Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Strahlentherapie und Onkologie
Published in: Strahlentherapie und Onkologie 10/2020

Open Access 01-10-2020 | Glioma | Review Article

Applications of radiomics and machine learning for radiotherapy of malignant brain tumors

Authors: Prof. Martin Kocher, Maximilian I. Ruge, Norbert Galldiks, Philipp Lohmann

Published in: Strahlentherapie und Onkologie | Issue 10/2020

Login to get access

Abstract

Background

Magnetic resonance imaging (MRI) and amino acid positron-emission tomography (PET) of the brain contain a vast amount of structural and functional information that can be analyzed by machine learning algorithms and radiomics for the use of radiotherapy in patients with malignant brain tumors.

Methods

This study is based on comprehensive literature research on machine learning and radiomics analyses in neuroimaging and their potential application for radiotherapy in patients with malignant glioma or brain metastases.

Results

Feature-based radiomics and deep learning-based machine learning methods can be used to improve brain tumor diagnostics and automate various steps of radiotherapy planning. In glioma patients, important applications are the determination of WHO grade and molecular markers for integrated diagnosis in patients not eligible for biopsy or resection, automatic image segmentation for target volume planning, prediction of the location of tumor recurrence, and differentiation of pseudoprogression from actual tumor progression. In patients with brain metastases, radiomics is applied for additional detection of smaller brain metastases, accurate segmentation of multiple larger metastases, prediction of local response after radiosurgery, and differentiation of radiation injury from local brain metastasis relapse. Importantly, high diagnostic accuracies of 80–90% can be achieved by most approaches, despite a large variety in terms of applied imaging techniques and computational methods.

