Advantages and limitations of intraoperative ultrasound strain elastography applied in brain tumor surgery: a single-center experience

doi:10.21203/rs.3.rs-654010/v1

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Advantages and limitations of intraoperative ultrasound strain elastography applied in brain tumor surgery: a single-center experience

https://doi.org/10.21203/rs.3.rs-654010/v1

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published 08 Mar, 2022

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Objective Strain elastography is an intraoperative ultrasound (ioUS) modality currently under development with various potential applications in neurosurgery. However, certain technical aspects and limitations have not yet been adequately explained. The objective of our work is to share the experience of our center in a case series of operated brain tumors in which we have applied strain elastography.

Methods We retrospectively analyzed patients who underwent craniotomy for a brain tumor between March 2018 to March 2021. Cases with an ioUS strain elastography study were included. The elastograms were processed semi-quantitatively by decomposing the image in HSB (Hue, Saturation, Brightness) format. Subsequently, the mean tissue elasticity (MTE) values were calculated from the histogram of intensities of the pixels of the hue images. The tumor was manually segmented, and regions of interest (ROIs) were placed in the peritumoral area. An analysis was performed to correlate the histopathological groups and the tumor and peritumoral MTE values using the Kruskal-Wallis test. In addition, a classification model was developed using an algorithm based on a decision tree. Then, model predictive capacity was evaluated through 10-folds cross-validation. Finally, elastogram’s quality was assessed to discuss possible sources of artifacts and weaknesses of the ultrasound technique.

Results One hundred two patients with the following histopathological diagnosis were analyzed: 43 high-grade gliomas, 11 low-grade gliomas, 28 meningiomas, and 20 metastases. The tumor MTE values were significantly different between the histopathological groups, p <.001, c²= 46.34, e²= .45. There were also significant differences for the MTE values concerning the peritumor, p<.001, c² = 25.47, e²= .25. The decision tree classifier showed an area under the curve (AUC) of the average over classes of 0.97, and the classification accuracy (CA) was 86%. After 10-folds cross-validation, the AUC was 0.73, and the CA was 72%. The main technical limitations found in our series were: the presence of artifacts after dural opening, the variability of the frequency and amplitude of the mechanical pulsations, and the challenge in evaluating deep lesions.

Conclusion ioUS strain elastography is a fast and versatile technique that provides relevant information to adapt the surgical strategy. Furthermore, the stiffness of the tissues has a plausible histopathological correlation. For these reasons, this technique has enormous potential to be exploited in the coming years.

Oncology

Neurosurgery

brain tumor

ultrasound

elastography

Intraoperative ultrasound (ioUS) has recently proven to be a valuable tool within the neurosurgical armamentarium.[22, 23, 30] Its versatility and low cost makes it an optimal intraoperative imaging technique that can be used in almost any center.

In recent years, improvements in image quality, the incorporation of new ultrasound modalities and the possibility of integrating magnetic resonance imaging (MRI) through navigated ultrasound have fostered a resurgence of this technique, gaining followers in the last years.[4, 34, 40] ioUS competes directly with other intraoperative imaging techniques such as intraoperative MRI which has shown significant benefits in brain tumor surgery.[17, 42] However, due to its high economic and infrastructure costs, it cannot be implemented in most centers in middle and low-income countries.

Even though ultrasound is an operator-dependent technique, this characteristic that might be considered a weakness, is really a strength since, in the hands of experienced surgeons, it becomes a great help thanks to its dynamic nature. Its speed of acquisition and its ability to provide information with visual-spatial characteristics that other imaging techniques cannot offer.

Other ultrasound modalities such as contrast-enhanced ultrasound (CEUS), and elastography give a bonus to this imaging technique, leaving its potential still to be fully exploited.[20, 26, 29]

Tissue stiffness assessed by palpation for diagnosis of neoplasms has been used for several years. Ultrasound elastography has been recently developed to display information about tissue stiffness through images.[2, 24, 35] Its use is widespread in other surgical specialties, and shows an incremental application in neurosurgery.[11, 31, 32, 39, 41] In brain tumors, elasticity/stiffness contributes to surgical planning, but this physical property may also be related to the histology and aggressiveness of the tumors.[7, 13] If we add advanced image processing techniques and algorithms based on artificial intelligence, the capacity of this modality opens a new line of research yet to be developed.

