Abstract P4-09-12: Quantitative image features of nuclear and tubule architecture distinguish high and low oncotype DX risk categories of ductal carcinoma in situ from H&amp;E tissue images
Authors: H Li, J Whitney, R Thawani, H Gilmore, S Badve, A Madabhushi
Journal: Cancer Research (2018)

Abstract Background: Ductal Carcinoma in Situ (DCIS) is a pre-invasive stage of breast cancer, where malignant cells line the duct but have not spread into other parts of the breast. Oncotype DX (ODX) is a genomic test, which divide patients into three risk of recurrence categories (Low, Intermediate, and High) to help physicians determine if patients require adjuvant therapy. However, ODX is expensive, tissue destructive, and has a turnaround time of 7-10 days. There has been an interest in the use of image analysis of routine H&amp;E histopathology slides to predict the course of the disease; the rationale being that the analytics are able to unearth subtle sub-visual cues regarding disease morphology that may escape visual examination. In this work, we evaluate the role of computer extracted features of nuclear morphology and the necrotic regions from surgically resected specimens to predict ODX categories in patients with DCIS. Methods: H&amp;E slides from breast tissue of 37 patients who were diagnosed with DCIS and underwent a lumpectomy were acquired. Nine of the 37 had high ODX score (higher than 54), while the rest had a low score (lower than 39). All the slides were digitized on a Philips slide scanner. For each image, a watershed algorithm segmented the individual nuclei, which were used to generate 230 nuclei features including nuclear architecture, nuclear shape and nuclear texture features within each candidate breast duct. In addition, we captured the area of necrosis and empty lumen region inside breast ducts to generate features pertaining to tubule packing. The average feature values for each patient were calculated across all the breast ducts in each slide. A 3-fold cross validation scheme with 50 repetitions was used with the Support Vector Machine (SVM) classifier to predict the ODX risk category for each patient. We used a covariance algorithm to select the top 4 features that were independent of each other but relevant to the ODX class label. Results: The top ranked features included features from three categories: nuclei architectural features (standard deviation of triangle area in Delaunay graph, skewness of edge length in Cell Cluster Graph), nuclear texture (standard deviation of Haralick matrix intensity) possibly reflecting chromatin patterns in the cell, and the Tubule Packing Ratio, a measure of the ratio of necrosis area and empty lumen area inside the breast ducts compared to the whole breast duct area. The SVM in conjunction with these 4 features yielded a mean area under receive operator characteristic curve (AUC) of 0.95 in correctly predicting high and low ODX risk categories. Conclusion: We found that our histomorphometry features pertaining to nuclear arrangement, nuclear texture and necrosis could differentiate between DCIS patients with high and low ODX risk categories. Additional independent validation of the approach is needed to confirm the preliminary findings presented here. Citation Format: Li H, Whitney J, Thawani R, Gilmore H, Badve S, Madabhushi A. Quantitative image features of nuclear and tubule architecture distinguish high and low oncotype DX risk categories of ductal carcinoma in situ from H&amp;E tissue images [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-09-12.