Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer

Abstract

Background: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive.

Methods: In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation.

Results: The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%.

Conclusion: Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Fig. 1

Illustration of the methodology used to classify whole slide images into ODx risk categories. 1) Image patches are extracted at 40× from regions within whole slides identified by pathologists as containing invasive cancer. 2) Nuclei detection is performed on these image patches and 3) combined with a Deep Learning epithelial/stromal separation model. 4) Nuclear architecture and shape features are extracted from the detected epithelial and stromal nuclei separately. These features are combined with (5) a trained classification model in order predict the ODx risk category for each patch. Classification results from the image patches for each patient are (6) combined in a patch-based-voting method to (7) yield the final risk prediction on a patient level

Fig. 2

Nuclear graphs used to calculate features relating to spatial arrangement of nuclei. Left to right: Original images at 1×, 4×, and 40×, Voronoi Diagram, Minimum Spanning Tree, and Cell Cluster Graph, reflecting local nuclear architecture. Comparison between graph appearance for a low ODx example (top) and a high ODx example (bottom)

Fig. 3

Example of the Low-Low vs. High-High random forest classifier using ranksum feature selection applied to patches from whole slide image. Machine classification uses the top ranked epithelial and stromal features. Green squares indicate patches that are predicted to be Low ODx while Blue squares are predicted to be High ODx

Fig. 4

Feature Distributions for the top ranked epithelial (left) and stromal (right) features using PCA-VIP feature ranking for each experiment. Green lines indicate the mean of each population, and red lines indicate the 25th and 75th percentiles of the distribution. Width of the plot indicates the relative number of data points at each normalized feature value along the y-axis

Fig. 5

ROC curves for each of the four experiments conducted (panels) and classification methods (lines) using PCA-VIP feature selection. Top left: Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High). Top Right: Low ODx vs. High ODx. Bottom Left: Low ODx vs. Intermediate and High ODx. Bottom Right: Low and Intermediate ODx vs. High ODx. Each panel displays the ROC curve using either (solid) random forest, (dashed) neural network, (dotted) SVM, or (intermediate dash) LDA classification. Feature set includes epithelial and stromal features. AUC values for each curve are displayed in the legend

Fig. 6

Determining the optimal feature ranking method - ROC curves for different combinations of feature ranking methods (panels) and classification methods (lines) for separating low from high ODx patches. Top left: Ranksum (Wilcoxon rank sum). Top right: PCA-VIP. Bottom left: MRMR-MID. Bottom right: MRMR-MIQ. Each panel displays the ROC curve using either (solid) random forest, (dashed) neural network, (dotted) SVM, or (intermediate dash) LDA classification. Feature set includes stromal and epithelial features. AUC values for each curve are displayed in the legend