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http://dx.doi.org/10.13104/imri.2021.25.4.266

Radiomics and Deep Learning in Brain Metastases: Current Trends and Roadmap to Future Applications  

Park, Yae Won (Yonsei University College of Medicine)
Lee, Narae (Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine)
Ahn, Sung Soo (Yonsei University College of Medicine)
Chang, Jong Hee (Yonsei University College of Medicine)
Lee, Seung-Koo (Yonsei University College of Medicine)
Publication Information
Investigative Magnetic Resonance Imaging / v.25, no.4, 2021 , pp. 266-280 More about this Journal
Abstract
Advances in radiomics and deep learning (DL) hold great potential to be at the forefront of precision medicine for the treatment of patients with brain metastases. Radiomics and DL can aid clinical decision-making by enabling accurate diagnosis, facilitating the identification of molecular markers, providing accurate prognoses, and monitoring treatment response. In this review, we summarize the clinical background, unmet needs, and current state of research of radiomics and DL for the treatment of brain metastases. The promises, pitfalls, and future roadmap of radiomics and DL in brain metastases are addressed as well.
Keywords
Artificial intelligence; Brain metastases; Deep learning; Machine learning; Radiomics;
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1 Bhatia A, Birger M, Veeraraghavan H, et al. MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors. Neuro Oncol 2019;21:1578-1586   DOI
2 Cagney DN, Martin AM, Catalano PJ, et al. Incidence and prognosis of patients with brain metastases at diagnosis of systemic malignancy: a population-based study. Neuro Oncol 2017;19:1511-1521   DOI
3 Dong F, Li Q, Jiang B, et al. Differentiation of supratentorial single brain metastasis and glioblastoma by using perienhancing oedema region-derived radiomic features and multiple classifiers. Eur Radiol 2020;30:3015-3022   DOI
4 Chen C, Ou X, Wang J, Guo W, Ma X. Radiomics-based machine learning in differentiation between glioblastoma and metastatic brain tumors. Front Oncol 2019;9:806   DOI
5 Bae S, An C, Ahn SS, et al. Robust performance of deep learning for distinguishing glioblastoma from single brain metastasis using radiomic features: model development and validation. Sci Rep 2020;10:12110   DOI
6 Shin I, Kim H, Ahn SS, et al. Development and validation of a deep learning-based model to distinguish glioblastoma from solitary brain metastasis using conventional MR images. AJNR Am J Neuroradiol 2021;42:838-844   DOI
7 Kniep HC, Madesta F, Schneider T, et al. Radiomics of brain MRI: utility in prediction of metastatic tumor type. Radiology 2019;290:479-487   DOI
8 Hotta M, Minamimoto R, Miwa K. 11C-methionine-PET for differentiating recurrent brain tumor from radiation necrosis: radiomics approach with random forest classifier. Sci Rep 2019;9:15666   DOI
9 Le Rhun E, Dhermain F, Vogin G, Reyns N, Metellus P. Radionecrosis after stereotactic radiotherapy for brain metastases. Expert Rev Neurother 2016;16:903-914   DOI
10 Lohmann P, Stoffels G, Ceccon G, et al. Radiation injury vs. recurrent brain metastasis: combining textural feature radiomics analysis and standard parameters may increase (18)F-FET PET accuracy without dynamic scans. Eur Radiol 2017;27:2916-2927   DOI
11 Aoyama H, Shirato H, Tago M, et al. Stereotactic radiosurgery plus whole-brain radiation therapy vs stereotactic radiosurgery alone for treatment of brain metastases: a randomized controlled trial. JAMA 2006;295:2483-2491   DOI
12 Soffietti R, Ahluwalia M, Lin N, Ruda R. Management of brain metastases according to molecular subtypes. Nat Rev Neurol 2020;16:557-574   DOI
13 Lundberg S, Lee S-I. A unified approach to interpreting model predictions. arXiv preprint arXiv:1705.07874, 2017
14 Park YW, An C, Lee J, et al. Diffusion tensor and postcontrast T1-weighted imaging radiomics to differentiate the epidermal growth factor receptor mutation status of brain metastases from non-small cell lung cancer. Neuroradiology 2021;63:343-352   DOI