Conclusion

Clinical application of automated image analyses based on radiomics and artificial intelligence has a great potential for improving radiotherapy in patients with malignant brain tumors. However, a common problem associated with these techniques is the large variability and the lack of standardization of the methods applied.
Literature
1.
Langen KJ, Galldiks N, Hattingen E, Shah NJ (2017) Advances in neuro-oncology imaging. Nat Rev Neurol 13:279–289PubMedCrossRef
2.
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, Ohgaki H, Wiestler OD, Kleihues P, Ellison DW (2016) The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820CrossRefPubMed
3.
Weller M, van den Bent M, Tonn JC, Stupp R, Preusser M, Cohen-Jonathan-Moyal E, Henriksson R, Rhun EL, Balana C, Chinot O, Bendszus M, Reijneveld JC, Dhermain F, French P, Marosi C, Watts C, Oberg I, Pilkington G, Baumert BG, Taphoorn MJB, Hegi M, Westphal M, Reifenberger G, Soffietti R, Wick W, European Association for Neuro-Oncology (EANO) Task Force on Gliomas (2017) European association for neuro-oncology (EANO) guideline on the diagnosis and treatment of adult astrocytic and oligodendroglial gliomas. Lancet Oncol 18:e315–e329PubMedCrossRef
4.
Hartmann C, Hentschel B, Wick W, Capper D, Felsberg J, Simon M, Westphal M, Schackert G, Meyermann R, Pietsch T, Reifenberger G, Weller M, Loeffler M, von Deimling A (2010) Patients with IDH1 wild type anaplastic astrocytomas exhibit worse prognosis than IDH1-mutated glioblastomas, and IDH1 mutation status accounts for the unfavorable prognostic effect of higher age: implications for classification of gliomas. Acta Neuropathol 120:707–718PubMedCrossRef
5.
Chamberlain MC, Born D (2015) Prognostic significance of relative 1p/19q codeletion in oligodendroglial tumors. J Neurooncol 125:249–251PubMedCrossRef
6.
Speirs CK, Simpson JR, Robinson CG, DeWees TA, Tran DD, Linette G, Chicoine MR, Dacey RG, Rich KM, Dowling JL, Leuthardt EC, Zipfel GJ, Kim AH, Huang J (2015) Impact of 1p/19q codeletion and histology on outcomes of anaplastic gliomas treated with radiation therapy and temozolomide. Int J Radiat Oncol Biol Phys 91:268–276PubMedCrossRef
7.
van den Bent MJ, Brandes AA, Taphoorn MJ, Kros JM, Kouwenhoven MC, Delattre JY, Bernsen HJ, Frenay M, Tijssen CC, Grisold W, Sipos L, Enting RH, French PJ, Dinjens WN, Vecht CJ, Allgeier A, Lacombe D, Gorlia T, Hoang-Xuan K (2013) Adjuvant procarbazine, lomustine, and vincristine chemotherapy in newly diagnosed anaplastic oligodendroglioma: long-term follow-up of EORTC brain tumor group study 26951. J Clin Oncol 31:344–350CrossRefPubMed
8.
Hegi ME, Liu L, Herman JG, Stupp R, Wick W, Weller M, Mehta MP, Gilbert MR (2008) Correlation of O6-methylguanine methyltransferase (MGMT) promoter methylation with clinical outcomes in glioblastoma and clinical strategies to modulate MGMT activity. J Clin Oncol 26:4189–4199PubMedCrossRef
9.
Buckner JC, Shaw EG, Pugh SL, Chakravarti A, Gilbert MR, Barger GR, Coons S, Ricci P, Bullard D, Brown PD, Stelzer K, Brachman D, Suh JH, Schultz CJ, Bahary JP, Fisher BJ, Kim H, Murtha AD, Bell EH, Won M, Mehta MP, Curran WJ Jr. (2016) Radiation plus procarbazine, CCNU, and vincristine in low-grade glioma. N Engl J Med 374:1344–1355PubMedPubMedCentralCrossRef
10.
Hegi ME, Diserens AC, Gorlia T, Hamou MF, de Tribolet N, Weller M, Kros JM, Hainfellner JA, Mason W, Mariani L, Bromberg JE, Hau P, Mirimanoff RO, Cairncross JG, Janzer RC, Stupp R (2005) MGMT gene silencing and benefit from temozolomide in glioblastoma. N Engl J Med 352:997–1003CrossRefPubMed
11.
Nieder C, Grosu AL, Astner S, Molls M (2005) Treatment of unresectable glioblastoma multiforme. Anticancer Res 25:4605–4610PubMed
12.
Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO, European Organisation for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups, National Cancer Institute of Canada Clinical Trials Group (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996CrossRefPubMed