The present study aims to share our experience in the performance, interpretation and analysis of ioUS strain elastography in brain tumors, in recent years. Furthermore, we wish to highlight the most relevant technical aspects, the technique's main limitations, and future perspectives. Thus, those who intend to start their application can find solid support elements that facilitate the use and expansion of this promising imaging technique.

Patient selection

A retrospective analysis was carried out of patients diagnosed with brain tumors consecutively operated in our center between March 2018 and March 2021. Patients with an ioUS study, including the strain elastography modality were selected. Stereotactic biopsies and those cases in which, due to artifacts or failures during their acquisition, the ioUS image quality was inadequate to ensure its processing were discarded. In addition, conventional clinical, demographic, and radiological variables were recorded. In all cases, the patient's written consent was obtained, in addition to the approval of the ethics committee of our center and following the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.

Intraoperative ultrasound image acquisition technique

All cases underwent craniotomy, ensuring that its dimensions were sufficient for the placement and maneuverability of the ultrasound probe. In all cases, we used microsurgical technique, neuronavigation and ultrasonic aspirator. Fluorescence with 5 - aminolevulinic acid was used when a high-grade tumor was suspected. Intraoperative neurophysiological monitoring and cortical-subcortical mapping in awake patients were implemented according to the tumor location.

The first ultrasound image acquisition was performed after craniotomy and before dural opening. Our ultrasound equipment was a Hitachi Noblus model with a C42 micro convex probe at a frequency range of 4–8 MHz, scan width of 20 mm radius and 80 º field of view scan angle. The probe was protected with double sterile sheets, and a minimum amount of conductive gel was used inside the sheet.

The probe was placed on the dural surface, first acquiring B-mode images to visualize the main features of the tumor and their relationships with the surrounding structures. So its morphology, limits, presence of cystic areas, calcifications, etc., were assessed.

Afterward, the strain elastography was performed. The probe was kept in a perpendicular position to the dura, in orthogonal planes in which the largest tumor diameter could be seen. Since biological tissue deforms in a non-linear manner, pre-compression should be minimal with the probe held lightly in contact with the dura.[3] Strain images are produced by using the probe to apply constant gentle pressure toward the brain. The consequent tissue displacement is tracked between pairs of echo frames and strain computed from the axial gradient of the displacements. Under an equal quantity of stress, a stiff region encounters less strain (deformation) than surrounding softer tissue. As a guide for constant pulses, the ultrasound scanner gives a scale indicating the optimal stress, providing real-time feedback to the examiner on the degree and uniformity of the compression technique. The most valuable frames for evaluating strain images with an excellent signal-noise ratio are those with a steady rate of displacement, that is, during the time of downward or upward movement of the transducer.[16] The ultrasound screen simultaneously displays the B-mode image and a color map translucently superimposed on the conventional B-mode image, called elastogram. The color scale ranges from red (soft) to blue (hard). A regular color pattern obtained in several consecutive frames indicates a reliable technique. Quantitative elasticity measurements cannot be achieved, as the local amount of stress is unknown. Thus, strain elastography is a qualitative method in which relative stiffness differences are displayed. During surgery and at the end of it, B-mode images are acquired exclusively to control the resection. All images were stored in DICOM (Digital Imaging and Communications in Medicine) format for further processing.

Elastogram processing

Because the information from the strain images is purely qualitative, semi-quantitative analysis was carried out to transform the colorimetric scale into a dimensionless scale of values called mean tissue elasticity (MTE). The MTE is calculated from the histogram of intensities of the pixels and expressed in arbitrary units.[18, 25] To perform this analysis, the elastograms were converted to HSB (hue-saturation-brightness) format using the ImageJ software version 1.8.0 (National Institutes of Health, Bethesda, Maryland, USA). The new scale uses the hue images with values ranging from 0 blue-hard to 256 red-soft.

