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1000
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Abstract/Summary
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Design!#!Prospective diagnostic study.!##!Objectives!#!Primary imaging-based diagnosis of spinal cord tumor-suspected lesions is often challenging. The identification of the definite entity is crucial for dedicated treatment and therefore reduction of morbidity. The aim of this trial was to investigate specific quantitative signal patterns to differentiate unclear intramedullary tumor-suspected lesions based on diffusion tensor imaging (DTI).!##!Setting!#!Medical Center - University of Freiburg, Germany.!##!Methods!#!Forty patients with an unclear tumor-suspected lesion of the spinal cord prospectively underwent DTI. Primary diagnosis was determined by histological or clinical work-up or remained indeterminate with follow-up. DTI metrics (FA/ADC) were evaluated at the central lesion area, lesion margin, edema, and normal spinal cord and compared between different diagnostic groups (ependymomas, other spinal cord tumors, inflammations).!##!Results!#!Mean DTI metrics for all spinal cord tumors (n = 18) showed significantly reduced FA and increased ADC values compared to inflammatory lesions (n = 8) at the lesion margin (p < 0.001, p = 0.001) and reduced FA at the central lesion area (p < 0.001). There were no significant differences comparing the neoplastic subgroups of ependymomas (n = 10) and other spinal cord tumors (n = 8), but remaining differences for both compared to the inflammation subgroup. We found significant higher ADC (p = 0.040) and a trend to decreased FA (p = 0.081) for ependymomas compared to inflammations at the edema.!##!Conclusion!#!Even if distinct differentiation of ependymomas from other spinal cord neoplasms was not possible based on quantitative DTI metrics, FA and ADC were feasible to separate inflammatory lesions. This may avoid unnecessary surgery in patients with unclear intramedullary tumor-suspected lesions.
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1000
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Hinweis
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DeepGreen-ID: d7041ffaf24a46a6852534956219f0af ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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