13.
Kellermann SG, Hamisch CA, Ruess D, Blau T, Goldbrunner R, Treuer H, Grau SJ, Ruge MI (2017) Stereotactic biopsy in elderly patients: risk assessment and impact on treatment decision. J Neurooncol 134:303–307PubMedCrossRef
14.
15.
Ditmer A, Zhang B, Shujaat T, Pavlina A, Luibrand N, Gaskill-Shipley M, Vagal A (2018) Diagnostic accuracy of MRI texture analysis for grading gliomas. J Neurooncol 140:583–589PubMedCrossRef
16.
Li-Chun Hsieh K, Chen CY, Lo CM (2017) Quantitative glioma grading using transformed gray-scale invariant textures of MRI. Comput Biol Med 83:102–108PubMedCrossRef
17.
18.
19.
Tian Q, Yan LF, Zhang X, Zhang X, Hu YC, Han Y, Liu ZC, Nan HY, Sun Q, Sun YZ, Yang Y, Yu Y, Zhang J, Hu B, Xiao G, Chen P, Tian S, Xu J, Wang W, Cui GB (2018) Radiomics strategy for glioma grading using texture features from multiparametric MRI. J Magn Reson Imaging 48:1518–1528PubMedCrossRef
20.
Vamvakas A, Williams SC, Theodorou K, Kapsalaki E, Fountas K, Kappas C, Vassiou K, Tsougos I (2019) Imaging biomarker analysis of advanced multiparametric MRI for glioma grading. Phys Med 60:188–198PubMedCrossRef
21.
Yang Y, Yan LF, Zhang X, Han Y, Nan HY, Hu YC, Hu B, Yan SL, Zhang J, Cheng DL, Ge XW, Cui GB, Zhao D, Wang W (2018) Glioma grading on conventional MR images: a deep learning study with transfer learning. Front Neurosci 12:804PubMedPubMedCentralCrossRef
22.
Chang K, Bai HX, Zhou H, Su C, Bi WL, Agbodza E, Kavouridis VK, Senders JT, Boaro A, Beers A, Zhang B, Capellini A, Liao W, Shen Q, Li X, Xiao B, Cryan J, Ramkissoon S, Ramkissoon L, Ligon K, Wen PY, Bindra RS, Woo J, Arnaout O, Gerstner ER, Zhang PJ, Rosen BR, Yang L, Huang RY, Kalpathy-Cramer J (2018) Residual convolutional neural network for the determination of IDH status in low- and high-grade gliomas from MR imaging. Clin Cancer Res 24:1073–1081PubMedCrossRef
23.
Chang P, Grinband J, Weinberg BD, Bardis M, Khy M, Cadena G, Su MY, Cha S, Filippi CG, Bota D, Baldi P, Poisson LM, Jain R, Chow D (2018) Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas. AJNR Am J Neuroradiol 39:1201–1207PubMedPubMedCentralCrossRef
24.
Eichinger P, Alberts E, Delbridge C, Trebeschi S, Valentinitsch A, Bette S, Huber T, Gempt J, Meyer B, Schlegel J, Zimmer C, Kirschke JS, Menze BH, Wiestler B (2017) Diffusion tensor image features predict IDH genotype in newly diagnosed WHO grade II/III gliomas. Sci Rep 7:13396PubMedPubMedCentralCrossRef
25.
Han L, Kamdar MR (2018) MRI to MGMT: predicting methylation status in glioblastoma patients using convolutional recurrent neural networks. Pac Symp Biocomput 23:331–342PubMedPubMedCentral
26.
Han Y, Xie Z, Zang Y, Zhang S, Gu D, Zhou M, Gevaert O, Wei J, Li C, Chen H, Du J, Liu Z, Dong D, Tian J, Zhou D (2018) Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas. J Neurooncol 140:297–306PubMedCrossRef
27.
Kong Z, Lin Y, Jiang C, Li L, Liu Z, Wang Y, Dai C, Liu D, Qin X, Wang Y, Liu Z, Cheng X, Tian J, Ma W (2019) (18)F-FDG-PET-based radiomics signature predicts MGMT promoter methylation status in primary diffuse glioma. Cancer Imaging 19:58PubMedPubMedCentralCrossRef
28.
Korfiatis P, Kline TL, Coufalova L, Lachance DH, Parney IF, Carter RE, Buckner JC, Erickson BJ (2016) MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas. Med Phys 43:2835–2844PubMedPubMedCentralCrossRef
29.
Korfiatis P, Kline TL, Lachance DH, Parney IF, Buckner JC, Erickson BJ (2017) Residual deep convolutional neural network predicts MGMT methylation status. J Digit Imaging 30:622–628PubMedPubMedCentralCrossRef
30.
Li Z, Wang Y, Yu J, Guo Y, Cao W (2017) Deep learning based radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma. Sci Rep 7:5467PubMedPubMedCentralCrossRef
31.
Li ZC, Bai H, Sun Q, Li Q, Liu L, Zou Y, Chen Y, Liang C, Zheng H (2018) Multiregional radiomics features from multiparametric MRI for prediction of MGMT methylation status in glioblastoma multiforme: a multicentre study. Eur Radiol 28:3640–3650PubMedCrossRef