All elastograms with abundant artifacts and void areas were discarded, and three images were selected for each patient on which tumor segmentation and elasticity measurements were performed.

Always taking B-mode as a reference, the tumor was manually segmented using the free-hand tool of ImageJ to create the regions of interest (ROIs). Three circular ROIs with a diameter of 20 pixels were also created for the peritumor, placed in the areas of alteration of echogenicity surrounding the tumor area. Placement was relatively straightforward for meningiomas, metastases, and high-grade gliomas, all of which have a well-defined tumor border. In the cases of low-grade tumors, as there was no clear border between tumor and peritumor, the border areas between echogenicity alterations and the adjacent healthy parenchyma were considered as peritumor. Figure 1.

Finally, the MTE values of the tumor and peritumor are obtained by calculating an average between the three elastograms used for each patient.

Statistical analysis

The distribution of the quantitative variables was evaluated using the Kolmogorov-Smirnov test. The comparison between the histopathological groups and their MTE values in the tumor and peritumoral region was carried out using the Kruskal-Wallis test, applying the epsilon squared coefficient to measure the effect size. Finally, posthoc comparisons were made using Dunn's test with Holm's correction.

A classification model was also developed using three classes or categories: glioma, meningioma, and metastases. In addition, the MTE values of the tumor and peritumoral region were included in the model. A decision tree was used as a classification algorithm. Decision trees are nonparametric methods that do not require assumptions about the distribution of variables within each class. For the first division of the sample into two parts, the feature with the most significant relevance is used, then each node contains a decision criterion based on a single feature. This procedure is repeated until no more divisions are possible. The advantages of decision trees are their simple structure, ease of interpretation and visualization. The classification algorithm is applied to the total sample and then validated through a 10-fold cross-validation process. Basic statistics were carried out with R version 4.0.5 (R Foundation for Statistical Computing, Vienna, Austria), and the decision tree was elaborated with Orange version 3.28.0 (University of Ljubljana, Slovenia).[15]

During the study period, one hundred fifty patients with a diagnosis of brain tumor were operated. Of these, forty patients were discarded because they did not have an intraoperative ultrasound study. Furthermore, eight patients were not included because they had poor quality intraoperative ultrasound images, making their interpretation and analysis impossible.

Thus, 102 patients met the selection criteria. Histopathological diagnoses were: 43 high-grade gliomas, 11 low grade-gliomas, 28 meningiomas and 20 metastases. The characteristics of each group and other clinical and demographic variables are shown in Table 1.

There were no complications related to the intraoperative imaging technique, and the postoperative infection rate was 4.9 %.

The tumor MTE values were significantly different between histopathological groups, p < .001, χ² = 46.34, ε² = .45. Thus, high-grade gliomas were characterized by showing a softer pattern with MTE values of 79.3 (19.3), followed by low-grade gliomas with 88.7 (13.7) and metastases with 114 (29.2). Finally, meningiomas exhibited a significantly higher stiffness than the above tumor mentioned groups, with MTE values of 120 (30.9).

There were also significant differences for the MTE values regarding the peritumor, p < .001, χ² = 25.47, ε² = .25. Thus, in high-grade gliomas [93.2 (25.5)] and low-grade gliomas [91.6 (19.6)], the peritumoral region shows lower stiffness values compared to meningiomas [126 (31.9)] and metastases [122 (37.3)]. Figure 2 and Table 2.

Among the histological types of gliomas, tumor MTE values had the following distribution: astrocytoma grade II = 94.2 (20.8), anaplastic astrocytoma grade III = 73.7 (6.5), oligodendroglioma grade II = 80.2 (3.3), anaplastic oligodendroglioma grade III = 73.7 (6.5) and glioblastoma = 84 (18.4). On the other hand, the following peritumoral MTE values were obtained: astrocytoma grade II = 94.8 (11.1), anaplastic astrocytoma grade III = 101 (25.9), oligodendroglioma grade II = 83.7 (17.6), anaplastic oligodendroglioma grade III = 121 (16.70) and glioblastoma = 89.3 (18.5). No significant differences were found between these groups.