15 Samek W, Muller K-R. Towards explainable artificial intelligence. In Samek W, Montavon G, Vedaldi A, Hansen LK, Muller K-R, eds. Explainable AI: interpreting, explaining and visualizing deep learning. Springer Nature, 2019:5-22
16 Mishra S, Sturm BL, Dixon S. Local interpretable model-agnostic explanations for music content analysis. ISMIR 2017:537-543
17 Selvaraju RR, Das A, Vedantam R, Cogswell M, Parikh D, Batra D. Grad-cam: Why did you say that? arXiv preprint arXiv:1611.07450, 2016
18 Petsiuk V, Das A, Saenko K. Rise: randomized input sampling for explanation of black-box models. arXiv preprint arXiv:1806.07421, 2018
19 Kocher M, Soffietti R, Abacioglu U, et al. Adjuvant whole-brain radiotherapy versus observation after radiosurgery or surgical resection of one to three cerebral metastases: results of the EORTC 22952-26001 study. J Clin Oncol 2011;29:134-141
20 Brown PD, Jaeckle K, Ballman KV, et al. Effect of radiosurgery alone vs radiosurgery with whole brain radiation therapy on cognitive function in patients with 1 to 3 brain metastases: a randomized clinical trial. JAMA 2016;316:401-409   DOI
21 Wang G, Wang B, Wang Z, et al. Radiomics signature of brain metastasis: prediction of EGFR mutation status. Eur Radiol 2021;31:4538-4547   DOI
22 Gondi V, Pugh SL, Tome WA, et al. Preservation of memory with conformal avoidance of the hippocampal neural stem-cell compartment during whole-brain radiotherapy for brain metastases (RTOG 0933): a phase II multi-institutional trial. J Clin Oncol 2014;32:3810-3816   DOI
23 Sounderajah V, Ashrafian H, Aggarwal R, et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: the STARD-AI Steering Group. Nat Med 2020;26:807-808   DOI
24 Ortiz-Ramon R, Larroza A, Ruiz-Espana S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 2018;28:4514-4523   DOI
25 Lohmann P, Kocher M, Ceccon G, et al. Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. Neuroimage Clin 2018;20:537-542   DOI
26 Peng L, Parekh V, Huang P, et al. Distinguishing true progression from radionecrosis after stereotactic radiation therapy for brain metastases with machine learning and radiomics. Int J Radiat Oncol Biol Phys 2018;102:1236-1243   DOI
27 Cho SJ, Sunwoo L, Baik SH, Bae YJ, Choi BS, Kim JH. Brain metastasis detection using machine learning: a systematic review and meta-analysis. Neuro Oncol 2021;23:214-225   DOI
28 Anzalone N, Essig M, Lee SK, et al. Optimizing contrast-enhanced magnetic resonance imaging characterization of brain metastases: relevance to stereotactic radiosurgery. Neurosurgery 2013;72:691-701   DOI
29 Park YW, Ahn SJ. Comparison of contrast-enhanced T2 FLAIR and 3D T1 black-blood fast spin-echo for detection of leptomeningeal metastases. Investig Magn Reson Imaging 2018;22:86-93   DOI
30 Lin NU, Lee EQ, Aoyama H, et al. Response assessment criteria for brain metastases: proposal from the RANO group. Lancet Oncol 2015;16:e270-278   DOI
31 Grovik E, Yi D, Iv M, Tong E, Rubin D, Zaharchuk G. Deep learning enables automatic detection and segmentation of brain metastases on multisequence MRI. J Magn Reson Imaging 2020;51:175-182   DOI
32 Zhang M, Young GS, Chen H, et al. Deep-learning detection of cancer metastases to the brain on MRI. J Magn Reson Imaging 2020;52:1227-1236   DOI
33 Bousabarah K, Ruge M, Brand JS, et al. Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data. Radiat Oncol 2020;15:87   DOI
34 Dikici E, Ryu JL, Demirer M, et al. Automated brain metastases detection framework for T1-weighted contrast-enhanced 3D MRI. IEEE J Biomed Health Inform 2020;24:2883-2893   DOI
35 Park YW, Jun Y, Lee Y, et al. Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging. Eur Radiol 2021;31:6686-6695   DOI
36 Mouraviev A, Detsky J, Sahgal A, et al. Use of radiomics for the prediction of local control of brain metastases after stereotactic radiosurgery. Neuro Oncol 2020;22:797-805   DOI