32.
Liu X, Li Y, Li S, Fan X, Sun Z, Yang Z, Wang K, Zhang Z, Jiang T, Liu Y, Wang L, Wang Y (2019) IDH mutation-specific radiomic signature in lower-grade gliomas. Aging (Albany NY) 11:673–696CrossRef
33.
Lohmann P, Lerche C, Bauer EK, Steger J, Stoffels G, Blau T, Dunkl V, Kocher M, Viswanathan S, Filss CP, Stegmayr C, Ruge MI, Neumaier B, Shah NJ, Fink GR, Langen KJ, Galldiks N (2018) Predicting IDH genotype in gliomas using FET PET radiomics. Sci Rep 8:13328PubMedPubMedCentralCrossRef
34.
Lu CF, Hsu FT, Hsieh KL, Kao YJ, Cheng SJ, Hsu JB, Tsai PH, Chen RJ, Huang CC, Yen Y, Chen CY (2018) Machine learning-based radiomics for molecular subtyping of gliomas. Clin Cancer Res 24:4429–4436PubMedCrossRef
35.
36.
Xi YB, Guo F, Xu ZL, Li C, Wei W, Tian P, Liu TT, Liu L, Chen G, Ye J, Cheng G, Cui LB, Zhang HJ, Qin W, Yin H (2017) Radiomics signature: a potential biomarker for the prediction of MGMT promoter methylation in glioblastoma. J Magn Reson Imaging. https://​doi.​org/​10.​1002/​jmri.​25860CrossRefPubMed
37.
Zhang X, Tian Q, Wang L, Liu Y, Li B, Liang Z, Gao P, Zheng K, Zhao B, Lu H (2018) Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging 48:916–926CrossRefPubMed
38.
Zhou H, Chang K, Bai HX, Xiao B, Su C, Bi WL, Zhang PJ, Senders JT, Vallieres M, Kavouridis VK, Boaro A, Arnaout O, Yang L, Huang RY (2019) Machine learning reveals multimodal MRI patterns predictive of isocitrate dehydrogenase and 1p/19q status in diffuse low- and high-grade gliomas. J Neurooncol 142:299–307PubMedPubMedCentralCrossRef
39.
Zhou H, Vallieres M, Bai HX, Su C, Tang H, Oldridge D, Zhang Z, Xiao B, Liao W, Tao Y, Zhou J, Zhang P, Yang L (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19:862–870PubMedPubMedCentralCrossRef
40.
Pyka T, Gempt J, Hiob D, Ringel F, Schlegel J, Bette S, Wester HJ, Meyer B, Forster S (2016) Textural analysis of pre-therapeutic [18F]-FET-PET and its correlation with tumor grade and patient survival in high-grade gliomas. Eur J Nucl Med Mol Imaging 43:133–141PubMedCrossRef
41.
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J, Burren Y, Porz N, Slotboom J, Wiest R, Lanczi L, Gerstner E, Weber MA, Arbel T, Avants BB, Ayache N, Buendia P, Collins DL, Cordier N, Corso JJ, Criminisi A, Das T, Delingette H, Demiralp C, Durst CR, Dojat M, Doyle S, Festa J, Forbes F, Geremia E, Glocker B, Golland P, Guo X, Hamamci A, Iftekharuddin KM, Jena R, John NM, Konukoglu E, Lashkari D, Mariz JA, Meier R, Pereira S, Precup D, Price SJ, Raviv TR, Reza SM, Ryan M, Sarikaya D, Schwartz L, Shin HC, Shotton J, Silva CA, Sousa N, Subbanna NK, Szekely G, Taylor TJ, Thomas OM, Tustison NJ, Unal G, Vasseur F, Wintermark M, Ye DH, Zhao L, Zhao B, Zikic D, Prastawa M, Reyes M, Van Leemput K (2015) The multimodal brain tumor image segmentation benchmark (BRATS). IEEE Trans Med Imaging 34:1993–2024CrossRefPubMed
42.
Li ZC, Bai H, Sun Q, Zhao Y, Lv Y, Zhou J, Liang C, Chen Y, Liang D, Zheng H (2018) Multiregional radiomics profiling from multiparametric MRI: identifying an imaging predictor of IDH1 mutation status in glioblastoma. Cancer Med 7:5999–6009PubMedPubMedCentralCrossRef
43.
Zhang B, Chang K, Ramkissoon S, Tanguturi S, Bi WL, Reardon DA, Ligon KL, Alexander BM, Wen PY, Huang RY (2017) Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas. Neuro Oncol 19:109–117PubMedCrossRef
44.
Kickingereder P, Isensee F, Tursunova I, Petersen J, Neuberger U, Bonekamp D, Brugnara G, Schell M, Kessler T, Foltyn M, Harting I, Sahm F, Prager M, Nowosielski M, Wick A, Nolden M, Radbruch A, Debus J, Schlemmer HP, Heiland S, Platten M, von Deimling A, van den Bent MJ, Gorlia T, Wick W, Bendszus M, Maier-Hein KH (2019) Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study. Lancet Oncol 20:728–740PubMedCrossRef
45.
Shaver MM, Kohanteb PA, Chiou C, Bardis MD, Chantaduly C, Bota D, Filippi CG, Weinberg B, Grinband J, Chow DS, Chang PD (2019) Optimizing neuro-oncology imaging: a review of deep learning approaches for glioma imaging. Cancers (Basel) 11(6):E829. https://​doi.​org/​10.​3390/​cancers11060829CrossRef