The classifier decision tree was applied with a pruning technique of at least two instances in leaves and at least five instances in each node with a maximum depth of 5. It is a binary tree with stop splitting when reaching 95% of the instances. In the total sample, the area under the curve (AUC) of the average over classes was 0.97, and the classification accuracy (CA) was 86%. After 10-folds cross-validation, the AUC of the average over classes was 0.73 and CA of 72%. A summary of the results is shown in Table 3, Fig. 3 and Supplemental Figs. 1 and 2.

In the present study, we demonstrate that ioUS strain elastography is a safe and easy-to-apply technique. Furthermore, through the semi-quantitative analysis of the elastograms, it is possible to characterize the different tumor types based on the calculated values of MTE.

Among the strengths of our study, we can mention that this is the largest published series about the application of ioUS strain elastography in brain tumor surgery. In addition, we confirm the findings of an initial study of our group in which we applied and validated the semi-quantitative analysis technique of the colorimetric scale of the elastograms to obtain a measure of the stiffness of the tissues.[7] As far as we know, we are pioneers in applying this type of ultrasound image analysis in brain pathology.

Among the limitations of our work, we can mention the lack of external validation of the elastogram acquisition technique. Furthermore, in our series of cases, the images were acquired by a single surgeon (S.C.). Therefore, in future work, an evaluation of the reproducibility of the acquisition of images must be carried out. Despite this, we offer a detailed description of the ultrasound technique to be replicated in future studies.

Elastogram quality

The image quality of ultrasound images and elastograms is susceptible to multiple factors.[1, 34, 36] The ultrasound equipment used in our series requires gentle mechanical compressions to acquire strain images. However, other ultrasound scanners are capable of generating elastograms using spontaneous brain pulsations.[27, 28] To obtain high-quality images, the elastograms are acquired before the dural opening. The dura mater offers resistance, and thanks to its elastic capacity, it is possible to perform more homogeneous pulsations, which translates into more uniform elastograms and fewer signal voids. Figure 4. In those cases in which small dural tears occurred, the quality of the elastogram did not vary significantly, at least in a qualitative way. Figure 5A-B. These variations should be studied in the future.

Regarding the ROI size, strain elastography demonstrates the relative stiffness of tissue, so it is crucial to include enough normal tissue surrounding the tumor. The most suitable image quality was registered in phantom experiments when the lesion of interest covered 25–50 % of the ROI.[19] Caution must be taken when using a convex probe since the region immediately in the middle of the transducer could apply more stress than the lateral portions of the probe, producing a “lateral stiffness artifact”.[5]

Another factor that influences the quality of the image is the presence of cystic areas. Figure 5C. The interposition of liquid content can seriously influence the transmission of mechanical pulsations. In these cases, the penetration of mechanical waves does not occur homogeneously throughout the tissue to be explored. Measuring displacement between two frames solely detects random noise, which is displayed in different ways. A commonly described artifact is the BGR (blue-green-red) sign encountered in small cystic tumors. Large cystic lesions are more likely to be seen as "black holes".[14] This circumstance occurs especially in metastases and less frequently in high-grade gliomas, possibly due to their content (cystic-mucinous or necrotic).

Elastograms can also vary depending on the tumor's location and, therefore, on how the mechanical impulse spreads. For example, in deep-seated tumors, the distance from the probe to the tumor can produce artifactual images and overestimate its stiffness. Figure 5D. In addition, in those cases of tumors of the skull base or posterior fossa, the mechanical impulse can make the tissue collide against bone structures, altering its compressibility and, consequently, the elastographic image. Also, it is recommendable to avoid assessment of tissue near stiff areas, as soft tissue will undergo more strain when it is above hard tissue.

Meningiomas

In meningioma surgery, strain elastography permits addressing the resection of the tumor with reliable information about its stiffness. The presence of a cleavage plane and the relationships with surrounding neurovascular structures allow the surgeon to adapt the surgical technique and anticipate the extent of the resection. This utility has been demonstrated in previous works.[6, 21, 27] In addition, they are a tumor group in which the elastograms are usually relatively homogeneous because they generally behave as compact masses.

