Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Preoperative differentiation between angiomatous meningioma (AM) and atypical meningioma (ATM) is related to treatment planning. In this study, we explored the utility of apparent diffusion coefficient (ADC) histogram analysis in differentiating AM and ATM, and further assess the correlations between these parameters and the Ki-67 proliferation index.

Methods

Thirty AM and 35 ATM patients were enrolled and their clinical and conventional magnetic resonance imaging (MRI) features were analyzed in this study. Nine ADC histogram parameters [mean, variance, skewness, and kurtosis, as well as the 1st (ADC1), 10th (ADC10), 50th (ADC50), 90th (ADC90), and 99th (ADC99) percentile of ADC] were selected and compared by independent t-test or Mann-Whitney U test. Diagnostic performance analysis was performed by receiver operating characteristic (ROC) curves. The relationship between ADC histogram parameters and the Ki-67 proliferation index was assessed by Spearman’s correlation coefficient.

Results

AM group showed a significantly higher mean [median (interquartile range): 124.07 (22.66) vs. 112.12 (16.04), P<0.001], ADC1 [107.50 (17.00) vs. 82.00 (20.33), P<0.001], ADC10 (mean ± standard deviation: 115.80±12.09 vs. 96.86±9.86, P<0.001), and ADC50 [124.00 (21.13) vs. 109.00 (15.17), P<0.001], compared to the ATM group. Significant correlations were identified between the mean (r=−0.428, P<0.001), ADC1 (r=−0.549, P<0.001), ADC10 (r=−0.529, P<0.001), ADC50 (r=−0.483, P<0.001), and the Ki-67 proliferation index. ROC analysis showed that the best diagnostic performance was achieved by ADC1 (AUC =0.900). Whereas, no differences were found between variance, skewness, kurtosis, ADC90, and ADC99 (P=0.067–0.787).

Conclusions

AM and ATM exhibit overlapping conventional MRI features. ADC histogram analysis, especially ADC1, maybe a reliable quantitative imaging biomarker for differentiation between AM and ATM.

Related collections

Most cited references 33

Record: found
Abstract: found
Article: not found

The 2021 WHO Classification of Tumors of the Central Nervous System: a summary

David N Louis, Arie Perry, Pieter Wesseling … (2021)

The fifth edition of the WHO Classification of Tumors of the Central Nervous System (CNS), published in 2021, is the sixth version of the international standard for the classification of brain and spinal cord tumors. Building on the 2016 updated fourth edition and the work of the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy, the 2021 fifth edition introduces major changes that advance the role of molecular diagnostics in CNS tumor classification. At the same time, it remains wedded to other established approaches to tumor diagnosis such as histology and immunohistochemistry. In doing so, the fifth edition establishes some different approaches to both CNS tumor nomenclature and grading and it emphasizes the importance of integrated diagnoses and layered reports. New tumor types and subtypes are introduced, some based on novel diagnostic technologies such as DNA methylome profiling. The present review summarizes the major general changes in the 2021 fifth edition classification and the specific changes in each taxonomic category. It is hoped that this summary provides an overview to facilitate more in-depth exploration of the entire fifth edition of the WHO Classification of Tumors of the Central Nervous System.

0 comments Cited 2589 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

An overview of meningiomas

Robin A Buerki, Craig M. Horbinski, Timothy Kruser … (2018)

Meningiomas are the most common primary intracranial tumor. Important advances are occurring in meningioma research. These are expected to accelerate, potentially leading to impactful changes on the management of meningiomas in the near and medium term. This review will cover the histo- and molecular pathology of meningiomas, including recent 2016 updates to the WHO classification of CNS tumors. We will discuss clinical and radiographic presentation and therapeutic management. Surgery and radiotherapy, the two longstanding primary therapeutic modalities, will be discussed at length. In addition, data from prior and ongoing investigations of other treatment modalities, including systemic and targeted therapies, will be covered. This review will quickly update the reader on the contemporary management and future directions in meningiomas.

