Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status
- 170 Downloads
Presurgical grading, estimation of growth kinetics, and other prognostic factors are becoming increasingly important for selecting the best therapeutic approach for meningioma patients. Diffusion-weighted imaging (DWI) provides microstructural information and reflects tumor biology. A novel DWI approach, histogram profiling of apparent diffusion coefficient (ADC) volumes, provides more distinct information than conventional DWI. Therefore, our study investigated whether ADC histogram profiling distinguishes low-grade from high-grade lesions and reflects Ki-67 expression and progesterone receptor status.
Pretreatment ADC volumes of 37 meningioma patients (28 low-grade, 9 high-grade) were used for histogram profiling. WHO grade, Ki-67 expression, and progesterone receptor status were evaluated. Comparative and correlative statistics investigating the association between histogram profiling and neuropathology were performed.
The entire ADC profile (p10, p25, p75, p90, mean, median) was significantly lower in high-grade versus low-grade meningiomas. The lower percentiles, mean, and modus showed significant correlations with Ki-67 expression. Skewness and entropy of the ADC volumes were significantly associated with progesterone receptor status and Ki-67 expression. ROC analysis revealed entropy to be the most accurate parameter distinguishing low-grade from high-grade meningiomas.
ADC histogram profiling provides a distinct set of parameters, which help differentiate low-grade versus high-grade meningiomas. Also, histogram metrics correlate significantly with histological surrogates of the respective proliferative potential. More specifically, entropy revealed to be the most promising imaging biomarker for presurgical grading. Both, entropy and skewness were significantly associated with progesterone receptor status and Ki-67 expression and therefore should be investigated further as predictors for prognostically relevant tumor biological features. Since absolute ADC values vary between MRI scanners of different vendors and field strengths, their use is more limited in the presurgical setting.
Key WordsMeningiomas Diffusion-weighted imaging Histogram analysis Histopathology Imaging biomarker
Compliance with Ethical Standards
The study was approved by the ethics committee of the medical council of Baden-Württemberg (Ethik-Kommission Landesärztekammer Baden-Württemberg, F-2017-047).
Conflict of Interest
The authors declare that they have no conflict of interest.
This study acknowledges funding via the Clinician-Scientist-Program of the medical faculty of the University Hospital Leipzig.
- 1.Ostrom QT, Gittleman H, Xu J, Kromer C, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS (2016) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2009-2013. Neuro-Oncology 18(suppl_5):v1–v75. https://doi.org/10.1093/neuonc/now207 CrossRefPubMedGoogle Scholar
- 5.Claus EB, Bondy ML, Schildkraut JM, Wiemels JL, Wrensch M, Black PM (2005) Epidemiology of intracranial meningioma. Neurosurgery 57(6):1088–1095. https://doi.org/10.1227/01.NEU.0000188281.91351.B9 CrossRefPubMedGoogle Scholar
- 6.Kshettry VR, Ostrom QT, Kruchko C, al –Mefty O, Barnett GH, Barnholtz–Sloan JS (2015) Descriptive epidemiology of World Health Organization grades II and III intracranial meningiomas in the United States. Neuro-Oncology 17(8):1166–1173. https://doi.org/10.1093/neuonc/nov069 CrossRefPubMedPubMedCentralGoogle Scholar
- 10.Meixensberger J, Meister T, Janka M et al (1996) Factors influencing morbidity and mortality after cranial meningioma surgery—a multivariate analysis. In: Modern Neurosurgery of Meningiomas and Pituitary Adenomas. Springer Vienna, Vienna, pp 99–101. https://doi.org/10.1007/978-3-7091-9450-8_27 CrossRefGoogle Scholar
- 17.Yan P-F, Yan L, Hu T-T et al (2017) The potential value of preoperative MRI texture and shape analysis in grading meningiomas: a preliminary investigation. TRANON 10:570–577Google Scholar
- 22.Surov A, Gottschling S, Mawrin C et al (2015) Diffusion-weighted imaging in meningioma: prediction of tumor grade and association with histopathological parameters. TRANON 8:517–523Google Scholar
- 32.Foroutan P, Kreahling JM, Morse DL, Grove O, Lloyd MC, Reed D, Raghavan M, Altiok S, Martinez GV, Gillies RJ (2013) Diffusion MRI and novel texture analysis in osteosarcoma xenotransplants predicts response to anti-checkpoint therapy. PLoS One 8(12):e82875. https://doi.org/10.1371/journal.pone.0082875 CrossRefPubMedPubMedCentralGoogle Scholar