Gliomas

The elastographic pattern of gliomas is very characteristic from a qualitative and quantitative point of view. They are usually tumors softer than the surrounding brain parenchyma, with extensive involvement of the peritumoral white matter, which also shows less stiffness. Peritumoral infiltration may explain a change in elasticity in these regions, as has been postulated.[10, 31] On the other hand, the contrast of the color image is superior to the B-mode when establishing the tumor's edges, as reported by Selbekk et al. in his work.[33]

Metastases

Metastases also tend to be softer than the normal parenchyma. However, the stiffness of the white matter does not appear to be significantly different compared to that found in primary tumors. Possibly, the presence of pure vasogenic edema present in metastases and meningiomas could explain this difference in peritumoral elasticity. For this, it is essential in future studies to carry out a histopathological correlation of these areas.

Histological classification based on elastography and glioma grading

Our results show significant differences in tumor and peritumoral stiffness expressed through MTE between gliomas and the rest of the tumor groups (metastases and meningiomas). These results are validated by applying the classification algorithm based on a decision tree, which reaches a precision of 70%. However, even though metastases showed less tumor stiffness than meningiomas (113 vs. 120), these differences were not statistically significant. Furthermore, their peritumoral MTE values were very similar (122 vs. 126).

Regarding glioma grading by ioUS elastography, we found previous descriptions in the literature in which a lower elasticity was observed in high-grade gliomas than low-grade gliomas.[7, 13, 28, 43] However, in our study, although tumor and peritumoral MTE values are indeed lower in high-grade glioma than low-grade glioma (80 vs. 86, 91 vs. 96, respectively), these differences were not statistically significant. Neither, we found differences after analyzing the histological subtypes and degrees of aggressiveness in the glioma group.

Strain elastography versus shear wave elastography

There are two types of ultrasound elastography, strain and shear wave. Strain elastography is a qualitative technique and provides knowledge on the relative stiffness between one tissue and another. Shear wave elastography (SWE) is a quantitative method that offers an estimated value of the tissue stiffness that can be expressed in either the shear wave speed through the tissues in meters per second or converted to Young’s modulus and expressed in kilopascals.[16] A critical advantage of SWE is to perform acquisitions to assess the extent of the resection. Using strain elastography, it is impossible to perform compressions on the deformed parenchyma and the interposition of fluid that fills the surgical cavity. In this sense, the SWE stands as the best option to assess residual tumor.[12]

The main limitation of the SWE is the size of the Q-box, which is the region of interest in which the color maps and elasticity values are obtained. Unfortunately, this region is usually limited in size, and the vast majority of cases do not cover the entire tumor and peritumor area. In this regard, strain elastography has an advantage by offering a more extensive and visually richer image compared to SWE. In addition, strain elastography allows an overview of relative tissue stiffness in a large field of view. This aspect is advantageous if we want to assess, for example, the stiffness of a tumor area close to a noble structure and to know globally if its stiffness has a homogeneous or heterogeneous pattern.

Each technique has its advantages and disadvantages, and maybe they should be used in a complementary way in brain tumor surgery.

Brain elastography and future perspectives

The usefulness of intraoperative cerebral elastography goes beyond the characterization of the histopathological types and serves as a support for surgical resection. Knowledge of the elasticity and stiffness of tissues is biologically based and is related to the cytoarchitecture of tumors and their ability to infiltrate in gliomas.[37, 38]

Advanced image processing techniques, such as texture analysis, also known as radiomics, can increase the diagnostic potential of this imaging technique and even contribute to predict the survival and progression of primary brain neoplasms.[9]

On the other hand, image processing automation and the combination of recognition techniques based on artificial intelligence are yet to be explored in depth.[8]

Our experience on brain tumor’s ioUS transmitted through this series of cases allows us to confirm the applicability of strain elastography in brain tumor surgery. In addition, this technique provides relevant information for surgical planning and differentiation of the different tumor types. Its potential is still under development, but it is already emerging as a valuable neurosurgical tool for the upcoming years.

