI. Radiat Oncol. 2016; 11(1):148. PMC: 5103611. DOI: 10.1186/s13014-016-0718-3. View