Literature DB >> 29392542

Diffusion Profiling via a Histogram Approach Distinguishes Low-grade from High-grade Meningiomas, Can Reflect the Respective Proliferative Potential and Progesterone Receptor Status.

Georg Alexander Gihr1, Diana Horvath-Rizea1, Nikita Garnov2, Patricia Kohlhof-Meinecke3, Oliver Ganslandt4, Hans Henkes1, Hans Jonas Meyer5, Karl-Titus Hoffmann6, Alexey Surov5, Stefan Schob7.   

Abstract

PURPOSE: 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. PROCEDURES: 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.
RESULTS: 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.
CONCLUSIONS: 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.

Entities:  

Keywords:  Diffusion-weighted imaging; Histogram analysis; Histopathology; Imaging biomarker; Meningiomas

Mesh:

Substances:

Year:  2018        PMID: 29392542     DOI: 10.1007/s11307-018-1166-2

Source DB:  PubMed          Journal:  Mol Imaging Biol        ISSN: 1536-1632            Impact factor:   3.488


  35 in total

1.  Morbidity and Mortality of Meningioma Resection Increases in Octogenarians.

Authors:  Jeremy Steinberger; Rachel S Bronheim; Prashant Vempati; Eric K Oermann; Travis R Ladner; Nathan J Lee; Parth Kothari; John M Caridi; Raj K Shrivastava
Journal:  World Neurosurg       Date:  2017-09-12       Impact factor: 2.104

Review 2.  The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

Authors:  David N Louis; Arie Perry; Guido Reifenberger; Andreas von Deimling; Dominique Figarella-Branger; Webster K Cavenee; Hiroko Ohgaki; Otmar D Wiestler; Paul Kleihues; David W Ellison
Journal:  Acta Neuropathol       Date:  2016-05-09       Impact factor: 17.088

3.  Progesterone and estrogen receptors in meningiomas: prognostic considerations.

Authors:  D W Hsu; J T Efird; E T Hedley-Whyte
Journal:  J Neurosurg       Date:  1997-01       Impact factor: 5.115

4.  Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation.

Authors:  V A Nagar; J R Ye; W H Ng; Y H Chan; F Hui; C K Lee; C C T Lim
Journal:  AJNR Am J Neuroradiol       Date:  2008-03-20       Impact factor: 3.825

5.  Characterization of breast masses as benign or malignant at 3.0T MRI with whole-lesion histogram analysis of the apparent diffusion coefficient.

Authors:  Shiteng Suo; Kebei Zhang; Mengqiu Cao; Xinjun Suo; Jia Hua; Xiaochuan Geng; Jie Chen; Zhiguo Zhuang; Xiang Ji; Qing Lu; He Wang; Jianrong Xu
Journal:  J Magn Reson Imaging       Date:  2015-09-07       Impact factor: 4.813

6.  Factors influencing morbidity and mortality after cranial meningioma surgery--a multivariate analysis.

Authors:  J Meixensberger; T Meister; M Janka; B Haubitz; K A Bushe; K Roosen
Journal:  Acta Neurochir Suppl       Date:  1996

7.  Diffusion-Weighted Imaging in Meningioma: Prediction of Tumor Grade and Association with Histopathological Parameters.

Authors:  Alexey Surov; Sebastian Gottschling; Christian Mawrin; Julian Prell; Rolf Peter Spielmann; Andreas Wienke; Eckhard Fiedler
Journal:  Transl Oncol       Date:  2015-12       Impact factor: 4.243

8.  The Potential Value of Preoperative MRI Texture and Shape Analysis in Grading Meningiomas: A Preliminary Investigation.

Authors:  Peng-Fei Yan; Ling Yan; Ting-Ting Hu; Dong-Dong Xiao; Zhen Zhang; Hong-Yang Zhao; Jun Feng
Journal:  Transl Oncol       Date:  2017-06-24       Impact factor: 4.243

Review 9.  EANO guidelines for the diagnosis and treatment of meningiomas.

Authors:  Roland Goldbrunner; Giuseppe Minniti; Matthias Preusser; Michael D Jenkinson; Kita Sallabanda; Emmanuel Houdart; Andreas von Deimling; Pantelis Stavrinou; Florence Lefranc; Morten Lund-Johansen; Elizabeth Cohen-Jonathan Moyal; Dieta Brandsma; Roger Henriksson; Riccardo Soffietti; Michael Weller
Journal:  Lancet Oncol       Date:  2016-08-30       Impact factor: 41.316

10.  Signal Intensities in Preoperative MRI Do Not Reflect Proliferative Activity in Meningioma.

Authors:  Stefan Schob; Clara Frydrychowicz; Matthias Gawlitza; Lionel Bure; Matthias Preuß; Karl-Titus Hoffmann; Alexey Surov
Journal:  Transl Oncol       Date:  2016-07-08       Impact factor: 4.243

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  16 in total

1.  Grading meningiomas utilizing multiparametric MRI with inclusion of susceptibility weighted imaging and quantitative susceptibility mapping.