37 Stefano A, Comelli A, Bravata V, et al. A preliminary PET radiomics study of brain metastases using a fully automatic segmentation method. BMC Bioinformatics 2020;21:325   DOI
38 Okada H, Weller M, Huang R, et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol 2015;16:e534-e542   DOI
39 Zhou Z, Sanders JW, Johnson JM, et al. Computer-aided detection of brain metastases in T1-weighted MRI for stereotactic radiosurgery using deep learning single-shot detectors. Radiology 2020;295:407-415   DOI
40 Suh CH, Jung SC, Kim KW, Pyo J. The detectability of brain metastases using contrast-enhanced spin-echo or gradient-echo images: a systematic review and meta-analysis. J Neurooncol 2016;129:363-371   DOI
41 Woo I, Lee A, Jung SC, et al. Fully automatic segmentation of acute ischemic lesions on diffusion-weighted imaging using convolutional neural networks: comparison with conventional algorithms. Korean J Radiol 2019;20:1275-1284   DOI
42 Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH. No new-net. International MICCAI Brainlesion Workshop, 2018:234-244
43 Ostrom QT, Cioffi G, Gittleman H, et al. CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012-2016. Neuro Oncol 2019;21:v1-v100   DOI
44 Bluemke DA, Moy L, Bredella MA, et al. Assessing radiology research on artificial intelligence: a brief guide for authors, reviewers, and readers-from the Radiology Editorial Board. Radiology 2020;294:487-489   DOI
45 Jun Y, Eo T, Kim T, et al. Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors. Sci Rep 2018;8:9450   DOI
46 Kleesiek J, Morshuis JN, Isensee F, et al. Can virtual contrast enhancement in brain MRI replace gadolinium?: a feasibility study. Invest Radiol 2019;54:653-660   DOI
47 England JR, Cheng PM. Artificial intelligence for medical image analysis: a guide for authors and reviewers. AJR Am J Roentgenol 2019;212:513-519   DOI
48 Artzi M, Bressler I, Ben Bashat D. Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging 2019;50:519-528   DOI
49 Kamnitsas K, Ledig C, Newcombe VFJ, et al. Efficient multiscale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017;36:61-78   DOI
50 Kwon YW, Moon W-J, Park M, et al. Dynamic susceptibility contrast (DSC) perfusion MR in the prediction of long-term survival of glioblastomas (GBM): correlation with MGMT promoter methylation and 1p/19q deletions. Investig Magn Reson Imaging 2018;22:158-167   DOI
51 Kim YE, Choi SH, Lee ST, et al. Differentiation between glioblastoma and primary central nervous system lymphoma using dynamic susceptibility contrast-enhanced perfusion MR imaging: comparison study of the manual versus semiautomatic segmentation method. Investig Magn Reson Imaging 2017;21:9-19   DOI
52 Soffietti R, Abacioglu U, Baumert B, et al. Diagnosis and treatment of brain metastases from solid tumors: guidelines from the European Association of Neuro-Oncology (EANO). Neuro Oncol 2017;19:162-174   DOI
53 Han C, Hayashi H, Rundo L, et al. GAN-based synthetic brain MR image generation. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) 2018:734-738
54 Ortiz-Ramon R, Ruiz-Espana S, Molla-Olmos E, Moratal D. Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach. Phys Med 2020;76:44-54   DOI
55 Hughes RAC, Brainin M, Gilhus NE. European handbook of neurological management. Wiley-Blackwell, 2008
56 Ballard P, Yates JW, Yang Z, et al. Preclinical comparison of osimertinib with other EGFR-TKIs in EGFR-mutant NSCLC brain metastases models, and early evidence of clinical brain metastases activity. Clin Cancer Res 2016;22:5130-5140   DOI
57 Sloot S, Chen YA, Zhao X, et al. Improved survival of patients with melanoma brain metastases in the era of targeted BRAF and immune checkpoint therapies. Cancer 2018;124:297-305   DOI
58 Tawbi HA, Forsyth PA, Algazi A, et al. Combined nivolumab and ipilimumab in melanoma metastatic to the brain. N Engl J Med 2018;379:722-730   DOI
59 Shin HC, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016;35:1285-1298   DOI
