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Erschienen in: European Radiology 12/2020

01.08.2020 | Neuro

Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas

verfasst von: Chae Jung Park, Kyunghwa Han, Hwiyoung Kim, Sung Soo Ahn, Yoon Seong Choi, Yae Won Park, Jong Hee Chang, Se Hoon Kim, Rajan Jain, Seung-Koo Lee

Erschienen in: European Radiology | Ausgabe 12/2020

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Abstract

Objectives

Isocitrate dehydrogenase wild-type (IDHwt) lower-grade gliomas of histologic grades II and III follow heterogeneous clinical outcomes, which necessitates risk stratification. We aimed to evaluate whether radiomics from MRI would allow prediction of overall survival in patients with IDHwt lower-grade gliomas and to investigate the added prognostic value of radiomics over clinical features.

Methods

Preoperative MRIs of 117 patients with IDHwt lower-grade gliomas from January 2007 to February 2018 were retrospectively analyzed. The external validation cohort consisted of 33 patients from The Cancer Genome Atlas. A total of 182 radiomic features were extracted. Radiomics risk scores (RRSs) for overall survival were derived from the least absolute shrinkage and selection operator (LASSO) and elastic net. Multivariable Cox regression analyses, including clinical features and RRSs, were performed. The integrated areas under the receiver operating characteristic curves (iAUCs) from models with and without RRSs were calculated for comparisons. The prognostic value of RRS was assessed in the validation cohort.

Results

The RRS derived from LASSO and elastic net independently predicted survival with hazard ratios of 9.479 (95% confidence interval [CI], 3.220–27.847) and 6.148 (95% CI, 3.009–12.563), respectively. Those RRSs enhanced model performance for predicting overall survival (iAUC increased to 0.780–0.797 from 0.726), which was externally validated. The RRSs stratified IDHwt lower-grade gliomas in the validation cohort with significantly different survival.

Conclusion

Radiomics has the potential for noninvasive risk stratification and can improve prediction of overall survival in patients with IDHwt lower-grade gliomas when integrated with clinical features.

