Abstract
Purpose
To investigate the value of histogram analysis of postcontrast T1-weighted (T1C) and apparent diffusion coefficient (ADC) images in predicting the grade and proliferative activity of adult intracranial ependymomas.
Methods
Forty-seven adult intracranial ependymomas were enrolled and underwent histogram parameters extraction (including minimum, maximum, mean, 1st percentile (Perc.01), Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy of T1C and ADC) using FireVoxel software. Differences in histogram parameters between grade 2 and grade 3 adult intracranial ependymomas were compared. Receiver operating characteristic curves and logistic regression analyses were conducted to evaluate the diagnostic performance. Spearman’s correlation analysis was used to evaluate the relationship between histogram parameters and Ki-67 proliferation index.
Results
Grade 3 intracranial ependymomas group showed significantly higher Perc.95, Perc.99, SD, variance, CV, and entropy of T1C; lower minimum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50 of ADC; and higher CV and entropy of ADC than grade 2 intracranial ependymomas group (all p < 0.05). Entropy (T1C) and Perc.10 (ADC) had a higher diagnostic performance with AUCs of 0.805 and 0.827 among the histogram parameters of T1C and ADC, respectively. The diagnostic performance was improved by combining entropy (T1C) and Perc.10 (ADC), with an AUC of 0.857. Significant correlations were observed between significant histogram parameters of T1C (r = 0.296–0.417, p = 0.001–0.044) and ADC (r = -0.428–0.395, p = 0.003–0.038).
Conclusion
Whole-tumor histogram analysis of T1C and ADC may be a promising approach for predicting the grade and proliferative activity of adult intracranial ependymomas.
Similar content being viewed by others
Data availability
The datasets generated and/or analyzed during the current study are not publicly available; however, they can be provided by the corresponding author on reasonable request.
Abbreviations
- CNS:
-
Central nervous system
- T1C:
-
Postcontrast T1-weighted images
- DWI:
-
Diffusion-weighted imaging
- ADC:
-
Apparent diffusion coefficient
- TE:
-
Echo time
- TR:
-
Repetition time
- FOV:
-
Field of view
- SD:
-
Standard deviation
- CV:
-
Coefficient of variation
- ROC:
-
Receiver operating characteristic curve
- AUC:
-
Area under the curve
- ROI:
-
Region of interest
- CI:
-
Confidence intervals
- ICC:
-
Intraclass correlation coefficient
References
Lombardi G, Della Puppa A, Pizzi M, Cerretti G, Bonaudo C, Gardiman MP, Dipasquale A, Gregucci F, Esposito A, De Bartolo D, Zagonel V, Simonelli M, Fiorentino A, Ducray F (2021) An overview of intracranial ependymomas in adults. Cancers (Basel) 13. https://doi.org/10.3390/cancers13236128
Kresbach C, Neyazi S, Schuller U (2022) Updates in the classification of ependymal neoplasms: The 2021 WHO classification and beyond. Brain Pathol 32:e13068. https://doi.org/10.1111/bpa.13068
Smith HL, Wadhwani N, Horbinski C (2022) Major features of the 2021 WHO classification of CNS tumors. Neurotherapeutics 19:1691–1704. https://doi.org/10.1007/s13311-022-01249-0
Ruda R, Reifenberger G, Frappaz D, Pfister SM, Laprie A, Santarius T, Roth P, Tonn JC, Soffietti R, Weller M, Moyal EC (2018) EANO guidelines for the diagnosis and treatment of ependymal tumors. Neuro Oncol 20:445–456. https://doi.org/10.1093/neuonc/nox166
Deng X, Zhang X, Yang L, Lu X, Fang J, Yu L, Li D, Sheng H, Yin B, Zhang N, Lin J (2020) Personalizing age-specific survival prediction and risk stratification in intracranial grade II/III ependymoma. Cancer Med 9:615–625. https://doi.org/10.1002/cam4.2753
Lim KY, Lee K, Shim Y, Park JW, Kim H, Kang J, Won JK, Kim SK, Phi JH, Park CK, Chung CK, Yun H, Park SH (2022) Molecular subtyping of ependymoma and prognostic impact of Ki-67. Brain Tumor Pathol 39:1–13. https://doi.org/10.1007/s10014-021-00417-y
Xue C, Zhou Q, Zhang P, Zhang B, Sun Q, Li S, Deng J, Liu X, Zhou J (2023) MRI histogram analysis of tumor-infiltrating CD8+ T cell levels in patients with glioblastoma. Neuroimage Clin 37:103353. https://doi.org/10.1016/j.nicl.2023.103353
Gao A, Zhang H, Yan X, Wang S, Chen Q, Gao E, Qi J, Bai J, Zhang Y, Cheng J (2022) Whole-tumor histogram analysis of multiple diffusion metrics for glioma genotyping. Radiology 302:652–661. https://doi.org/10.1148/radiol.210820