Disclosures:

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Conflicts of Interest:

The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Funding:

No funding was received for this research.

Acknowledgments:

We thank Santiago Aja-Fernández, Professor at the ETSI (Higher Technical School of Telecommunications Engineering) and (LPI) Image Processing Laboratory, at the University of Valladolid, for his technical assistance in planning our study.

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Table 1. Patient characteristics
Variable	n
Age	60.76 ± 11.61
Sex Female Male	45 (44 %) 57 (66 %)
Histopathology Meningiomas High-grade gliomas Glioblastoma Anaplastic astrocytoma grade III Anaplastic oligodendroglioma grade III Low-grade gliomas Astrocytoma grade II Oligodendroglioma grade II Metastases Lung Breast Ovarian Gastrointestinal Prostate Urothelial Thyroid	28 (27.4 %) 43 (41.2 %) 34 7 2 11 (11.8 %) 6 5 20 (19.6 %) 7 2 4 3 2 1 1
Tumor location Frontal Temporal Parietal Occipital Posterior fossa / cerebellum	47 (46.1 %) 22 (21.5 %) 21 (20.6 %) 5 (4.9 %) 7 (6.9 %)
Values are expressed as the mean ± standard deviation or as frequency (%).

Table 2. Analysis of MTE values of tumor types in their different regions
REGION	Descriptive statistics				Kruskal-Wallis test					Post hoc Dunn test
REGION	PATHOLOGY	n	MEDIAN	IQR	c²	df	p	e²	95% CI	COMPARISON	p (Holm)
TUMOR (MTE)					46.34	3	<.001	.46	.31 - .60	HGG-LGG	.366
	HIGH-GRADE GLIOMA	43	79.3	19.3						HGG-MENINGIOMA	<.001
	LOW-GRADE GLIOMA	11	88.7	13.7						LGG-MENINGIOMA	.009
	MENINGIOMA	28	120	30.9						HGG-METS	<.001
	METASTASES	20	114	29.2						LGG-METS	.130
										MENINGIOMA-METS	.267
PERITUMORAL REGION (MTE)					25.47	3	<.001	.25	.12 - .44	HGG-LGG	.86
	HIGH-GRADE GLIOMA	43	93.2	25.5						HGG-MENINGIOMA	<.001
	LOW-GRADE GLIOMA	11	91.9	19.6						LGG-MENINGIOMA	.011
	MENINGIOMA	28	126	31.9						HGG-METS	.002
	METASTASES	20	122	37.2						LGG-METS	.019
										MENINGIOMA-METS	.905
IQR = interquartile range, df = degrees of freedom, METS = metastases, MTE = mean tissue elasticity, HGG = high-grade glioma, LGG = low-grade glioma.

Table 3. Performance evaluation of the classification model by decision tree algorithm
Model	AUC	CA	Precision	Recall
*Training*
Average over classes	0.968	0.863	0.860	0.863
Glioma	0.983	0.933	0.883	0.981
Meningioma	0.978	0.922	0.833	0.893
Metastases	0.932	0.882	0.833	0.500

*10-fold Cross Validation*
Average over classes	0.734	0.716	0.702	0.716
Glioma	0.859	0.853	0.831	0.907
Meningioma	0.725	0.765	0.559	0.679
Metastases	0.573	0.814	0.566	0.250
AUC = area under the curve, CA = classification accuracy.

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published 08 Mar, 2022

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Advantages and limitations of intraoperative ultrasound strain elastography applied in brain tumor surgery: a single-center experience

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Abstract

Figures

Introduction

Methods

Patient selection

Intraoperative ultrasound image acquisition technique

Elastogram processing

Statistical analysis

Results

Discussion

Elastogram quality

Meningiomas

Gliomas

Metastases

Histological classification based on elastography and glioma grading

Strain elastography versus shear wave elastography

Brain elastography and future perspectives

Conclusion

Declarations

Disclosures:

Conflicts of Interest:

Funding:

Acknowledgments:

References

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