Key Points

Isocitrate dehydrogenase wild-type lower-grade gliomas with histologic grades II and III follow heterogeneous clinical outcomes, which necessitates further risk stratification.
Radiomics risk scores derived from MRI independently predict survival even after incorporating strong clinical prognostic features (hazard ratios 6.148–9.479).
Radiomics risk scores derived from MRI have the potential to improve survival prediction when added to clinical features (integrated areas under the receiver operating characteristic curves increased from 0.726 to 0.780–0.797).
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Literatur
1.
Zurück zum Zitat Brat DJ, Verhaak RG, Aldape KD et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372:2481–2498CrossRef Brat DJ, Verhaak RG, Aldape KD et al (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med 372:2481–2498CrossRef
2.
Zurück zum Zitat Yan H, Parsons DW, Jin G et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773CrossRef Yan H, Parsons DW, Jin G et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773CrossRef
3.
Zurück zum Zitat Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820CrossRef Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820CrossRef
4.
Zurück zum Zitat Jiao Y, Killela PJ, Reitman ZJ et al (2012) Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas. Oncotarget 3:709–722CrossRef Jiao Y, Killela PJ, Reitman ZJ et al (2012) Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas. Oncotarget 3:709–722CrossRef
5.
Zurück zum Zitat Metellus P, Coulibaly B, Colin C et al (2010) Absence of IDH mutation identifies a novel radiologic and molecular subtype of WHO grade II gliomas with dismal prognosis. Acta Neuropathol 120:719–729CrossRef Metellus P, Coulibaly B, Colin C et al (2010) Absence of IDH mutation identifies a novel radiologic and molecular subtype of WHO grade II gliomas with dismal prognosis. Acta Neuropathol 120:719–729CrossRef
6.
Zurück zum Zitat Eckel-Passow JE, Lachance DH, Molinaro AM et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508CrossRef Eckel-Passow JE, Lachance DH, Molinaro AM et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508CrossRef
7.
Zurück zum Zitat Reuss DE, Kratz A, Sahm F et al (2015) Adult IDH wild type astrocytomas biologically and clinically resolve into other tumor entities. Acta Neuropathol 130:407–417CrossRef Reuss DE, Kratz A, Sahm F et al (2015) Adult IDH wild type astrocytomas biologically and clinically resolve into other tumor entities. Acta Neuropathol 130:407–417CrossRef
8.
Zurück zum Zitat Aibaidula A, Chan AK, Shi Z et al (2017) Adult IDH wild-type lower-grade gliomas should be further stratified. Neuro Oncol 19:1327–1337CrossRef Aibaidula A, Chan AK, Shi Z et al (2017) Adult IDH wild-type lower-grade gliomas should be further stratified. Neuro Oncol 19:1327–1337CrossRef
9.
Zurück zum Zitat Chan AK, Yao Y, Zhang Z et al (2015) TERT promoter mutations contribute to subset prognostication of lower-grade gliomas. Mod Pathol 28:177–186CrossRef Chan AK, Yao Y, Zhang Z et al (2015) TERT promoter mutations contribute to subset prognostication of lower-grade gliomas. Mod Pathol 28:177–186CrossRef
10.
Zurück zum Zitat Chan AK, Yao Y, Zhang Z et al (2015) Combination genetic signature stratifies lower-grade gliomas better than histological grade. Oncotarget 6:20885–20901CrossRef Chan AK, Yao Y, Zhang Z et al (2015) Combination genetic signature stratifies lower-grade gliomas better than histological grade. Oncotarget 6:20885–20901CrossRef
11.
Zurück zum Zitat Brat DJ, Aldape K, Colman H et al (2018) cIMPACT-NOW update 3: recommended diagnostic criteria for “diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV”. Acta Neuropathol 136:805–810CrossRef Brat DJ, Aldape K, Colman H et al (2018) cIMPACT-NOW update 3: recommended diagnostic criteria for “diffuse astrocytic glioma, IDH-wildtype, with molecular features of glioblastoma, WHO grade IV”. Acta Neuropathol 136:805–810CrossRef
12.
Zurück zum Zitat Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577CrossRef
13.
Zurück zum Zitat Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRef Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRef
14.
Zurück zum Zitat Kickingereder P, Gotz M, Muschelli J et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRef Kickingereder P, Gotz M, Muschelli J et al (2016) Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 22:5765–5771CrossRef
15.
Zurück zum Zitat Bae S, Choi YS, Ahn SS et al (2018) Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 289:797–806CrossRef Bae S, Choi YS, Ahn SS et al (2018) Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology 289:797–806CrossRef