0 comments Cited 149 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas.

Y. Kim, H M Baek, S. H. Kim … (2012)

To test the predictive value of skewness and kurtosis changes of normalized cerebral blood volume (nCBV) during the early treatment period for differentiating early tumor progression from pseudoprogression in patients with newly diagnosed glioblastomas. The institutional review board approved this retrospective study. The authors assessed 135 patients with newly diagnosed glioblastomas who underwent concurrent chemotherapy and radiation therapy (CCRT) after surgical resection. Patients who developed new or enlarged contrast material-enhanced lesions after CCRT were assessed by means of conventional and perfusion magnetic resonance (MR) imaging. The percent change of skewness and kurtosis on nCBV histograms between the first and second post-CCRT follow-up were classified into four categories. Independent predictors of early tumor progression were determined by means of logistic regression analysis. Of 135 patients, 79 had new or enlarged contrast-enhanced lesions after CCRT, subsequently classified as early tumor progression (n = 42, 53.2%) and pseudoprogression (n = 37, 46.8%). Pseudoprogression was observed in 23 of 24 (95.8%) patients in category 1, 10 of 15 (66.7%) in category 2, four of 20 (20.0%) in category 3, and 0 of 20 (0%) in category 4 (χ(2) test, P < .0001). The histographic pattern of nCBV was the best independent predictor (odds ratio, 3.51; P = .0032) for early tumor progression, rather than each percent change of skewness or kurtosis; the histographic pattern of nCBV represented the largest area under the receiver operating characteristic curve (0.934; 95% confidence interval: 0.855, 0.977), with a sensitivity of 85.7% and a specificity of 89.2%. The percent change of skewness and kurtosis of nCBV may be a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. © RSNA, 2012

0 comments Cited 56 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Journal

Journal ID (nlm-ta): Quant Imaging Med Surg

Journal ID (iso-abbrev): Quant Imaging Med Surg

Journal ID (publisher-id): QIMS

Title: Quantitative Imaging in Medicine and Surgery

Publisher: AME Publishing Company

ISSN (Print): 2223-4292

ISSN (Electronic): 2223-4306

Publication date (Electronic): 08 May 2023

Publication date (Print): 01 July 2023

Volume: 13

Issue: 7

Pages: 4160-4170

Affiliations

[1 ]deptDepartment of Radiology , Lanzhou University Second Hospital , Lanzhou, China;

[2 ]deptSecond Clinical School , Lanzhou University , Lanzhou, China;

[3 ]Key Laboratory of Medical Imaging of Gansu Province , Lanzhou, China;

[4 ]Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence , Lanzhou, China

Author notes

Contributions: (I) Conception and design: X Liu, T Han, Y Wang; (II) Administrative support: J Zhou; (III) Provision of study materials or patients: H Liu, J Zhou; (IV) Collection and assembly of data: T Han, Y Wang; (V) Data analysis and interpretation: X Liu, T Han, X Huang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

[#]

These authors contributed equally to this work.

Correspondence to: Junlin Zhou, MD, PhD. Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China. Email: lzuzhoujl601@ 123456163.com .

[^]

ORCID: 0000-0002-2336-2480.

Article

Publisher ID: qims-13-07-4160

DOI: 10.21037/qims-22-1224

PMC ID: 10347304

SO-VID: a6ba1fef-2938-49eb-ab0b-f7f805555404

License:

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0.

Differentiating angiomatous meningioma from atypical meningioma using histogram analysis of apparent diffusion coefficient maps

Read this article at

Abstract

Background

Methods

Results

Conclusions

Related collections

Genome Engineering using CRISPR

Most cited references 33

The 2021 WHO Classification of Tumors of the Central Nervous System: a summary

An overview of meningiomas

Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas.

Author and article information

Journal

Affiliations

Author notes

Article

History

Categories

Comments

Comment on this article

Similar content 240

Most referenced authors 365