46.
Bakas S, Reyes M, Jakab A, Bauer S, Rempfler M, Crimi A, Shinohara RT, Berger C, Ha SM, Rozycki M (2018) Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge (arXiv:1811.02629)
47.
Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH (2018) Brain tumor segmentation and radiomics survival prediction: contribution to the BRATS 2017 challenge (arXiv:1802.10508)
48.
Zhuge Y, Krauze AV, Ning H, Cheng JY, Arora BC, Camphausen K, Miller RW (2017) Brain tumor segmentation using holistically nested neural networks in MRI images. Med Phys 44:5234–5243PubMedCrossRef
49.
Wu YP, Lin YS, Wu WG, Yang C, Gu JQ, Bai Y, Wang MY (2017) Semiautomatic segmentation of glioma on mobile devices. J Healthc Eng 2017:8054939PubMedPubMedCentral
50.
Chamberlain MC (2011) Radiographic patterns of relapse in glioblastoma. J Neurooncol 101:319–323PubMedCrossRef
51.
Oppitz U, Maessen D, Zunterer H, Richter S, Flentje M (1999) 3D-recurrence-patterns of glioblastomas after CT-planned postoperative irradiation. Radiother Oncol 53:53–57PubMedCrossRef
52.
Syed M, Liermann J, Verma V, Bernhardt D, Bougatf N, Paul A, Rieken S, Debus J, Adeberg S (2018) Survival and recurrence patterns of multifocal glioblastoma after radiation therapy. Cancer Manag Res 10:4229–4235PubMedPubMedCentralCrossRef
53.
Rapp M, Baernreuther J, Turowski B, Steiger HJ, Sabel M, Kamp MA (2017) Recurrence pattern analysis of primary glioblastoma. World Neurosurg 103:733–740PubMedCrossRef
54.
Minniti G, Amelio D, Amichetti M, Salvati M, Muni R, Bozzao A, Lanzetta G, Scarpino S, Arcella A, Enrici RM (2010) Patterns of failure and comparison of different target volume delineations in patients with glioblastoma treated with conformal radiotherapy plus concomitant and adjuvant temozolomide. Radiother Oncol 97:377–381PubMedCrossRef
55.
Cabrera AR, Kirkpatrick JP, Fiveash JB, Shih HA, Koay EJ, Lutz S, Petit J, Chao ST, Brown PD, Vogelbaum M, Reardon DA, Chakravarti A, Wen PY, Chang E (2016) Radiation therapy for glioblastoma: executive summary of an American society for radiation oncology evidence-based clinical practice guideline. Pract Radiat Oncol 6:217–225PubMedCrossRef
56.
Niyazi M, Brada M, Chalmers AJ, Combs SE, Erridge SC, Fiorentino A, Grosu AL, Lagerwaard FJ, Minniti G, Mirimanoff RO, Ricardi U, Short SC, Weber DC, Belka C (2016) ESTRO-ACROP guideline “target delineation of glioblastomas”. Radiother Oncol 118:35–42PubMedCrossRef
57.
Chan JL, Lee SW, Fraass BA, Normolle DP, Greenberg HS, Junck LR, Gebarski SS, Sandler HM (2002) Survival and failure patterns of high-grade gliomas after three-dimensional conformal radiotherapy. J Clin Oncol 20:1635–1642PubMedCrossRef
58.
Nakagawa K, Aoki Y, Fujimaki T, Tago M, Terahara A, Karasawa K, Sakata K, Sasaki Y, Matsutani M, Akanuma A (1998) High-dose conformal radiotherapy influenced the pattern of failure but did not improve survival in glioblastoma multiforme. Int J Radiat Oncol Biol Phys 40:1141–1149PubMedCrossRef
59.
Tanaka M, Ino Y, Nakagawa K, Tago M, Todo T (2005) High-dose conformal radiotherapy for supratentorial malignant glioma: a historical comparison. Lancet Oncol 6:953–960PubMedCrossRef
60.
Monjazeb AM, Ayala D, Jensen C, Case LD, Bourland JD, Ellis TL, McMullen KP, Chan MD, Tatter SB, Lesser GJ, Shaw EG (2012) A phase I dose escalation study of hypofractionated IMRT field-in-field boost for newly diagnosed glioblastoma multiforme. Int J Radiat Oncol Biol Phys 82:743–748PubMedCrossRef
61.
Piroth MD, Pinkawa M, Holy R, Klotz J, Schaar S, Stoffels G, Galldiks N, Coenen HH, Kaiser HJ, Langen KJ, Eble MJ (2012) Integrated boost IMRT with FET-PET-adapted local dose escalation in glioblastomas. Results of a prospective phase II study. Strahlenther Onkol 188:334–339PubMedCrossRef
62.
