Top

Published in:

Open Access 01-10-2020 | Magnetic Resonance Imaging | Review Article

Radiomics in radiation oncology—basics, methods, and limitations

Authors: Philipp Lohmann, PhD, Khaled Bousabarah, Mauritius Hoevels, Harald Treuer, PhD

Published in: Strahlentherapie und Onkologie | Issue 10/2020

Abstract

Over the past years, the quantity and complexity of imaging data available for the clinical management of patients with solid tumors has increased substantially. Without the support of methods from the field of artificial intelligence (AI) and machine learning, a complete evaluation of the available image information is hardly feasible in clinical routine. Especially in radiotherapy planning, manual detection and segmentation of lesions is laborious, time consuming, and shows significant variability among observers. Here, AI already offers techniques to support radiation oncologists, whereby ultimately, the productivity and the quality are increased, potentially leading to an improved patient outcome. Besides detection and segmentation of lesions, AI allows the extraction of a vast number of quantitative imaging features from structural or functional imaging data that are typically not accessible by means of human perception. These features can be used alone or in combination with other clinical parameters to generate mathematical models that allow, for example, prediction of the response to radiotherapy. Within the large field of AI, radiomics is the subdiscipline that deals with the extraction of quantitative image features as well as the generation of predictive or prognostic mathematical models. This review gives an overview of the basics, methods, and limitations of radiomics, with a focus on patients with brain tumors treated by radiation therapy.

Back M, Jayamanne D, Brazier D, Newey A, Bailey D, Schembri G, Hsiao E, Khasraw M, Wong M, Kastelan M, Brown C, Wheeler H (2020) Pattern of failure in anaplastic glioma patients with an IDH1/2 mutation. Strahlenther Onkol 196(1):31–39. https://doi.org/10.1007/s00066-019-01467-0CrossRefPubMed

Bodensohn R, Hadi I, Fleischmann DF, Corradini S, Thon N, Rauch J, Belka C, Niyazi M (2020) Bevacizumab as a treatment option for radiation necrosis after cranial radiation therapy: a retrospective monocentric analysis. Strahlenther Onkol 196(1):70–76. https://doi.org/10.1007/s00066-019-01521-xCrossRefPubMed

Straube C, Elpula G, Gempt J, Gerhardt J, Bette S, Zimmer C, Schmidt-Graf F, Meyer B, Combs SE (2017) Re-irradiation after gross total resection of recurrent glioblastoma : spatial pattern of recurrence and a review of the literature as a basis for target volume definition. Strahlenther Onkol 193(11):897–909. https://doi.org/10.1007/s00066-017-1161-6CrossRefPubMed

Popp I, Weber WA, Combs SE, Yuh WTC, Grosu AL (2019) Neuroimaging for radiation therapy of brain tumors. Top Magn Reson Imaging 28(2):63–71. https://doi.org/10.1097/RMR.0000000000000198CrossRefPubMed

Villanueva-Meyer JE, Mabray MC, Cha S (2017) Current clinical brain tumor imaging. Neurosurgery 81(3):397–415. https://doi.org/10.1093/neuros/nyx103CrossRefPubMedPubMedCentral

Pope WB, Brandal G (2018) Conventional and advanced magnetic resonance imaging in patients with high-grade glioma. Q J Nucl Med Mol Imaging 62(3):239–253. https://doi.org/10.23736/S1824-4785.18.03086-8CrossRefPubMedPubMedCentral

Zhang X, Liu S, Zhao X, Shi X, Li J, Guo J, Niedermann G, Luo R, Zhang X (2020) Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas. Strahlenther Onkol. https://doi.org/10.1007/s00066-020-01584-1CrossRefPubMedPubMedCentral

Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48(4):441–446. https://doi.org/10.1016/j.ejca.2011.11.036CrossRefPubMedPubMedCentral

Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749–762. https://doi.org/10.1038/nrclinonc.2017.141CrossRefPubMed

10.

Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169CrossRefPubMed

11.

Aerts HJ, Velazquez ER, Leijenaar RT, Parmar C, Grossmann P, Carvalho S, Bussink J, Monshouwer R, Haibe-Kains B, Rietveld D, Hoebers F, Rietbergen MM, Leemans CR, Dekker A, Quackenbush J, Gillies RJ, Lambin P (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006. https://doi.org/10.1038/ncomms5006CrossRefPubMed

12.

Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, Bellomi M (2018) Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp 2(1):36. https://doi.org/10.1186/s41747-018-0068-zCrossRefPubMedPubMedCentral

13.

Lohmann P, Kocher M, Ceccon G, Bauer EK, Stoffels G, Viswanathan S, Ruge MI, Neumaier B, Shah NJ, Fink GR, Langen KJ, Galldiks N (2018) Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. Neuroimage Clin 20:537–542. https://doi.org/10.1016/j.nicl.2018.08.024CrossRefPubMedPubMedCentral

14.

Lohmann P, Kocher M, Ruge MI, Visser-Vandewalle V, Shah NJ, Fink GR, Langen KJ, Galldiks N (2020) PET/MRI Radiomics in patients with brain metastases. Front Neurol 11:1. https://doi.org/10.3389/fneur.2020.00001CrossRefPubMedPubMedCentral

15.

Soltaninejad M, Yang G, Lambrou T, Allinson N, Jones TL, Barrick TR, Howe FA, Ye X (2018) Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels. Comput Methods Programs Biomed 157:69–84. https://doi.org/10.1016/j.cmpb.2018.01.003CrossRefPubMed

16.

Deng W, Shi Q, Luo K, Yang Y, Ning N (2019) Brain tumor segmentation based on improved convolutional neural network in combination with non-quantifiable local texture feature. J Med Syst 43(6):152. https://doi.org/10.1007/s10916-019-1289-2CrossRefPubMed

17.

Selvapandian A, Manivannan K (2018) Fusion based Glioma brain tumor detection and segmentation using ANFIS classification. Comput Methods Programs Biomed 166:33–38. https://doi.org/10.1016/j.cmpb.2018.09.006CrossRefPubMed

18.

Prasanna P, Karnawat A, Ismail M, Madabhushi A, Tiwari P (2019) Radiomics-based convolutional neural network for brain tumor segmentation on multiparametric magnetic resonance imaging. J Med Imaging 6(2):24005. https://doi.org/10.1117/1.JMI.6.2.024005CrossRef

19.

Charron O, Lallement A, Jarnet D, Noblet V, Clavier JB, Meyer P (2018) Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 95:43–54. https://doi.org/10.1016/j.compbiomed.2018.02.004CrossRefPubMed

20.

Grovik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G (2020) Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 51(1):175–182. https://doi.org/10.1002/jmri.26766CrossRefPubMed

21.

Bousabarah K, Ruge M, Brand JS, Hoevels M, Ruess D, Borggrefe J, Grosse Hokamp N, Visser-Vandewalle V, Maintz D, Treuer H, Kocher M (2020) Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data. Radiat Oncol 15(1):87. https://doi.org/10.1186/s13014-020-01514-6CrossRefPubMedPubMedCentral

22.

Zwanenburg A, Vallieres M, Abdalah MA, Aerts H, Andrearczyk V, Apte A, Ashrafinia S, Bakas S, Beukinga RJ, Boellaard R, Bogowicz M, Boldrini L, Buvat I, Cook GJR, Davatzikos C, Depeursinge A, Desseroit MC, Dinapoli N, Dinh CV, Echegaray S, El Naqa I, Fedorov AY, Gatta R, Gillies RJ, Goh V, Gotz M, Guckenberger M, Ha SM, Hatt M, Isensee F, Lambin P, Leger S, Leijenaar RTH, Lenkowicz J, Lippert F, Losnegard A, Maier-Hein KH, Morin O, Muller H, Napel S, Nioche C, Orlhac F, Pati S, Pfaehler EAG, Rahmim A, Rao AUK, Scherer J, Siddique MM, Sijtsema NM, Socarras Fernandez J, Spezi E, Steenbakkers R, Tanadini-Lang S, Thorwarth D, Troost EGC, Upadhaya T, Valentini V, van Dijk LV, van Griethuysen J, van Velden FHP, Whybra P, Richter C, Lock S (2020) The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 295(2):328–338. https://doi.org/10.1148/radiol.2020191145CrossRefPubMed

23.

