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
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Journal of Neuro-Oncology
Published in: Journal of Neuro-Oncology 2/2018

01-11-2018 | Clinical Study

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas

Authors: Yuqi Han, Zhen Xie, Yali Zang, Shuaitong Zhang, Dongsheng Gu, Mu Zhou, Olivier Gevaert, Jingwei Wei, Chao Li, Hongyan Chen, Jiang Du, Zhenyu Liu, Di Dong, Jie Tian, Dabiao Zhou

Published in: Journal of Neuro-Oncology | Issue 2/2018

Login to get access

Abstract

Purpose

To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas.

Methods

This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93).

Results

The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction.

Conclusion

Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.
Appendix
Available only for authorised users
Literature
1.
Chen B, Liang T, Yang P et al (2016) Classifying lower grade glioma cases according to whole genome gene expression. Oncotarget 7(45):74031–74042PubMedPubMedCentral
2.
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 131:803–820CrossRefPubMed
3.
Network CGAR (2015) Comprehensive, integrative genomic analysis of diffuse low-grade gliomas. N Engl J Med 372:2481–2498CrossRef
4.
Smith JS, Perry A, Borell TJ et al (2000) Alterations of chromosome arms 1p Fand 19q as predictors of survival in oligodendrogliomas, astrocytomas, and mixed oligoastrocytomas. J Clin Oncol 18:636–636CrossRefPubMed
5.
Lindberg N, Jiang Y, Xie Y et al (2013) Oncogenic signaling is dominant to cell of origin and dictates astrocytic or oligodendroglial tumor development from oligodendrocyte precursor cells. J Neurosci 33(42):16805–16817CrossRef
6.
7.
Bauman G, Ino Y, Ueki K et al (2000) Allelic loss of chromosome 1p and radiotherapy plus chemotherapy in patients with oligodendrogliomas. Int J Radiat Oncol Biol Phys 48:825–830CrossRefPubMed
8.
Ino Y, Betensky RA, Zlatescu MC et al (2001) Molecular subtypes of anaplastic oligodendroglioma. Clin Cancer Res 7:839–845PubMed
9.
Kaloshi G, Benouaich-Amiel A, Diakite F et al (2007) Temozolomide for low-grade gliomas predictive impact of 1p/19q loss on response and outcome. Neurology 68:1831–1836CrossRefPubMed
10.
Reifenberger J, Reifenberger G, Liu L et al (1994) Molecular genetic analysis of oligodendroglial tumors shows preferential allelic deletions on 19q and 1p. Am J Pathol 145:1175–1190PubMedPubMedCentral
11.
12.
13.
Sanai N, Martino J, Berger MS (2012) Morbidity profile following aggressive resection of parietal lobe gliomas: clinical article. J Neurosurg 116:1182–1186CrossRefPubMed
14.
Tate MC, Kim C-Y, Chang EF et al (2011) Assessment of morbidity following resection of cingulate gyrus gliomas: clinical article. J Neurosurg 114:640–647CrossRefPubMed
15.
Ducray F, Idbaih A, Reyniès AD et al (2008) Anaplastic oligodendrogliomas with 1p19q codeletion have a proneural gene expression profile. Mol Cancer 7(1):41CrossRefPubMedPubMedCentral
16.
Mukasa A, Ueki K, Ge X et al (2010) Selective expression of a subset of neuronal genes in oligodendroglioma with chromosome 1p loss. Brain Pathol 14(1):34–42CrossRef
17.
Van den Bent MJ, Smits M, Kros JM et al (2017) Diffuse infiltrating oligodendroglioma and astrocytoma. J Clin Oncol 35(21):JCO2017726737
18.
Jenkinson MD, Du PD, Smith TS et al (2006) Histological growth patterns and genotype in oligodendroglial tumours: correlation with MRI features. Brain 129(Pt 7):1884CrossRefPubMed
19.
Megyesi JF, Kachur E, Lee DH et al (2004) Imaging correlates of molecular signatures in oligodendrogliomas. Clin Cancer Res 10:4303–4306CrossRefPubMed
20.
21.
22.
Lambin P, Rios-Velazquez E, Leijenaar R et al (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446CrossRefPubMedPubMedCentral
23.
24.
Zhou M, Hall L, Goldgof D et al (2014) Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol 7:5–13CrossRefPubMedPubMedCentral
25.
Zhou M, Scott J, Chaudhury B et al (2017) Radiomics in brain tumor: image assessment, quantitative feature descriptors, and machine-learning approaches. Am J Neuroradiol 39(12):208–216PubMed
26.
Zhou M, Chaudhury B, Hall LO et al (2017) Identifying spatial imaging biomarkers of glioblastoma multiforme for survival group prediction. J Magn Reson Imaging 46(1):115–123CrossRefPubMed
27.
