Table 2. Evaluation of classifiers as indicators of tumor infiltration.

Nuclear density, axonal density, and protein:lipid ratio were measured for each of the 1477 300×300 μm² FOVs from 51 fresh tissue biopsies from 18 patients. A quasi-likelihood approach with a GAM was used to incorporate all of the attributes into a single classifier. Half of the FOVs (n=738) were used to create the classifier, which was then tested on the other half of the data (n=739). Given that glioma can be more difficult to distinguish from normal tissue than metastases and extra-axial tumors, a quasi-likelihood generalized additive model (GAM) was also used on a subset of tumors, excluding all non-glial tumors, to create the glioma-only classifier. To eliminate correlation between the testing set and training set, we used a leave-one-out cross-validation approach. The leave-one-out cross-validation was performed in a data set excluding non-glioma patients.

Classification condition	Area under curve	Mean sensitivity (%)	95% CI	Mean specificity (%)	95% CI
GAM (all subjects)
Normal vs. abnormal	0.995	97.5	95.9–98.9	98.5	97.0–99.7
Normal vs. infiltrating	0.988	94.7	91.4–98.9	98.5	97.0–99.5
Normal vs. dense	0.989	98.0	95.6–100	99.0	97.4–100
GAM (glioma only)
Normal vs. abnormal	0.994	97.0	95.0–98.7	98.7	97.2–99.5
Normal vs. infiltrating	0.988	94.9	91.3–98.1	98.5	97.1–99.5
Normal vs. dense	0.990	98.2	95.1–100	99.0	98.2–100
Leave-one-out cross-validation
Normal vs. abnormal	0.893	87.3	n/a	87.5	n/a
Normal vs. infiltrating	0.911	82.8	n/a	95	n/a
Normal vs. dense	0.908	83.9	n/a	93.3	n/a

CI, confidence interval.