Shinohara RT, Sweeney EM, Goldsmith J, Shiee N, Mateen FJ, Calabresi PA, Jarso S, Pham DL, Reich DS, Crainiceanu CM (2014) Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 6:9–19. https://doi.org/10.1016/j.nicl.2014.08.008CrossRefPubMedPubMedCentral

24.

Ellingson BM, Zaw T, Cloughesy TF, Naeini KM, Lalezari S, Mong S, Lai A, Nghiemphu PL, Pope WB (2012) Comparison between intensity normalization techniques for dynamic susceptibility contrast (DSC)-MRI estimates of cerebral blood volume (CBV) in human gliomas. J Magn Reson Imaging 35(6):1472–1477. https://doi.org/10.1002/jmri.23600CrossRefPubMed

25.

Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30(9):1234–1248. https://doi.org/10.1016/j.mri.2012.06.010CrossRefPubMedPubMedCentral

26.

Xu D‑H, Kurani AS, Furst JD, Raicu DS (2004) Run-length encoding for volumetric texture. Conference proeedings from the 4th IASTED international conference on visualization, imaging and image processing: VIP

27.

Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst, Man, Cybern 3(6):610–621. https://doi.org/10.1109/Tsmc.1973.4309314CrossRef

28.

Zhou M, Scott J, Chaudhury B, Hall L, Goldgof D, Yeom KW, Iv M, Ou Y, Kalpathy-Cramer J, Napel S, Gillies R, Gevaert O, Gatenby R (2018) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. AJNR Am J Neuroradiol 39(2):208–216. https://doi.org/10.3174/ajnr.A5391CrossRefPubMedPubMedCentral

29.

Zhang Y, Oikonomou A, Wong A, Haider MA, Khalvati F (2017) Radiomics-based prognosis analysis for non-small cell lung cancer. Sci Rep 7:46349. https://doi.org/10.1038/srep46349CrossRefPubMedPubMedCentral

30.

Parekh V, Jacobs MA (2016) Radiomics: a new application from established techniques. Expert Rev Precis Med Drug Dev 1(2):207–226. https://doi.org/10.1080/23808993.2016.1164013CrossRefPubMedPubMedCentral

31.

Kuhn M, Johnson K (2013) Applied predictive modeling. Springer, New York https://doi.org/10.1007/978-1-4614-6849-3CrossRef

32.

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. https://doi.org/10.1145/3065386CrossRef

33.

Khalvati F, Zhang J, Chung AG, Shafiee MJ, Wong A, Haider MA (2018) MPCaD: a multi-scale radiomics-driven framework for automated prostate cancer localization and detection. BMC Med Imaging 18(1):16. https://doi.org/10.1186/s12880-018-0258-4CrossRefPubMedPubMedCentral

34.

Cha YJ, Jang WI, Kim MS, Yoo HJ, Paik EK, Jeong HK, Youn SM (2018) Prediction of response to stereotactic radiosurgery for brain metastases using convolutional neural networks. Anticancer Res 38(9):5437–5445. https://doi.org/10.21873/anticanres.12875CrossRefPubMed

35.

Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learningCrossRef

36.

Avanzo M, Wei L, Stancanello J, Vallieres M, Rao A, Morin O, Mattonen SA, El Naqa I (2020) Machine and deep learning methods for radiomics. Med Phys 47(5):e185–e202. https://doi.org/10.1002/mp.13678CrossRefPubMed

37.

Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102(4):1143–1158. https://doi.org/10.1016/j.ijrobp.2018.05.053CrossRefPubMedPubMedCentral

38.

van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104–e107. https://doi.org/10.1158/0008-5472.CAN-17-0339CrossRefPubMedPubMedCentral

39.

Szczypinski PM, Strzelecki M, Materka A, Klepaczko A (2009) MaZda—a software package for image texture analysis. Comput Methods Programs Biomed 94(1):66–76. https://doi.org/10.1016/j.cmpb.2008.08.005CrossRefPubMed

40.

Nioche C, Orlhac F, Boughdad S, Reuze S, Goya-Outi J, Robert C, Pellot-Barakat C, Soussan M, Frouin F, Buvat I (2018) LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res 78(16):4786–4789. https://doi.org/10.1158/0008-5472.CAN-18-0125CrossRefPubMed

41.

Gulli A, Pal S (2017) Deep learning with Keras

42.

Abadi M, Barham P, Chen J, Chen U, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M (2016) Tensorflow: a system for large-scale machine learning. In: 12th symposium on operating systems design and implementation, pp 265–283

43.

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S (2019) PyTorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A (eds) Advances in neural information processing systems, vol 32, pp 8024–8035

44.

Luo Y, McShan D, Ray D, Matuszak M, Jolly S, Lawrence T, Kong FM, Ten Haken R, El Naqa I (2019) Development of a fully cross-validated Bayesian network approach for local control prediction in lung cancer. IEEE Trans Radiat Plasma Med Sci 3(2):232–241. https://doi.org/10.1109/TRPMS.2018.2832609CrossRefPubMed

45.

Nguyen A, Yosinski J, Clune J (2019) Understanding neural networks via feature visualization: a survey. In: Samek W, Montavon G, Vedaldi A, Hansen LK, Müller K‑R (eds) Explainable AI: interpreting, explaining and visualizing deep learning. Springer, Cham, pp 55–76 https://doi.org/10.1007/978-3-030-28954-6_4CrossRef

46.

Baessler B, Weiss K, Pinto Dos Santos D (2019) Robustness and reproducibility of radiomics in magnetic resonance imaging: a phantom study. Invest Radiol 54(4):221–228. https://doi.org/10.1097/RLI.0000000000000530CrossRefPubMed

47.

Zwanenburg A (2019) Radiomics in nuclear medicine: robustness, reproducibility, standardization, and how to avoid data analysis traps and replication crisis. Eur J Nucl Med Mol Imaging 46(13):2638–2655. https://doi.org/10.1007/s00259-019-04391-8CrossRefPubMed

Title: Radiomics in radiation oncology—basics, methods, and limitations
Authors: Philipp Lohmann, PhD
Khaled Bousabarah
Mauritius Hoevels
Harald Treuer, PhD
Publication date: 01-10-2020
Publisher: Springer Berlin Heidelberg
Keywords: Magnetic Resonance Imaging
Magnetic Resonance Imaging
Artificial Intelligence
Radiotherapy
Positron Emission Tomography
Published in: Strahlentherapie und Onkologie / Issue 10/2020
Print ISSN: 0179-7158
Electronic ISSN: 1439-099X
DOI: https://doi.org/10.1007/s00066-020-01663-3

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Radiomics in radiation oncology—basics, methods, and limitations

Abstract

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 10/2020

Magnetic resonance imaging-based radiomic features for extrapolating infiltration levels of immune cells in lower-grade gliomas

Radiomics for liver tumours

Radiomic biomarkers for head and neck squamous cell carcinoma

Vielversprechende Langzeitergebnisse zum Einsatz von Anastrozol zur Prävention von Brustkrebs bei postmenopausalen Hochrisikopatientinnen

A radiomic approach to predicting nodal relapse and disease-specific survival in patients treated with stereotactic body radiation therapy for early-stage non-small cell lung cancer.

Artificial intelligence and radiomics for radiation oncology