60 Shofty B, Artzi M, Shtrozberg S, et al. Virtual biopsy using MRI radiomics for prediction of BRAF status in melanoma brain metastasis. Sci Rep 2020;10:6623   DOI
61 Zhang Z, Yang J, Ho A, et al. A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 2018;28:2255-2263   DOI
62 Chang EL, Wefel JS, Hess KR, et al. Neurocognition in patients with brain metastases treated with radiosurgery or radiosurgery plus whole-brain irradiation: a randomised controlled trial. Lancet Oncol 2009;10:1037-1044   DOI
63 Chen BT, Jin T, Ye N, et al. Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases. Magn Reson Imaging 2020;69:49-56   DOI
64 Cha YJ, Jang WI, Kim MS, et al. Prediction of response to stereotactic radiosurgery for brain metastases using convolutional neural networks. Anticancer Res 2018;38:5437-5445   DOI
65 Zwanenburg A, Vallieres M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. Radiology 2020;295:328-338   DOI
66 Park JE, Kim D, Kim HS, et al. Quality of science and reporting of radiomics in oncologic studies: room for improvement according to radiomics quality score and TRIPOD statement. Eur Radiol 2020;30:523-536   DOI
67 Won SY, Park YW, Park M, Ahn SS, Kim J, Lee SK. Quality reporting of radiomics analysis in mild cognitive impairment and Alzheimer's disease: a roadmap for moving forward. Korean J Radiol 2020;21:1345-1354   DOI
68 Sun Q, Liu Y, Chua T-S, Schiele B. Meta-transfer learning for few-shot learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019:403-412
69 Dalca AV, Guttag J, Sabuncu MR. Anatomical priors in convolutional networks for unsupervised biomedical segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018:9290-9299
70 Charron O, Lallement A, Jarnet D, Noblet V, Clavier JB, Meyer P. Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network. Comput Biol Med 2018;95:43-54   DOI
71 Yi X, Walia E, Babyn P. Generative adversarial network in medical imaging: a review. Med Image Anal 2019;58:101552   DOI
72 Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and generalizability in radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 2019;20:1124-1137   DOI
73 Sharma S, Mehra R. Breast cancer histology images classification: training from scratch or transfer learning? ICT Express 2018;4:247-254   DOI
74 Samangouei P, Kabkab M, Chellappa R. Defense-gan: protecting classifiers against adversarial attacks using generative models. arXiv preprint arXiv:1805.06605 2018
75 Collins GS, Moons KGM. Reporting of artificial intelligence prediction models. Lancet 2019;393:1577-1579   DOI
76 Xue J, Wang B, Ming Y, et al. Deep learning-based detection and segmentation-assisted management of brain metastases. Neuro Oncol 2020;22:505-514   DOI
77 Okada H, Kalinski P, Ueda R, et al. Induction of CD8+ T-cell responses against novel glioma-associated antigen peptides and clinical activity by vaccinations with {alpha}-type 1 polarized dendritic cells and polyinosinic-polycytidylic acid stabilized by lysine and carboxymethylcellulose in patients with recurrent malignant glioma. J Clin Oncol 2011;29:330-336
78 Nishino M, Hatabu H, Hodi FS. Imaging of cancer immunotherapy: current approaches and future directions. Radiology 2019;290:9-22   DOI
79 Basler L, Gabrys HS, Hogan SA, et al. Radiomics, tumor volume, and blood biomarkers for early prediction of pseudoprogression in patients with metastatic melanoma treated with immune checkpoint inhibition. Clin Cancer Res 2020;26:4414-4425   DOI
80 Xue Y, Farhat FG, Boukrina O, et al. A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images. Neuroimage Clin 2020;25:102118   DOI
81 Qian Z, Li Y, Wang Y, et al. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett 2019;451:128-135   DOI
82 Huang CY, Lee CC, Yang HC, et al. Radiomics as prognostic factor in brain metastases treated with Gamma Knife radiosurgery. J Neurooncol 2020;146:439-449   DOI
83 Loeffler JS, Patchell RA, Sawaya R. Metastatic brain cancer. In Davita VT, Hellman S, Rosenberg SA, eds. Cancer: principles and practice of oncology. Philadelphia: JP Lippincott, 1997:2523