16.
Zurück zum Zitat Hu LS, Ning S, Eschbacher JM et al (2017) Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 19:128–137CrossRef Hu LS, Ning S, Eschbacher JM et al (2017) Radiogenomics to characterize regional genetic heterogeneity in glioblastoma. Neuro Oncol 19:128–137CrossRef
17.
Zurück zum Zitat 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 280:880–889CrossRef 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 280:880–889CrossRef
18.
Zurück zum Zitat Zhou H, Vallieres M, Bai HX et al (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19:862–870CrossRef Zhou H, Vallieres M, Bai HX et al (2017) MRI features predict survival and molecular markers in diffuse lower-grade gliomas. Neuro Oncol 19:862–870CrossRef
19.
Zurück zum Zitat Liu X, Li Y, Qian Z et al (2018) A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin 20:1070–1077CrossRef Liu X, Li Y, Qian Z et al (2018) A radiomic signature as a non-invasive predictor of progression-free survival in patients with lower-grade gliomas. Neuroimage Clin 20:1070–1077CrossRef
20.
Zurück zum Zitat Zhang X, Tian Q, Wang L et al (2018) Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging 48:916–926CrossRef Zhang X, Tian Q, Wang L et al (2018) Radiomics strategy for molecular subtype stratification of lower-grade glioma: detecting IDH and TP53 mutations based on multimodal MRI. J Magn Reson Imaging 48:916–926CrossRef
21.
Zurück zum Zitat Ren Y, Zhang X, Rui W et al (2019) Noninvasive prediction of IDH1 mutation and ATRX expression loss in low-grade gliomas using multiparametric MR radiomic features. J Magn Reson Imaging 49:808–817CrossRef Ren Y, Zhang X, Rui W et al (2019) Noninvasive prediction of IDH1 mutation and ATRX expression loss in low-grade gliomas using multiparametric MR radiomic features. J Magn Reson Imaging 49:808–817CrossRef
22.
Zurück zum Zitat Li Y, Qian Z, Xu K et al (2018) MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Neuroimage Clin 17:306–311CrossRef Li Y, Qian Z, Xu K et al (2018) MRI features predict p53 status in lower-grade gliomas via a machine-learning approach. Neuroimage Clin 17:306–311CrossRef
23.
Zurück zum Zitat Liu X, Li Y, Li S et al (2019) IDH mutation-specific radiomic signature in lower-grade gliomas. Aging (Albany NY) 11:673–696CrossRef Liu X, Li Y, Li S et al (2019) IDH mutation-specific radiomic signature in lower-grade gliomas. Aging (Albany NY) 11:673–696CrossRef
24.
Zurück zum Zitat Pedano N, Flanders A, Scarpace L et al (2016) Radiology data from the cancer genome atlas low grade glioma [TCGA-LGG] collection. Cancer Imaging Archive Pedano N, Flanders A, Scarpace L et al (2016) Radiology data from the cancer genome atlas low grade glioma [TCGA-LGG] collection. Cancer Imaging Archive
25.
Zurück zum Zitat Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRef Tustison NJ, Avants BB, Cook PA et al (2010) N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29:1310–1320CrossRef
26.
Zurück zum Zitat Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97CrossRef Sled JG, Zijdenbos AP, Evans AC (1998) A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans Med Imaging 17:87–97CrossRef
27.
Zurück zum Zitat Shinohara RT, Sweeney EM, Goldsmith J et al (2014) Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 6:9–19CrossRef Shinohara RT, Sweeney EM, Goldsmith J et al (2014) Statistical normalization techniques for magnetic resonance imaging. Neuroimage Clin 6:9–19CrossRef
28.
Zurück zum Zitat Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341CrossRef Fedorov A, Beichel R, Kalpathy-Cramer J et al (2012) 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging 30:1323–1341CrossRef
29.
Zurück zum Zitat Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:29CrossRef Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15:29CrossRef
30.
Zurück zum Zitat Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH (2017) Quality of radiomic features in glioblastoma multiforme: impact of semi-automated tumor segmentation software. Korean J Radiol 18:498–509CrossRef Lee M, Woo B, Kuo MD, Jamshidi N, Kim JH (2017) Quality of radiomic features in glioblastoma multiforme: impact of semi-automated tumor segmentation software. Korean J Radiol 18:498–509CrossRef
31.
Zurück zum Zitat van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRef van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107CrossRef
32.
