Comparison of quantitative measurements of central nervous system tumour consistency and the associated preoperative imaging findings

Relationship Between Quantitative Tumor Consistency and Pathological Factors in Intracranial Meningioma

BACKGROUND

The consistency of intracranial meningiomas is an important clinical factor because it affects the success of surgical resection. This study aimed at identifying and quantitatively measuring pathological factors that contribute to the consistency of meningiomas. Furthermore, we investigated the relationship between these factors and preoperative neuroradiological imaging.

METHODS

We analyzed 42 intracranial meningioma specimens, which had been removed at our institution between October 2012 and March 2018. Consistency was measured quantitatively after resection using an industrial stiffness meter. For pathological evaluation, we quantitatively measured the collagen-fiber content through binarization of images of Azan-Mallory-stained section. We assessed calcification and necrosis semi-quantitatively using images acquired of Hematoxylin and Eosin stained samples. The relationship between collagen-fiber content rate and imaging findings was examined.

RESULTS

The content of collagen fibers significantly positively correlated with meningioma consistency (p < 0.0001). Collagen-fiber content was significantly higher in low- and iso-intensity regions compared with high-intensity regions on the magnetic resonance T2-weighted images (p = 0.0148 and p = 0.0394, respectively). Calcification and necrosis showed no correlation with tumor consistency.

CONCLUSIONS

The quantitative hardness of intracranial meningiomas positively correlated with collagen-fiber content; thus, the amount of collagen fibers may be a factor that determines the hardness of intracranial meningiomas. Our results demonstrate that T2-weighted images reflect the collagen-fiber content and are useful for estimating tumor consistency preoperatively and non-invasively.

Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers

BACKGROUND

The consistency of meningioma is a factor that may influence surgical planning and the extent of resection. The aim of our study is to develop a predictive model of tumor consistency using the radiomic features of preoperative magnetic resonance imaging and the tumor elasticity measured by intraoperative ultrasound elastography (IOUS-E) as a reference parameter.

METHODS

A retrospective analysis was performed on supratentorial meningiomas that were operated on between March 2018 and July 2020. Cases with IOUS-E studies were included. A semiquantitative analysis of elastograms was used to define the meningioma consistency. MRIs were preprocessed before extracting radiomic features. Predictive models were built using a combination of feature selection filters and machine learning algorithms: logistic regression, Naive Bayes, k-nearest neighbors, Random Forest, Support Vector Machine, and Neural Network. A stratified 5-fold cross-validation was performed. Then, models were evaluated using the area under the curve and classification accuracy.

RESULTS

Eighteen patients were available for analysis. Meningiomas were classified as hard or soft according to a mean tissue elasticity threshold of 120. The best-ranked radiomic features were obtained from T1-weighted post-contrast, apparent diffusion coefficient map, and T2-weighted images. The combination of Information Gain and ReliefF filters with the Naive Bayes algorithm resulted in an area under the curve of 0.961 and classification accuracy of 94%.

CONCLUSIONS

We have developed a high-precision classification model that is capable of predicting consistency of meningiomas based on the radiomic features in preoperative magnetic resonance imaging (T2-weighted, T1-weighted post-contrast, and apparent diffusion coefficient map).