Piroth MD, Galldiks N, Pinkawa M, Holy R, Stoffels G, Ermert J, Mottaghy FM, Shah NJ, Langen KJ, Eble MJ (2016) Relapse patterns after radiochemotherapy of glioblastoma with FET PET-guided boost irradiation and simulation to optimize radiation target volume. Radiat Oncol 11:87PubMedPubMedCentralCrossRef
63.
Weber DC, Casanova N, Zilli T, Buchegger F, Rouzaud M, Nouet P, Vees H, Ratib O, Dipasquale G, Miralbell R (2009) Recurrence pattern after [(18)F]fluoroethyltyrosine-positron emission tomography-guided radiotherapy for high-grade glioma: a prospective study. Radiother Oncol 93:586–592PubMedCrossRef
64.
Bette S, Huber T, Gempt J, Boeckh-Behrens T, Wiestler B, Kehl V, Ringel F, Meyer B, Zimmer C, Kirschke JS (2017) Local fractional anisotropy is reduced in areas with tumor recurrence in glioblastoma. Radiology 283:499–507PubMedCrossRef
65.
Elson A, Paulson E, Bovi J, Siker M, Schultz C, Laviolette PS (2015) Evaluation of pre-radiotherapy apparent diffusion coefficient (ADC): patterns of recurrence and survival outcomes analysis in patients treated for glioblastoma multiforme. J Neurooncol 123:179–188PubMedCrossRef
66.
Esmaeili M, Stensjoen AL, Berntsen EM, Solheim O, Reinertsen I (2018) The direction of tumour growth in glioblastoma patients. Sci Rep 8:1199PubMedPubMedCentralCrossRef
67.
Jayamanne D, Wheeler H, Brazier D, Newey A, Kastelan M, Guo L, Back M (2018) Predicting patterns of failure in temporal lobe GBMs: possible implications on radiotherapy treatment portals. Radiat Oncol 13:133PubMedPubMedCentralCrossRef
68.
Akbari H, Macyszyn L, Da X, Bilello M, Wolf RL, Martinez-Lage M, Biros G, Alonso-Basanta M, O’Rourke DM, Davatzikos C (2016) Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery 78:572–580PubMedCrossRef
69.
Akbari H, Macyszyn L, Da X, Wolf RL, Bilello M, Verma R, O’Rourke DM, Davatzikos C (2014) Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. Radiology 273:502–510PubMedCrossRef
70.
Rathore S, Akbari H, Doshi J, Shukla G, Rozycki M, Bilello M, Lustig R, Davatzikos C (2018) Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning. J Med Imaging (Bellingham) 5:21219
71.
Kickingereder P, Burth S, Wick A, Gotz M, Eidel O, Schlemmer HP, Maier-Hein KH, Wick W, Bendszus M, Radbruch A, Bonekamp D (2016) Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 280:880–889PubMedCrossRef
72.
Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P (2017) Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur Radiol 27:4188–4197PubMedCrossRef
73.
Jain R, Poisson LM, Gutman D, Scarpace L, Hwang SN, Holder CA, Wintermark M, Rao A, Colen RR, Kirby J, Freymann J, Jaffe CC, Mikkelsen T, Flanders A (2014) Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 272:484–493PubMedCrossRef
74.
Kickingereder P, Neuberger U, Bonekamp D, Piechotta PL, Gotz M, Wick A, Sill M, Kratz A, Shinohara RT, Jones DTW, Radbruch A, Muschelli J, Unterberg A, Debus J, Schlemmer HP, Herold-Mende C, Pfister S, von Deimling A, Wick W, Capper D, Maier-Hein KH, Bendszus M (2018) Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 20:848–857PubMedCrossRef
75.
76.
Mohsen LA, Shi V, Jena R, Gillard JH, Price SJ (2013) Diffusion tensor invasive phenotypes can predict progression-free survival in glioblastomas. Br J Neurosurg 27:436–441PubMedCrossRef
77.
Galldiks N, Kocher M, Langen KJ (2017) Pseudoprogression after glioma therapy: an update. Expert Rev Neurother 17:1109–1115PubMedCrossRef
78.
Young RJ, Gupta A, Shah AD, Graber JJ, Zhang Z, Shi W, Holodny AI, Omuro AM (2011) Potential utility of conventional MRI signs in diagnosing pseudoprogression in glioblastoma. Neurology 76:1918–1924PubMedPubMedCentralCrossRef
79.
Wen PY, Macdonald DR, Reardon DA, Cloughesy TF, Sorensen AG, Galanis E, Degroot J, Wick W, Gilbert MR, Lassman AB, Tsien C, Mikkelsen T, Wong ET, Chamberlain MC, Stupp R, Lamborn KR, Vogelbaum MA, van den Bent MJ, Chang SM (2010) Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 28:1963–1972CrossRefPubMed
80.
