Artikel
Early postoperative delineation of residual tumor after low-grade glioma resection by probabilistic quantification of diffusion-weighted imaging Imaging
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Autoren
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Moritz Scherer
- Universitätsklinikum Heidelberg, Neurochirurgische Klinik, Heidelberg, Deutschland
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Christine Jungk
- Universitätsklinikum Heidelberg, Neurochirurgische Klinik, Heidelberg, Deutschland
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Michael Götz
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Abteilung Medizinische Bildverarbeitung, Heidelberg, Deutschland
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Philipp Kickingereder
- Universitätsklinikum Heidelberg, Neuroradiologie, Heidelberg, Deutschland
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Martin Bendszus
- Universitätsklinikum Heidelberg, Neuroradiologie, Heidelberg, Deutschland
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Klaus Maier-Hein
- Deutsches Konsortium für Translationale Krebsforschung (DKTK), Abteilung Medizinische Bildverarbeitung, Heidelberg, Deutschland
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Andreas W. Unterberg
- Universitätsklinikum Heidelberg, Neurochirurgische Klinik, Heidelberg, Deutschland
| Veröffentlicht: | 18. Juni 2018 |
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Gliederung
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Objective: In WHO grade II low-grade gliomas (LGG), early postoperative MRI (epMRI) may over-estimate residual tumor on fluid-attenuated inversion recovery (FLAIR) sequences. Consequently, MRI at 3-6 months follow-up (fuMRI) is used for delineation of residual tumor. This study sought to evaluate if integration of apparent diffusion coefficient (ADC)-maps permits an accurate estimation of residual tumor already on epMRI.
Methods: From a consecutive cohort, 43 cases with first surgery for a LGG and complete epMRI (<72h) and fuMRI including ADC-maps were retrospectively identified. Residual FLAIR hyperintense tumor was manually segmented on epMRI and corresponding ADC-maps were co-registered. Using an expectation maximization algorithm, residual tumor segments were probabilistically clustered into areas of either residual tumor, ischemia or normal white matter (NWM) by fitting a mixture model of superimposed Gaussians to the ADC histogram. Tumor volumes from epMRI, clustering and fuMRI were statistically compared and agreement analysis was performed.
Results: Mean FLAIR hyperintensity suggesting residual tumor was significantly larger on epMRI compared to fuMRI (19.4±16.5ml vs. 8.4±10.2ml, p<0.0001). Probabilistic clustering of corresponding ADC-histograms on epMRI identified subsegments that were interpreted as mean residual tumor (7.6±10.2ml), ischemia (8.1±5.9ml) and NWM (3.7±4.9ml), respectively. Thereby, mean tumor quantification error between epMRI and fuMRI was significantly reduced (11.0±10.6ml vs. -0.8±3.7ml, p<0.0001). Mean clustered tumor volumes on epMRI were no longer different from the fuMRI reference (7.6±10.2ml vs. 8.4±10.2ml, p=0.16). Correlation [Pearson r=0.96 (p<0.0001), Concordance Correlation Coefficient (CCC) 0.89 (95% CI 0.83)] and Bland-Altman analysis suggested strong agreement between both measures after clustering.
Conclusion: Probabilistic segmentation of ADC-maps facilitates accurate assessment of residual tumor within 72h after LGG resection. Multiparametric image analysis detected FLAIR signal alterations attributable to surgical trauma, which led to overestimation of residual LGG on epMRI compared to fuMRI. The prognostic value and clinical impact of this method has to be evaluated in larger case-series in the future.
