nach oben

Erschienen in:

24.11.2017 | Oncology

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images

verfasst von: Zijian Zhang, Jinzhong Yang, Angela Ho, Wen Jiang, Jennifer Logan, Xin Wang, Paul D. Brown, Susan L. McGovern, Nandita Guha-Thakurta, Sherise D. Ferguson, Xenia Fave, Lifei Zhang, Dennis Mackin, Laurence E. Court, Jing Li

Erschienen in: European Radiology | Ausgabe 6/2018

Einloggen, um Zugang zu erhalten

Abstract

Objectives

To develop a model using radiomic features extracted from MR images to distinguish radiation necrosis from tumour progression in brain metastases after Gamma Knife radiosurgery.

Methods

We retrospectively identified 87 patients with pathologically confirmed necrosis (24 lesions) or progression (73 lesions) and calculated 285 radiomic features from four MR sequences (T1, T1 post-contrast, T2, and fluid-attenuated inversion recovery) obtained at two follow-up time points per lesion per patient. Reproducibility of each feature between the two time points was calculated within each group to identify a subset of features with distinct reproducible values between two groups. Changes in radiomic features from one time point to the next (delta radiomics) were used to build a model to classify necrosis and progression lesions.

Results

A combination of five radiomic features from both T1 post-contrast and T2 MR images were found to be useful in distinguishing necrosis from progression lesions. Delta radiomic features with a RUSBoost ensemble classifier had an overall predictive accuracy of 73.2% and an area under the curve value of 0.73 in leave-one-out cross-validation.

Conclusions

Delta radiomic features extracted from MR images have potential for distinguishing radiation necrosis from tumour progression after radiosurgery for brain metastases.

Key points

• Some radiomic features showed better reproducibility for progressive lesions than necrotic ones

• Delta radiomic features can help to distinguish radiation necrosis from tumour progression

• Delta radiomic features had better predictive value than did traditional radiomic features

Nayak L, Lee EQ, Wen PY (2012) Epidemiology of brain metastases. Current Oncology Reports 14:48–54CrossRefPubMed

Kondziolka D, Martin JJ, Flickinger JC et al (2005) Long-term survivors after gamma knife radiosurgery for brain metastases. Cancer 104:2784–2791CrossRefPubMed

Elaimy AL, Mackay AR, Lamoreaux WT et al (2011) Clinical outcomes of stereotactic radiosurgery in the treatment of patients with metastatic brain tumors. World Neurosurg 75:673–683CrossRefPubMed

Minniti G, Clarke E, Lanzetta G et al (2011) Stereotactic radiosurgery for brain metastases: analysis of outcome and risk of brain radionecrosis. Radiation Oncology 6:48CrossRefPubMedPubMedCentral

Shaw E, Scott C, Souhami L et al (2000) Single dose radiosurgical treatment of recurrent previously irradiated primary brain tumors and brain metastases: Final report of RTOG protocol 90-05. International Journal of Radiation Oncology Biology Physics 47:291–298CrossRef

Chuang MT, Liu YS, Tsai YS, Chen YC, Wang CK (2016) Differentiating Radiation-Induced Necrosis from Recurrent Brain Tumor Using MR Perfusion and Spectroscopy: A Meta-Analysis. PLoS ONE 11

Lai G, Mahadevan A, Hackney D et al (2015) Diagnostic Accuracy of PET, SPECT, and Arterial Spin-Labeling in Differentiating Tumor Recurrence from Necrosis in Cerebral Metastasis after Stereotactic Radiosurgery. American Journal of Neuroradiology 36:2250–2255CrossRefPubMed

Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nature Communications 5

Fehr D, Veeraraghavan H, Wibmer A et al (2015) Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proceedings of the National Academy of Sciences 112:E6265–E6273CrossRef

10.

Gevaert O, Mitchell LA, Achrol AS et al (2014) Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features. Radiology 273:168–174CrossRefPubMedPubMedCentral

11.

Gillies RJ, Kinahan PE, Hricak H (2015) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRefPubMedPubMedCentral

12.

Itakura H, Achrol AS, Mitchell LA et al (2015) Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Science translational medicine 7:303ra138–303ra138CrossRefPubMedPubMedCentral

13.

Yamamoto S, Han W, Kim Y et al (2015) Breast cancer: radiogenomic biomarker reveals associations among dynamic contrast-enhanced MR imaging, long noncoding RNA, and metastasis. Radiology 275:384–392CrossRefPubMed

14.

Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W (2003) PET-CT image registration in the chest using free-form deformations. IEEE Transactions On Medical Imaging 22:120–128CrossRefPubMed

15.

Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE (2015) IBEX: An open infrastructure software platform to facilitate collaborative work in radiomics. Medical Physics 42:1341–1353CrossRefPubMedPubMedCentral

16.

Yang J, Zhang L, Fave XJ et al (2016) Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors. Comput Med Imaging Graph 48:1–8CrossRefPubMed

17.

Burger W, Burge MJ (2013) Edge-Preserving Smoothing FiltersPrinciples of Digital Image Processing. Springer, pp 119-167

18.

Cunliffe AR, Armato Iii SG, Fei XM, Tuohy RE, Al-Hallaq HA (2013) Lung texture in serial thoracic CT scans: Registration-based methods to compare anatomically matched regionsa. Medical Physics 40:061906CrossRefPubMedPubMedCentral

19.

Fried DV, Tucker SL, Zhou S et al (2014) Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer. Int J Radiat Oncol Biol Phys 90:834–842CrossRefPubMedPubMedCentral

20.

Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE T. Syst. Man Cyb. 3:610–621CrossRef

21.

Tang X (1998) Texture information in run-length matrices. IEEE Transactions on Image Processing 7:1602–1609CrossRefPubMed

22.

Legland D, Kiêu K, Devaux M-F (2007) Computation of Minkowski measures on 2D and 3D binary images. Image Anal Stereol 26:83–92CrossRef

23.

Basu S (2012) Developing predictive models for lung tumor analysis.

24.

Amadasun M, King R (1989) Textural Features Corresponding to Textural Properties. Ieee Transactions on Systems Man and Cybernetics 19:1264–1274CrossRef

25.

Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) pp 886-893

26.

Tiwari P, Prasanna P, Wolansky L et al (2016) Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study. AJNR Am J Neuroradiol. https://doi.org/10.3174/ajnr.A4931

27.

Galizia MS, Töre HG, Chalian H, McCarthy R, Salem R, Yaghmai V (2012) MDCT necrosis quantification in the assessment of hepatocellular carcinoma response to yttrium 90 radioembolization therapy: comparison of two-dimensional and volumetric techniques. Academic Radiology 19:48–54CrossRefPubMed

28.

Lin LIK (1989) A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 45:255–268CrossRefPubMed

29.

Duda RO, Hart PE, Stork DG (2001) Pattern Classification. John Wiley and Sons, Inc.

30.

Seiffert C, Khoshgoftaar TM, Van Hulse J, Napolitano A (2010) RUSBoost: A Hybrid Approach to Alleviating Class Imbalance. Ieee Transactions on Systems Man and Cybernetics Part a-Systems and Humans 40:185-197

31.

Barajas RF, Chang JS, Segal MR et al (2009) Differentiation of Recurrent Glioblastoma Multiforme from Radiation Necrosis after External Beam Radiation Therapy with Dynamic Susceptibility-weighted Contrast-enhanced Perfusion MR Imaging. Radiology 253:486–496CrossRefPubMedPubMedCentral

32.

Kickingereder P, Burth S, Wick A et al (2016) Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models. Radiology 280:880–889CrossRefPubMed

33.

Li H, Zhu Y, Burnside ES et al (2016) MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology:152110

34.

Larroza A, Moratal D, Paredes-Sánchez A et al (2015) Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI. Journal of Magnetic Resonance Imaging 42:1362–1368CrossRefPubMed

35.

Shinohara RT, Sweeney EM, Goldsmith J et al (2014) Statistical normalization techniques for magnetic resonance imaging. NeuroImage: Clinical 6:9–19CrossRef

Titel: A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images
verfasst von: Zijian Zhang
Jinzhong Yang
Angela Ho
Wen Jiang
Jennifer Logan
Xin Wang
Paul D. Brown
Susan L. McGovern
Nandita Guha-Thakurta
Sherise D. Ferguson
Xenia Fave
Lifei Zhang
Dennis Mackin
Laurence E. Court
Jing Li
Publikationsdatum: 24.11.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: European Radiology / Ausgabe 6/2018
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-5154-8

Update Radiologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.

Newsletter bestellen

Springer Medizin

A predictive model for distinguishing radiation necrosis from tumour progression after gamma knife radiosurgery based on radiomic features from MR images