Galldiks N, Dunkl V, Stoffels G, Hutterer M, Rapp M, Sabel M, Reifenberger G, Kebir S, Dorn F, Blau T, Herrlinger U, Hau P, Ruge MI, Kocher M, Goldbrunner R, Fink GR, Drzezga A, Schmidt M, Langen KJ (2015) Diagnosis of pseudoprogression in patients with glioblastoma using O‑(2-[18F]fluoroethyl)-L-tyrosine PET. Eur J Nucl Med Mol Imaging 42:685–695PubMedCrossRef
81.
Patel P, Baradaran H, Delgado D, Askin G, Christos P, John Tsiouris A, Gupta A (2017) MR perfusion-weighted imaging in the evaluation of high-grade gliomas after treatment: a systematic review and meta-analysis. Neuro Oncol 19:118–127PubMedCrossRef
82.
Filss CP, Cicone F, Shah NJ, Galldiks N, Langen KJ (2017) Amino acid PET and MR perfusion imaging in brain tumours. Clin Transl Imaging 5:209–223PubMedPubMedCentralCrossRef
83.
Hu X, Wong KK, Young GS, Guo L, Wong ST (2011) Support vector machine multiparametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 33:296–305PubMedPubMedCentralCrossRef
84.
Kim JY, Park JE, Jo Y, Shim WH, Nam SJ, Kim JH, Yoo RE, Choi SH, Kim HS (2019) Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients. Neuro Oncol 21:404–414PubMedCrossRef
85.
Kebir S, Khurshid Z, Gaertner FC, Essler M, Hattingen E, Fimmers R, Scheffler B, Herrlinger U, Bundschuh RA, Glas M (2017) Unsupervised consensus cluster analysis of [18F]-fluoroethyl-L-tyrosine positron emission tomography identified textural features for the diagnosis of pseudoprogression in high-grade glioma. Oncotarget 8:8294–8304PubMedCrossRef
86.
Lohmann P, Elahmadawy MA, Werner J, Rapp M, Ceccon G, Fink GR, Shah NJ, Langen K, Galldiks N (2019) OS9.6 diagnosis of pseudoprogression using FET PET radiomics. Neuro Oncol 21:iii19–iii19PubMedCentralCrossRef
87.
Jang BS, Jeon SH, Kim IH, Kim IA (2018) Prediction of pseudoprogression versus progression using machine learning algorithm in glioblastoma. Sci Rep 8:12516PubMedPubMedCentralCrossRef
88.
Bacchi S, Zerner T, Dongas J, Asahina AT, Abou-Hamden A, Otto S, Oakden-Rayner L, Patel S (2019) Deep learning in the detection of high-grade glioma recurrence using multiple MRI sequences: a pilot study. J Clin Neurosci 70:11–13PubMedCrossRef
89.
Akbari H, Rathore S, Bakas S, Nasrallah MP, Shukla G, Mamourian E, Rozycki M, Bagley SJ, Rudie JD, Flanders AE, Dicker AP, Desai AS, O’Rourke DM, Brem S, Lustig R, Mohan S, Wolf RL, Bilello M, Martinez-Lage M, Davatzikos C (2020) Histopathology-validated machine learning radiographic biomarker for noninvasive discrimination between true progression and pseudo-progression in glioblastoma. Cancer. https://​doi.​org/​10.​1002/​cncr.​32790CrossRefPubMed
90.
Li M, Tang H, Chan MD, Zhou X, Qian X (2020) DC-AL GAN: pseudoprogression and true tumor progression of glioblastoma multiform image classification based on DCGAN and Alexnet. Med Phys 47:1139–1150PubMedCrossRef
91.
Ohgaki H, Kleihues P (2005) Epidemiology and etiology of gliomas. Acta Neuropathol 109:93–108PubMedCrossRef
92.
Platta CS, Khuntia D, Mehta MP, Suh JH (2010) Current treatment strategies for brain metastasis and complications from therapeutic techniques: a review of current literature. Am J Clin Oncol 33:398–407PubMedCrossRef
93.
Qian Z, Li Y, Wang Y, Li L, Li R, Wang K, Li S, Tang K, Zhang C, Fan X, Chen B, Li W (2019) Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett 451:128–135PubMedCrossRef
94.
Artzi M, Bressler I, Ben Bashat D (2019) Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis. J Magn Reson Imaging 50:519–528PubMedCrossRef
95.
National Comprehensive Cancer Network (2019) Central nervous system cancers—version 3.2019. NCCN clinical practice guidelines in oncology (NCCN guidelines) (Version 3.2019)
96.
Liu Y, Stojadinovic S, Hrycushko B, Wardak Z, Lau S, Lu W, Yan Y, Jiang SB, Zhen X, Timmerman R, Nedzi L, Gu X (2017) A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery. PLoS ONE 12:e185844PubMedPubMedCentralCrossRef
97.
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 95:43–54PubMedCrossRef
98.
Brown JM, Carlson DJ, Brenner DJ (2014) The tumor radiobiology of SRS and SBRT: are more than the 5 Rs involved? Int J Radiat Oncol Biol Phys 88:254–262PubMedPubMedCentralCrossRef