Authors:  Shun Zhang; Gloria Chia-Yi Chiang; Jacquelyn Marion Knapp; Christina M Zecca; Diana He; Rohan Ramakrishna; Rajiv S Magge; David J Pisapia; Howard Alan Fine; Apostolos John Tsiouris; Yize Zhao; Linda A Heier; Yi Wang; Ilhami Kovanlikaya
Journal:  J Neuroradiol       Date:  2019-05-25       Impact factor: 3.447

2.  Diffusion profiling of tumor volumes using a histogram approach can predict proliferation and further microarchitectural features in medulloblastoma.

Authors:  Stefan Schob; Anne Beeskow; Julia Dieckow; Hans-Jonas Meyer; Matthias Krause; Clara Frydrychowicz; Franz-Wolfgang Hirsch; Alexey Surov
Journal:  Childs Nerv Syst       Date:  2018-05-31       Impact factor: 1.475

3.  Rheologically Essential Surfactant Proteins of the CSF Interacting with Periventricular White Matter Changes in Hydrocephalus Patients - Implications for CSF Dynamics and the Glymphatic System.

Authors:  Alexander Weiß; Matthias Krause; Anika Stockert; Cindy Richter; Joana Puchta; Pervinder Bhogal; Karl-Titus Hoffmann; Alexander Emmer; Ulf Quäschling; Cordula Scherlach; Wolfgang Härtig; Stefan Schob
Journal:  Mol Neurobiol       Date:  2019-05-24       Impact factor: 5.590

4.  Preoperative MR Imaging to Differentiate Chordoid Meningiomas from Other Meningioma Histologic Subtypes.

Authors:  J D Baal; W C Chen; D A Solomon; J S Pai; C-H Lucas; J H Hara; N A Oberheim Bush; M W McDermott; D R Raleigh; J E Villanueva-Meyer
Journal:  AJNR Am J Neuroradiol       Date:  2019-02-28       Impact factor: 3.825

5.  Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients.

Authors:  Mehrsad Mehrnahad; Sara Rostami; Farnaz Kimia; Reza Kord; Morteza Sanei Taheri; Hamidreza Saligheh Rad; Hamidreza Haghighatkhah; Afshin Moradi; Ali Kord
Journal:  Neuroradiol J       Date:  2020-07-06

6.  T1 and ADC histogram parameters may be an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma.

Authors:  Tiexin Cao; Rifeng Jiang; Lingmin Zheng; Rufei Zhang; Xiaodan Chen; Zongmeng Wang; Peirong Jiang; Yilin Chen; Tianjin Zhong; Hu Chen; PuYeh Wu; Yunjing Xue; Lin Lin
Journal:  Eur Radiol       Date:  2022-08-12       Impact factor: 7.034

7.  Differentiation of brain metastases originating from lung and breast cancers using apparent diffusion coefficient histogram analysis and the relation of histogram parameters with Ki-67.

Authors:  Mustafa Bozdağ; Ali Er; Sümeyye Ekmekçi
Journal:  Neuroradiol J       Date:  2021-10-05

8.  The relationship between the apparent diffusion coefficient and the Ki-67 proliferation index in intracranial solitary fibrous tumor/hemangiopericytoma.

Authors:  Shenglin Li; Qing Zhou; Peng Zhang; Shize Ma; Caiqiang Xue; Juan Deng; Xianwang Liu; Junlin Zhou
Journal:  Neurosurg Rev       Date:  2021-11-11       Impact factor: 2.800

9.  Histogram Profiling of Postcontrast T1-Weighted MRI Gives Valuable Insights into Tumor Biology and Enables Prediction of Growth Kinetics and Prognosis in Meningiomas.

Authors:  Georg Alexander Gihr; Diana Horvath-Rizea; Patricia Kohlhof-Meinecke; Oliver Ganslandt; Hans Henkes; Cindy Richter; Karl-Titus Hoffmann; Alexey Surov; Stefan Schob
Journal:  Transl Oncol       Date:  2018-06-18       Impact factor: 4.243

10.  Histogram Analysis Parameters Apparent Diffusion Coefficient for Distinguishing High and Low-Grade Meningiomas: A Multicenter Study.

Authors:  Alexey Surov; Daniel T Ginat; Tchoyoson Lim; Teresa Cabada; Ozdil Baskan; Stefan Schob; Hans Jonas Meyer; Georg Alexander Gihr; Diana Horvath-Rizea; Gordian Hamerla; Karl Titus Hoffmann; Andreas Wienke
Journal:  Transl Oncol       Date:  2018-07-11       Impact factor: 4.243

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