Abstract P5-06-15: Computer extracted features of nuclear shape, orientation disorder and texture from H&E Whole slide images are associated with disease-free survival in ductal carcinoma in situ (DCIS) (2020)

Abstract Background: Ductal Carcinoma in Situ (DCIS) of the breast comprises a diverse group of cancerous lesions confined to the breast ducts. About 25% of all breast cancers in the US are DCIS and women with DCIS have a 10 times higher lifetime risk of progressing to invasive breast cancer (IBC). While DCIS is treated routinely with breast conserving therapy which includes a lumpectomy followed by adjacent radiation therapy (RT). However, studies have shown that RT can be omitted in women with a lower risk of recurrence/progression. Gene expression methods like Oncotype Dx can be used to risk stratify patients but are expensive, tissue-destructive and time consuming. Recently, there has been substantial interest in developing computerized approaches to quantitatively extract tumor histomorphometric (QH) features from digitized images of tissue slides to predict cancer outcome. In this work, we evaluate the role of QH features related to the architecture, shape, orientation disorder and texture of cancer nuclei from lumpectomy specimens of DCIS to predict disease-free survival (DFS). Methods: H&amp;E whole slide images (WSI) and corresponding outcome information for 121 DCIS patients (61 with progression/recurrence and 60 without) from the no adjuvant treatment arm of the UK/ANZ DCIS trial were retrieved and collected from Queen Mary university of London. WSIs were scanned by 3DHistech scanner at 43x magnification. For each image, a deep learning based generative adversarial network model was employed to segment the cancer nuclei, following which 379 nuclear features from four feature families including architecture, shape, orientation disorder and Haralick texture features were extracted within each candidate breast duct in the WSI and averaged across the multiple cancerous breast ducts for each patient. The dataset was randomly split into training (D1, n=60) and a hold-out validation set (D2,n=61) with events split equally in the two sets. For each of the four feature families, a set of 6 top features was identified by an elastic Cox Model to predict DFS (time from diagnosis to recurrence/invasive breast cancer or death whichever is sooner) onD1. The feature sets giving significant prognostic value (assessed by log rank test between the feature derived risk groups) were combined, from which 6 top features were further identified by a Cox Model to predict DFS on D1. Results: The top ranked QH features included features from three feature families: 2 shape features, 1 orientation disorder feature and 3 texture features. The Elastic Cox model predicted DFS on D1 with hazard ratio (HR) of 7.29 (95% CI = 2.94 ~18.05, p=1.75e-05) and on D2 with HR of 2.07 (95% CI = 1.02 ~2.94, p=0.045). Conclusion: Computer extracted nuclear histomorphometric features pertaining to shape, orientation disorder and texture were associated with disease free survival for DCIS. Additional independent multi-site validation of the approach is planned to further confirm the preliminary findings presented here. Citation Format: Haojia Li, Kaustav Bera, Hannah Gilmore, Zelin Zhang, Jack Cuzick, Mangesh A Thorat, Anant Madabhushi. Computer extracted features of nuclear shape, orientation disorder and texture from H&amp;E Whole slide images are associated with disease-free survival in ductal carcinoma in situ (DCIS) [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 P5-06-15.