Su Y, Kang J, Lin X, She D, Guo W, Xing Z, Yang X, Cao D (2023) Whole-tumor histogram analysis of diffusion and perfusion metrics for noninvasive pediatric glioma grading. Neuroradiology 65:1063–1071. https://doi.org/10.1007/s00234-023-03145-6
Zhang B, Zhou F, Zhou Q, Xue C, Ke X, Zhang P, Han T, Deng L, Jing M, Zhou J (2023) Whole-tumor histogram analysis of multi-parametric MRI for differentiating brain metastases histological subtypes in lung cancers: relationship with the Ki-67 proliferation index. Neurosurg Rev 46:218. https://doi.org/10.1007/s10143-023-02129-7
Vitanovics D, Balint K, Hanzely Z, Banczerowski P, Afra D (2010) Ependymoma in adults: surgery, reoperation and radiotherapy for survival. Pathol Oncol Res 16:93–99. https://doi.org/10.1007/s12253-009-9194-5
Dutzmann S, Schatlo B, Lobrinus A, Murek M, Wostrack M, Weiss C, Schaller K, Raabe A, Meyer B, Goldbrunner R, Franz K, Seifert V, Senft C (2013) A multi-center retrospective analysis of treatment effects and quality of life in adult patients with cranial ependymomas. J Neurooncol 114:319–327. https://doi.org/10.1007/s11060-013-1187-2
Korshunov A, Golanov A, Sycheva R, Timirgaz V (2004) The histologic grade is a main prognostic factor for patients with intracranial ependymomas treated in the microneurosurgical era: an analysis of 258 patients. Cancer 100:1230–1237. https://doi.org/10.1002/cncr.20075
Yuh EL, Barkovich AJ, Gupta N (2009) Imaging of ependymomas: MRI and CT. Childs Nerv Syst 25:1203–1213. https://doi.org/10.1007/s00381-009-0878-7
Nowak J, Seidel C, Pietsch T, Alkonyi B, Fuss TL, Friedrich C, von Hoff K, Rutkowski S, Warmuth-Metz M (2015) Systematic comparison of MRI findings in pediatric ependymoblastoma with ependymoma and CNS primitive neuroectodermal tumor not otherwise specified. Neuro Oncol 17:1157–1165. https://doi.org/10.1093/neuonc/nov063
Kuai XP, Wang SY, Lu YP, Xiong J, Geng DY, Yin B (2020) MRI features of intracranial anaplastic ependymomas: A comparison of Supratentorial and Infratentorial lesions. Front Oncol 10:1063. https://doi.org/10.3389/fonc.2020.01063
Xing Z, Zhou X, Xiao Z, She D, Wang X, Cao D (2020) Comparison of conventional, diffusion, and perfusion MRI between low-grade and anaplastic Extraventricular Ependymoma. AJR Am J Roentgenol 215:978–984. https://doi.org/10.2214/AJR.20.22764
Gihr GA, Horvath-Rizea D, Kohlhof-Meinecke P, Ganslandt O, Henkes H, Richter C, Hoffmann KT, Surov A, Schob S (2018) Histogram profiling of postcontrast T1-weighted MRI gives valuable insights into tumor biology and enables prediction of growth kinetics and prognosis in Meningiomas. Transl Oncol 11:957–961. https://doi.org/10.1016/j.tranon.2018.05.009
Li X, Miao Y, Han L, Dong J, Guo Y, Shang Y, Xie L, Song Q, Liu A (2019) Meningioma grading using conventional MRI histogram analysis based on 3D tumor measurement. Eur J Radiol 110:45–53. https://doi.org/10.1016/j.ejrad.2018.11.016
Yeo DM, Oh SN, Jung CK, Lee MA, Oh ST, Rha SE, Jung SE, Byun JY, Gall P, Son Y (2015) Correlation of dynamic contrast-enhanced MRI perfusion parameters with angiogenesis and biologic aggressiveness of rectal cancer: Preliminary results. J Magn Reson Imaging 41:474–480. https://doi.org/10.1002/jmri.24541
Chen YL, Li R, Chen TW, Ou J, Zhang XM, Chen F, Wu L, Jiang Y, Laws M, Shah K, Joseph B, Hu J (2019) Whole-tumour histogram analysis of pharmacokinetic parameters from dynamic contrast-enhanced MRI in resectable oesophageal squamous cell carcinoma can predict T-stage and regional lymph node metastasis. Eur J Radiol 112:112–120. https://doi.org/10.1016/j.ejrad.2019.01.012
Ghosh A, Yekeler E, Dalal D, Holroyd A, States L (2022) Whole-tumour apparent diffusion coefficient (ADC) histogram analysis to identify MYCN-amplification in neuroblastomas: preliminary results. Eur Radiol 32:8453–8462. https://doi.org/10.1007/s00330-022-08750-2
Bozdag M, Er A, Ekmekci S (2021) Association of apparent diffusion coefficient with Ki-67 proliferation index, progesterone-receptor status and various histopathological parameters, and its utility in predicting the high grade in meningiomas. Acta Radiol 62:401–413. https://doi.org/10.1177/0284185120922142
