Abstract P5-02-02: Artificial intelligence powered predictive analysis of atypical ductal hyperplasia from digitized pathology images
Authors: Kerner Jennifer K. , Cleary Allison , Jain Suyog , Pokkalla Harsha , Glass Benjamin , Grossmith Sam , Harary Maya , Mittendorf Elizabeth , Beck Andrew H. , Khosla Aditya , Schnitt Stuart J. , Wapinski Ilan , King Tari 
Published: March 2020

Background: Approximately 15-25% of patients with atypical ductal hyperplasia (ADH) diagnosed on breast core needle biopsy (CNB) are upgraded to ductal carcinoma in situ (DCIS) or invasive carcinoma (IC) on surgical excision. The reproducible identification of patients with ADH on CNB who are more likely to have upgrades at excision remains elusive. We hypothesized that a machine learning approach could be utilized to train models to recognize ADH on digitized pathology images and to identify cases of ADH more likely to be upgraded to DCIS or IC at excision. The purpose of this study was to determine the accuracy of the machine learning approach to identify ADH.

Methods: 726 digitized images of CNB slides derived from 306 cases with a diagnosis of ADH between 11/2004-3/2018 were included in this study. Independent histologic review by two breast pathologists identified slides with and without ADH from each case. 39 board certified pathologists with experience in evaluation of breast biopsies were employed for tissue region annotation on the PathAI research platform (not intended for diagnostic purposes), yielding 14,118 tissue region annotations. Region annotations included ADH, ADH stroma, flat epithelial atypia (FEA), lobular neoplasia (LN), calcifications (Ca), columnar cell change/hyperplasia, sclerosing adenosis, papilloma, normal terminal duct lobular units and other non-atypical breast tissue regions. These annotations were used to train a convolutional neural network (CNN) with 35 layers and approximately 9 million parameters to identify ADH. The data were split into training and testing sets, representing 61.1% and 38.9% of the data respectively. The distribution of cases, images with ADH and cases with upgrade were balanced between the training and testing sets.

Results: CNB specimens were assigned labels of “ADH” or “No ADH” based on histologic assessment. AI models were able to predict the diagnosis of ADH with 85% sensitivity (144 of 168 images within the test set) and 69% specificity (78 of 113 images within the test set). The slide-level area under the receiver operator curve (ROC) for this model was 0.84.

Conclusions: A deep learning-based classifier showed strong performance for the identification of ADH from whole slide images of H&E stained breast CNBs. With further development, this approach may improve the reproducibility and standardization of the diagnosis of ADH. Future analyses will focus on determining if morphologic features of ADH extracted by the deep learning system can be used to predict upgrade to DCIS and IC. This approach may help stratify patients with ADH on CNB into those who require surgical excision and those who can be followed with active surveillance.

Citation Format: Jennifer K. Kerner, Allison Cleary, Suyog Jain, Harsha Pokkalla, Benjamin Glass, Sam Grossmith, Maya Harary, Elizabeth Mittendorf, Andrew H. Beck, Aditya Khosla, Stuart J. Schnitt, Ilan Wapinski, Tari King. Artificial intelligence powered predictive analysis of atypical ductal hyperplasia from digitized pathology images [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P5-02-02.