99.
Kocher M, Voges J, Treuer H, Sturm V, Müller R‑P (2000) Reduced response rate of necrotic brain metastases to radiosurgery. In: Kondziolka D (ed) Radiosurgery 1999. Karger, Basel, pp 240–246
100.
Della Seta M, Collettini F, Chapiro J, Angelidis A, Engeling F, Hamm B, Kaul D (2019) 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 60:1496–1503PubMedCrossRef
101.
Tini P, Nardone V, Pastina P, Battaglia G, Vinciguerra C, Carfagno T, Rubino G, Carbone SF, Sebaste L, Cerase A, Federico A, Pirtoli L (2017) Perilesional edema in brain metastasis from non-small cell lung cancer (NSCLC) as predictor of response to radiosurgery (SRS). Neurol Sci 38:975–982PubMedCrossRef
102.
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 38:5437–5445PubMedCrossRef
103.
104.
Sneed PK, Mendez J, Vemer-van den Hoek JG, Seymour ZA, Ma L, Molinaro AM, Fogh SE, Nakamura JL, McDermott MW (2015) Adverse radiation effect after stereotactic radiosurgery for brain metastases: incidence, time course, and risk factors. J Neurosurg 123:373–386CrossRefPubMed
105.
Kondziolka D, Shin SM, Brunswick A, Kim I, Silverman JS (2015) The biology of radiosurgery and its clinical applications for brain tumors. Neuro Oncol 17:29–44PubMedCrossRef
106.
Peng L, Parekh V, Huang P, Lin DD, Sheikh K, Baker B, Kirschbaum T, Silvestri F, Son J, Robinson A, Huang E, Ames H, Grimm J, Chen L, Shen C, Soike M, McTyre E, Redmond K, Lim M, Lee J, Jacobs MA, Kleinberg L (2018) Distinguishing true progression from radionecrosis after stereotactic radiation therapy for brain metastases with machine learning and radiomics. Int J Radiat Oncol Biol Phys 102:1236–1243PubMedPubMedCentralCrossRef
107.
Spitzer M, Lorkowski S, Cullen P, Sczyrba A, Fuellen G (2006) IsoSVM—distinguishing isoforms and paralogs on the protein level. BMC Bioinformatics 7:110PubMedPubMedCentralCrossRef
108.
Zhang Z, Yang J, Ho A, Jiang W, Logan J, Wang X, Brown PD, McGovern SL, Guha-Thakurta N, Ferguson SD, Fave X, Zhang L, Mackin D, Court LE, Li J (2018) A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images. Eur Radiol 28:2255–2263PubMedCrossRef
109.
Ceccon G, Lohmann P, Stoffels G, Judov N, Filss CP, Rapp M, Bauer E, Hamisch C, Ruge MI, Kocher M, Kuchelmeister K, Sellhaus B, Sabel M, Fink GR, Shah NJ, Langen KJ, Galldiks N (2017) Dynamic O‑(2-18F-fluoroethyl)-L-tyrosine positron emission tomography differentiates brain metastasis recurrence from radiation injury after radiotherapy. Neuro Oncol 19:281–288PubMed
110.
Lohmann P, Stoffels G, Ceccon G, Rapp M, Sabel M, Filss CP, Kamp MA, Stegmayr C, Neumaier B, Shah NJ, Langen KJ, Galldiks N (2017) 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 27:2916–2927PubMedCrossRef
111.
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 20:537–542PubMedPubMedCentralCrossRef
112.
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. https://​doi.​org/​10.​1148/​radiol.​2020191145:​191145CrossRefPubMed
Metadata
Title
Applications of radiomics and machine learning for radiotherapy of malignant brain tumors
Authors
Prof. Martin Kocher
Maximilian I. Ruge
Norbert Galldiks
Philipp Lohmann
Publication date
01-10-2020
Publisher
Springer Berlin Heidelberg
Published in
Strahlentherapie und Onkologie / Issue 10/2020
Print ISSN: 0179-7158
Electronic ISSN: 1439-099X
DOI
https://doi.org/10.1007/s00066-020-01626-8

Other articles of this Issue 10/2020

Strahlentherapie und Onkologie 10/2020 Go to the issue

Review Article

Radiomics in radiation oncology—basics, methods, and limitations

Literatur kommentiert

Die Herzmortalität nach Strahlentherapie, Chemotherapie und endokriner Therapie des Mammakarzinoms

Review Article

The role of radiomics in prostate cancer radiotherapy

Review Article

Radiomics for liver tumours

Editorial

Artificial intelligence and radiomics for radiation oncology

Original Article

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.