NI FEATURE: THE EDITORIAL DEBATE III-- PROS AND CONS
|Year : 2018 | Volume
| Issue : 6 | Page : 1612--1613
Use of quantitative diffusion tensor imaging as a diagnostic tool for intracranial lesions
Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
Dr. Aditya Vedantam
Department of Neurosurgery, Baylor College of Medicine 7200 Cambridge, Suite 9A, MS: BCM650 Houston, TX 77030
|How to cite this article:|
Vedantam A. Use of quantitative diffusion tensor imaging as a diagnostic tool for intracranial lesions.Neurol India 2018;66:1612-1613
|How to cite this URL:|
Vedantam A. Use of quantitative diffusion tensor imaging as a diagnostic tool for intracranial lesions. Neurol India [serial online] 2018 [cited 2022 Jun 25 ];66:1612-1613
Available from: https://www.neurologyindia.com/text.asp?2018/66/6/1612/246221
There is increased interest in using advanced neuroimaging for the diagnosis and treatment of intracranial lesions. With the development of novel magnetic resonance imaging (MRI) sequences, we are able to better understand tissue microstructure and predict histopathology of brain lesions. However, in some cases, no single imaging sequence can conclusively diagnose the lesion, and multiple pieces of information obtained from different imaging sequences are needed to make a preliminary diagnosis.
In the present manuscript by Soni et al., the authors used quantitative diffusion tensor imaging (DTI) to determine if certain DTI metrics, measured from perilesional tissue and within the lesion, could predict the final histopathology. In studying 25 neoplastic and 25 non-neoplastic lesions, the authors found significantly higher mean diffusivity (MD) for non-neoplastic lesions as compared to neoplastic lesions. In addition, there were significant differences in fractional anisotropy (FA) and MD for different types of lesions- high grade gliomas, metastases and tuberculomas. These results led the authors to conclude the DTI metrics, particularly FA and MD, may be useful tools to determine lesion pathology.
This study demonstrates some of the advantages and challenges encountered while using quantitative DTI as a non-invasive biomarker for intracranial lesions. Since all patients underwent surgery in this series, the authors were able to conclusively determine the histopathology of the lesions and correlate this with DTI metrics. This study evaluated both neoplastic and non-neoplastic cases, and it was discovered that DTI may be a useful tool to preoperatively distinguish these two groups of lesions. By evaluating perilesional areas, which look similar on conventional MRI, the authors were able to show that DTI can differentiate perilesional tissue around neoplastic and non-neoplastic lesions. These findings underscore the advantage of DTI over conventional MR imaging for certain lesions.
Some of the challenges encountered with the use of quantitative DTI are also evident in this manuscript. DTI metrics, such as FA and MD, represent a measure of water diffusion within a region of interest. Therefore, measurement of DTI metrics may be affected by heterogeneity of the lesion. For example, partially cystic or calcified lesions can have different DTI metrics depending on where the region of interest is drawn within the lesion. The location of the region of interest within the lesion was not standardized in this manuscript, and this may have affected the results. Although the authors found significant differences in MD of the perilesional tissue for neoplastic and non-neoplastic lesions, they did not have histopathological data for the perilesional tissue to explain these differences. As the authors hypothesize, microstructural changes due to the underlying lesion are most likely responsible for these differences; however, these remain hypotheses at this time. Lastly, the authors describe group differences between lesions, and it is unclear if there is a threshold FA or MD that can determine the etiology of a lesion for the individual patient.
DTI is commonly used to describe white matter tracts for surgical planning of intracranial lesions. However, Soni et al. highlight the diagnostic utility of quantitative DTI metrics for intracranial lesions. The characterization of white matter tracts using DTI is often performed using semi-automated methods, and is used in select cases for planning a surgical approach. There is variability in the description of white matter tracts that can depend on the FA threshold selected. Similarly, the measurement of DTI metrics within and around the lesion requires manually drawn regions of interest, which may contribute to some variability in the results. Uniform scanning protocols to acquire DTI images as well as standardized software to measure DTI metrics could help in translating this technique for more widespread clinical use.
Although advanced MR imaging is not necessary for routine clinical use, there is a role for these techniques in certain lesions, for example, in distinguishing infectious and neoplastic cysts. The added diagnostic value of DTI scans needs to be weighed against additional scanning time and processing required to measure DTI metrics. Newer techniques such as MR texture analysis use conventional MR images, and analyze the heterogenous MR signal intensity within the lesion. These techniques can predict tumor pathology, and in some cases, even tumor genomics. However, histopathology remains the gold standard for diagnosing intracranial lesions, and at present, imaging represents an adjunct diagnostic tool. It is likely that composite data obtained from various imaging sequences may offer the most accurate prediction of lesion pathology. In light of the findings of the present study, the addition of DTI sequences to conventional MR imaging may add valuable diagnostic information for select intracranial lesions.
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