Zurück zum Zitat Takano S, Tian W, Matsuda M et al (2011) Detection of IDH1 mutation in human gliomas: comparison of immunohistochemistry and sequencing. Brain Tumor Pathol 28:115–123CrossRef Takano S, Tian W, Matsuda M et al (2011) Detection of IDH1 mutation in human gliomas: comparison of immunohistochemistry and sequencing. Brain Tumor Pathol 28:115–123CrossRef
33.
Zurück zum Zitat Choi J, Lee EY, Shin KJ, Minn YK, Kim J, Kim SH (2013) IDH1 mutation analysis in low cellularity specimen: a limitation of diagnostic accuracy and a proposal for the diagnostic procedure. Pathol Res Pract 209:284–290CrossRef Choi J, Lee EY, Shin KJ, Minn YK, Kim J, Kim SH (2013) IDH1 mutation analysis in low cellularity specimen: a limitation of diagnostic accuracy and a proposal for the diagnostic procedure. Pathol Res Pract 209:284–290CrossRef
34.
Zurück zum Zitat Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22CrossRef Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22CrossRef
36.
Zurück zum Zitat Simon N, Friedman J, Hastie T, Tibshirani R (2011) Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw 39:1–13CrossRef Simon N, Friedman J, Hastie T, Tibshirani R (2011) Regularization paths for Cox’s proportional hazards model via coordinate descent. J Stat Softw 39:1–13CrossRef
37.
Zurück zum Zitat Heagerty PJ, Zheng Y (2005) Survival model predictive accuracy and ROC curves. Biometrics 61:92–105CrossRef Heagerty PJ, Zheng Y (2005) Survival model predictive accuracy and ROC curves. Biometrics 61:92–105CrossRef
38.
Zurück zum Zitat Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:716–723 Akaike H (1974) A new look at the statistical model identification. IEEE Transactions on Automatic Control 19:716–723
39.
Zurück zum Zitat Contal C, O’Quigley J (1999) An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 30:253–270CrossRef Contal C, O’Quigley J (1999) An application of changepoint methods in studying the effect of age on survival in breast cancer. Comput Stat Data Anal 30:253–270CrossRef
40.
Zurück zum Zitat Kickingereder P, Neuberger U, Bonekamp D et al (2018) Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 20:848–857CrossRef Kickingereder P, Neuberger U, Bonekamp D et al (2018) Radiomic subtyping improves disease stratification beyond key molecular, clinical, and standard imaging characteristics in patients with glioblastoma. Neuro Oncol 20:848–857CrossRef
41.
Zurück zum Zitat Ingrisch M, Schneider MJ, Norenberg D et al (2017) Radiomic analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 52:360–366CrossRef Ingrisch M, Schneider MJ, Norenberg D et al (2017) Radiomic analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 52:360–366CrossRef
42.
Zurück zum Zitat Soni N, Priya S, Bathla G (2019) Texture Analysis in Cerebral Gliomas: A Review of the Literature. AJNR Am J Neuroradiol 40:928–934 Soni N, Priya S, Bathla G (2019) Texture Analysis in Cerebral Gliomas: A Review of the Literature. AJNR Am J Neuroradiol 40:928–934
44.
Zurück zum Zitat Meyer M, Reimand J, Lan X et al (2015) Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc Natl Acad Sci U S A 112:851–856CrossRef Meyer M, Reimand J, Lan X et al (2015) Single cell-derived clonal analysis of human glioblastoma links functional and genomic heterogeneity. Proc Natl Acad Sci U S A 112:851–856CrossRef
45.
Zurück zum Zitat Liu Y, Zhang X, Feng N et al (2018) The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis. Acta Radiol 59:1239–1246CrossRef Liu Y, Zhang X, Feng N et al (2018) The effect of glioblastoma heterogeneity on survival stratification: a multimodal MR imaging texture analysis. Acta Radiol 59:1239–1246CrossRef
46.
Zurück zum Zitat Yang D, Rao G, Martinez J, Veeraraghavan A, Rao A (2015) Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Med Phys 42:6725–6735CrossRef Yang D, Rao G, Martinez J, Veeraraghavan A, Rao A (2015) Evaluation of tumor-derived MRI-texture features for discrimination of molecular subtypes and prediction of 12-month survival status in glioblastoma. Med Phys 42:6725–6735CrossRef
Metadaten
Titel
Radiomics risk score may be a potential imaging biomarker for predicting survival in isocitrate dehydrogenase wild-type lower-grade gliomas
verfasst von
Chae Jung Park
Kyunghwa Han
Hwiyoung Kim
Sung Soo Ahn
Yoon Seong Choi
Yae Won Park
Jong Hee Chang
Se Hoon Kim
Rajan Jain
Seung-Koo Lee
Publikationsdatum
01.08.2020
Verlag
Springer Berlin Heidelberg
Erschienen in
European Radiology / Ausgabe 12/2020
Print ISSN: 0938-7994
Elektronische ISSN: 1432-1084
DOI
https://doi.org/10.1007/s00330-020-07089-w

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