Aerts HJWL, Velazquez ER, Leijenaar RTH et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006CrossRefPubMedPubMedCentral
28.
Huang YQ, Liu ZY et al (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281(3):947CrossRefPubMed
29.
Huang YQ, Liang CH, He L et al (2016) Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 34:2157–2164CrossRefPubMed
30.
Henson JW, Gaviani P, Gonzalez RG (2005) MRI in treatment of adult gliomas. Lancet Oncol 6:167–175CrossRefPubMed
31.
Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31:1116–1128CrossRefPubMed
32.
Gebejes A, Huertas R (2013) Texture characterization based on grey-level co-occurrence matrix. Proc Conf Inf Manag Sci 2:375–378
33.
Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graph Image Process 4:172–179CrossRef
34.
Chu A, Sehgal CM, Greenleaf JF (1990) Use of gray value distribution of run lengths for texture analysis. Pattern Recognit Lett 11:415–419CrossRef
35.
Dasarathy BV, Holder EB (1991) Image characterizations based on joint gray level run length distributions. Pattern Recognit Lett 12:497–502CrossRef
36.
Thibault G, Fertil B, Navarro C et al (2013) Shape and texture indexes application to cell nuclei classification. Int J Pattern Recognit Artif Intell 27:1357002CrossRef
37.
Amadasun M, King R (1989) Textural features corresponding to textural properties. IEEE Trans Syst Man Cybern 19:1264–1274CrossRef
38.
Kim SH, Kim H, Kim TS (2005) Clinical, histological, and immunohistochemical features predicting 1p/19q loss of heterozygosity in oligodendroglial tumors. Acta Neuropathol 110:27–38CrossRefPubMed
39.
DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRef
40.
Louis BN, Jana P, Joachim B et al (2018) NCCN Guidelines Version 1.2018 Panel Members Central Nervous System Cancers. National Comprehensive Cancer Network
41.
Buckner J, Giannini C, Eckelpassow J et al (2017) Management of diffuse low-grade gliomas in adults - use of molecular diagnostics. Nat Rev Neurol 13(6):340–351CrossRefPubMed
42.
Chahlavi A, Kanner A, Peereboom D et al (2003) Impact of chromosome 1p status in response of oligodendroglioma to temozolomide: preliminary results. J Neurooncol 61:267–273CrossRefPubMed
43.
Alattar AA, Brandel MG, Hirshman BR et al (2017) Oligodendroglioma resection: a surveillance, epidemiology, and end results (SEER) analysis. J Neurosurg 128:1076–1083CrossRef
44.
Yang XF et al (2012) Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: an in vivo study of late toxicity. Med Phys 39(9):5732CrossRefPubMedPubMedCentral
45.
Brown R, Zlatescu M, Sijben A et al (2008) The use of magnetic resonance imaging to noninvasively detect genetic signatures in oligodendroglioma. Clin Cancer Res 14:2357–2362CrossRefPubMed
46.
Sanai N, Chang S, Berger MS (2011) Low-grade gliomas in adults: a review. J Neurosurg 115:1–18
Metadata
Title
Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas
Authors
Yuqi Han
Zhen Xie
Yali Zang
Shuaitong Zhang
Dongsheng Gu
Mu Zhou
Olivier Gevaert
Jingwei Wei
Chao Li
Hongyan Chen
Jiang Du
Zhenyu Liu
Di Dong
Jie Tian
Dabiao Zhou
Publication date
01-11-2018
Publisher
Springer US
Published in
Journal of Neuro-Oncology / Issue 2/2018
Print ISSN: 0167-594X
Electronic ISSN: 1573-7373
DOI
https://doi.org/10.1007/s11060-018-2953-y

Other articles of this Issue 2/2018

Journal of Neuro-Oncology 2/2018 Go to the issue

Laboratory Investigation

UAB30, a novel RXR agonist, decreases tumorigenesis and leptomeningeal disease in group 3 medulloblastoma patient-derived xenografts

Clinical Study

Impact of adjuvant treatments on survival in Korean patients with WHO grade II gliomas: KNOG 15-02 and KROG 16-04 intergroup study

Correction

Correction to: One decade of glioblastoma multiforme surgery in 342 elderly patients: what have we learned?

Clinical Study

Feasibility of dose escalation using intraoperative radiotherapy following resection of large brain metastases compared to post-operative stereotactic radiosurgery

Laboratory Investigation

Bevacizumab, irinotecan, temozolomide, tyrosine kinase inhibition, and MEK inhibition are effective against pleomorphic xanthoastrocytoma regardless of V600E status

Clinical Study

Fotemustine, teniposide and dexamethasone versus high-dose methotrexate plus cytarabine in newly diagnosed primary CNS lymphoma: a randomised phase 2 trial