Fig. 2.

Model predictions. Plots show the observed Ki-67 at each biopsy location compared with predicted Ki-67 from a random forest using 5-fold cross validation. Each point represents a prediction made on a member of a fold not used for training during cross validation. The dashed lines indicate perfect agreement (predicted equals observed) and the solid line is a best-fit. In both plots the correlation between predicted and observed values is significant (P < 0.0001). (A) The final model using 4 imaging inputs (T2, FA, CBF, K^trans) based on conventional plus advanced imaging gives good estimates of Ki-67 compared with the ground truth immunohistochemistry (best fit line: predicted = 0.680 * observed + 1.10, R² = 0.771). (B) Using conventional imaging data, only the model performance decreases, as shown by the weakened correlation between predicted and observed values (best fit line: predicted = 0.426 * observed + 2.09, R² = 0.397). There are also considerably more outliers using conventional imaging only. The R² values for these plots are comparable to the average R² values from cross validation listed in Table 3, which calculates the correlations between predicted and observed values for each fold separately and averages.