A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies

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Ninety-one localized biopsies were obtained from 36 patients with glioblastoma. Signal intensities corresponding to these samples were derived from T1-postcontrast subtraction, T2-FLAIR, and ADC sequences by using an automated coregistration algorithm. Cell density was calculated for each specimen by using an automated cell-counting algorithm. T2-FLAIR and ADC sequences were inversely correlated with cell density. T1-postcontrast subtraction was directly correlated with cell density. The authors conclude that the model illustrates a quantitative and significant relationship between MR signal and cell density. Applying this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity for both enhancing core and nonenhancing margins.

Abstract

Figure 5 from paper
Whole-tumor model overlay. Estimated cellularity by applying the multiple regression model on a voxelwise basis across the tumor. The model is derived from linear regression by using ADC, T2-FLAIR, and T1-postcontrast sequences shown in the inset on the left. In the right panels, corresponding biopsy specimens (400× magnification, H&E stained sections) are shown from 2 regions obtained on the same section, highlighting the considerable variation in cellularity in and around the region of contrast enhancement (demarcated by a white outline).

BACKGROUND AND PURPOSE

The complex MR imaging appearance of glioblastoma is a function of underlying histopathologic heterogeneity. A better understanding of these correlations, particularly the influence of infiltrating glioma cells and vasogenic edema on T2 and diffusivity signal in nonenhancing areas, has important implications in the management of these patients. With localized biopsies, the objective of this study was to generate a model capable of predicting cellularity at each voxel within an entire tumor volume as a function of signal intensity, thus providing a means of quantifying tumor infiltration into surrounding brain tissue.

MATERIALS AND METHODS

Ninety-one localized biopsies were obtained from 36 patients with glioblastoma. Signal intensities corresponding to these samples were derived from T1-postcontrast subtraction, T2-FLAIR, and ADC sequences by using an automated coregistration algorithm. Cell density was calculated for each specimen by using an automated cell-counting algorithm. Signal intensity was plotted against cell density for each MR image.

RESULTS

T2-FLAIR (r = −0.61) and ADC (r = −0.63) sequences were inversely correlated with cell density. T1-postcontrast (r = 0.69) subtraction was directly correlated with cell density. Combining these relationships yielded a multiparametric model with improved correlation (r = 0.74), suggesting that each sequence offers different and complementary information.

CONCLUSIONS

Using localized biopsies, we have generated a model that illustrates a quantitative and significant relationship between MR signal and cell density. Projecting this relationship over the entire tumor volume allows mapping of the intratumoral heterogeneity in both the contrast-enhancing tumor core and nonenhancing margins of glioblastoma and may be used to guide extended surgical resection, localized biopsies, and radiation field mapping.

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A Multiparametric Model for Mapping Cellularity in Glioblastoma Using Radiographically Localized Biopsies
Jeffrey Ross
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