Deng J, Xue C, Liu X, Li S, Zhou J (2023) Differentiating between adult intracranial medulloblastoma and ependymoma using MRI. Clin Radiol 78:e288–e293. https://doi.org/10.1016/j.crad.2022.11.016
Xianwang L, Lei H, Hong L, Juan D, Shenglin L, Caiqiang X, Yan H, Junlin Z (2021) Apparent diffusion coefficient to evaluate adult intracranial ependymomas: Relationship to Ki-67 proliferation index. J Neuroimaging 31:132–136. https://doi.org/10.1111/jon.12789
Wang X, Han F, Lv Y, Gao J, Du Z, Zhang J (2021) Supratentorial extraventricular ependymomas: Imaging features and the added value of apparent diffusion coefficient. J Comput Assist Tomogr 45:463–471. https://doi.org/10.1097/RCT.0000000000001164
Tensaouti F, Ducassou A, Chaltiel L, Sevely A, Bolle S, Muracciole X, Coche-Dequant B, Alapetite C, Supiot S, Huchet A, Bernier V, Claude L, Bertozzi-Salamon AI, Liceaga S, Lotterie JA, Peran P, Payoux P, Laprie A, radiotherapy committee of the French Society for Childhood C (2016) Prognostic and predictive values of diffusion and perfusion MRI in paediatric intracranial ependymomas in a large national study. Br J Radiol 89:20160537. https://doi.org/10.1259/bjr.20160537
Bohara M, Nakajo M, Kamimura K, Yoneyama T, Fukukura Y, Kiyao Y, Yonezawa H, Higa N, Kirishima M, Yoshiura T (2020) Histological grade of Meningioma: Prediction by intravoxel incoherent motion histogram parameters. Acad Radiol 27:342–353. https://doi.org/10.1016/j.acra.2019.04.012
Surov A, Gottschling S, Mawrin C, Prell J, Spielmann RP, Wienke A, Fiedler E (2015) Diffusion-weighted imaging in meningioma: prediction of tumor grade and association with histopathological parameters. Transl Oncol 8:517–523. https://doi.org/10.1016/j.tranon.2015.11.012
Ma X, Ren X, Shen M, Ma F, Chen X, Zhang G, Qiang J (2022) Volumetric ADC histogram analysis for preoperative evaluation of LVSI status in stage I endometrioid adenocarcinoma. Eur Radiol 32:460–469. https://doi.org/10.1007/s00330-021-07996-6
Yang H, Liu X, Jiang J, Zhou J (2022) Apparent diffusion coefficient histogram analysis to preoperative evaluate intracranial solitary fibrous tumor: Relationship to Ki-67 proliferation index. Clin Neurol Neurosurg 220:107364. https://doi.org/10.1016/j.clineuro.2022.107364
Liu X, Deng J, Sun Q, Xue C, Li S, Zhou Q, Huang X, Liu H, Zhou J (2022) Differentiation of intracranial solitary fibrous tumor/hemangiopericytoma from atypical meningioma using apparent diffusion coefficient histogram analysis. Neurosurg Rev 45:2449–2456. https://doi.org/10.1007/s10143-022-01771-x
He W, Xiao X, Li X, Guo Y, Guo L, Liu X, Xu Y, Zhou J, Wu Y (2019) Whole-tumor histogram analysis of apparent diffusion coefficient in differentiating intracranial solitary fibrous tumor/hemangiopericytoma from angiomatous meningioma. Eur J Radiol 112:186–191. https://doi.org/10.1016/j.ejrad.2019.01.023
Ren J, Yuan Y, Tao X (2022) Histogram analysis of diffusion-weighted imaging and dynamic contrast-enhanced MRI for predicting occult lymph node metastasis in early-stage oral tongue squamous cell carcinoma. Eur Radiol 32:2739–2747. https://doi.org/10.1007/s00330-021-08310-0
Xue C, Liu S, Deng J, Liu X, Li S, Zhang P, Zhou J (2022) Apparent diffusion coefficient histogram analysis for the preoperative evaluation of Ki-67 expression in pituitary Macroadenoma. Clin Neuroradiol 32:269–276. https://doi.org/10.1007/s00062-021-01134-x
Funding
This work was supported by the National Natural Science Foundation of China (82071872; 82260341; 82260361; 82371914), the 2021 SKY Imaging Research Fund of China International Medical Exchange Foundation (Z-2014–07-2101), and the Science and Technology Program of Gansu Province (21YF5FA123; 21JR11RA105).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics approval
Ethical approval (2020A-070) was obtained from our institutional ethics review board.
Informed consent
Informed consent Informed consent for this retrospective study was waived by the institutional ethics committee.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Liu, X., Han, T., Wang, Y. et al. Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas. Neuroradiology 66, 531–541 (2024). https://doi.org/10.1007/s00234-024-03319-w
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00234-024-03319-w