Article Figures & Data
Figures
- Fig 1.
Whole-cell counting. A, Digitized, low-power magnification view of a single H&E-stained slide. Two representative 400× fields from this single tissue specimen of relatively lower (B) and higher (C) cell density illustrate the tissue heterogeneity present at a microscopic level. Stained cellular nuclei identified by the automated counting algorithm are outlined in green. D, The “heat map” demonstrates distribution of cell density at the level of a HPF throughout the tissue sample.
- Fig 2.
Cell-counting statistics. A, Comparison between manual and automated cell counts for 25 high-power fields of various cellular densities. Correlation is high (r = 0.984), suggesting that the automated algorithm accurately reflects manual counts. B, For each biopsy sample, the median cell density of all HPFs is compared with that of the 98th percentile. A relatively strong linear correlation is preserved (r = 0.901), suggesting that the 98th percentile cell density simply represents a linear translation of the median cell density. C, Correlation analysis is repeated for all percentiles (0–100). With the exception of extreme values, most percentiles retain a strong linear correlation (r > 90%) with the median cell density.
- Fig 3.
Cell count versus MR signal intensity. Scatterplots demonstrate median cell density as a function of signal intensity on ADC (A), T2-FLAIR (B), and T1-postcontrast subtraction sequences (C) correlated by using single-variable regression analysis. The linear regression and Pearson correlation (r) were significant (P < .05) for all 3 sequences. D, The scatterplot shows the actual and predicted cell counts as estimated by combining all 3 imaging modalities in a multiple-variable regression model.
- Fig 4.
Correlation versus distance from the biopsy. Scatterplots demonstrate the correlation between cell density and signal intensity for each MR image (ADC, T2-FLAIR, T1-postcontrast subtraction) obtained by taking the mean of concentric spheric shells of voxels at an increasing distance from the original biopsy point. Notably, the correlations drop to 0 at a radius of approximately 5 mm (∼10 voxels), providing an estimate of the spatial accuracy of the biopsy location. T1SUB indicates T1-subtraction.
- Fig 5.
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).
Tables
Multivariate linear regression model coefficients
β SE T-Score P Value Constant 102 5.98 17.1 <.001 ADC −106 32.0 −3.30 <.001 FLAIR −56.0 23.5 −2.38 <.001 T1-subtracted 129 24.6 5.27 <.001 Note:—SE indicates standard error.
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