84 Arvold ND, Lee EQ, Mehta MP, et al. Updates in the management of brain metastases. Neuro Oncol 2016;18:1043-1065   DOI
85 Long GV, Trefzer U, Davies MA, et al. Dabrafenib in patients with Val600Glu or Val600Lys BRAF-mutant melanoma metastatic to the brain (BREAK-MB): a multicentre, open-label, phase 2 trial. Lancet Oncol 2012;13:1087-1095   DOI
86 Carre A, Klausner G, Edjlali M, et al. Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics. Sci Rep 2020;10:12340   DOI
87 Nichol A, Achiam J, Schulman J. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018
88 Ahn SJ, Kwon H, Yang JJ, et al. Contrast-enhanced T1-weighted image radiomics of brain metastases may predict EGFR mutation status in primary lung cancer. Sci Rep 2020;10:8905   DOI
89 Graber JJ, Cobbs CS, Olson JJ. Congress of neurological surgeons systematic review and evidence-based guidelines on the use of stereotactic radiosurgery in the treatment of adults with metastatic brain tumors. Neurosurgery 2019;84:E168-E170   DOI
90 Jaboin JJ, Ferraro DJ, DeWees TA, et al. Survival following gamma knife radiosurgery for brain metastasis from breast cancer. Radiat Oncol 2013;8:131   DOI
91 Brastianos PK, Carter SL, Santagata S, et al. Genomic characterization of brain metastases reveals branched evolution and potential therapeutic targets. Cancer Discov 2015;5:1164-1177   DOI
92 Alderton GK. Tumour evolution: epigenetic and genetic heterogeneity in metastasis. Nat Rev Cancer 2017;17:141   DOI
93 An C, Park YW, Ahn SS, Han K, Kim H, Lee S-K. Radiomics machine learning study with a small sample size: single random training-test set split may result in unreliable results. https://www.researchsquare.com/article/rs-105766/v2. Accessed June 9, 2021
94 Yu B, Wang Y, Wang L, Shen D, Zhou L. Medical image synthesis via deep learning. Adv Exp Med Biol 2020;1213:23-44.   DOI
95 Nayak L, Lee EQ, Wen PY. Epidemiology of brain metastases. Curr Oncol Rep 2012;14:48-54   DOI
96 Patchell RA, Tibbs PA, Walsh JW, et al. A randomized trial of surgery in the treatment of single metastases to the brain. N Engl J Med 1990;322:494-500   DOI
97 Mehta MP, Rodrigus P, Terhaard CH, et al. Survival and neurologic outcomes in a randomized trial of motexafin gadolinium and whole-brain radiation therapy in brain metastases. J Clin Oncol 2003;21:2529-2536   DOI
98 Sperduto PW, Kased N, Roberge D, et al. Summary report on the graded prognostic assessment: an accurate and facile diagnosis-specific tool to estimate survival for patients with brain metastases. J Clin Oncol 2012;30:419-425   DOI
99 Nagao E, Yoshiura T, Hiwatashi A, et al. 3D turbo spin-echo sequence with motion-sensitized driven-equilibrium preparation for detection of brain metastases on 3T MR imaging. AJNR Am J Neuroradiol 2011;32:664-670   DOI
100 Growcott S, Dembrey T, Patel R, Eaton D, Cameron A. Inter-observer variability in target volume delineations of benign and metastatic brain tumours for stereotactic radiosurgery: results of a national quality assurance programme. Clin Oncol (R Coll Radiol) 2020;32:13-25   DOI
101 Della Seta M, Collettini F, Chapiro J, et al. A 3D quantitative imaging biomarker in pre-treatment MRI predicts overall survival after stereotactic radiation therapy of patients with a singular brain metastasis. Acta Radiol 2019;60:1496-1503   DOI
102 Kondziolka D, Patel A, Lunsford LD, Kassam A, Flickinger JC. Stereotactic radiosurgery plus whole brain radiotherapy versus radiotherapy alone for patients with multiple brain metastases. Int J Radiat Oncol Biol Phys 1999;45:427-434
103 Brown PD, Brown CA, Pollock BE, Gorman DA, Foote RL. Stereotactic radiosurgery for patients with "radioresistant" brain metastases. Neurosurgery 2002;51:656-665; discussion 665-657   DOI
104 Zheng Y, Geng D, Yu T, et al. Prognostic value of pretreatment MRI texture features in breast cancer brain metastasis treated with Gamma Knife radiosurgery. Acta Radiol 2021;62:1208-1216   DOI