Abstract PS4-45: Computerized image analysis of nuclear morphological features reveals differences in phenotype and prognosis of disease free survival of early stage ER+ breast cancers for South Asian and North American women
Authors: Li Haojia , Bera Kaustav , Toro Paula , Fu Pingfu , Rao Vidya , Siddique Shabina , Harbhajanka Aparna , Sechrist Haley , Zhang Zelin , Desai Sangeeta , Parmar Vani , Madabhushi Anant 
Published: February 2021

Background: Breast cancer is the most common cancer worldwide. There has been emerging interest in studying differences in breast tumor phenotypes between South Asian and South American women given that racial/ethnic disparities in incidence and mortality have been demonstrated in multiple studies. South Asian women are more likely to be diagnosed with an advanced stage breast tumor despite lower incidence than in North American women. There is evidence that computer-extracted nuclear morphological features on H&E slide images may be associated with breast cancer aggressiveness, specifically recurrence and disease free survival. However, studies have mostly focused on North American women. In this work, we evaluated whether there is a difference in computer extracted features of nuclear morphology from H&E tissue slide images between South Asian (SA) and North American (NA) women and we also investigated how these differences could impact the development of population specific breast cancer prognostic models. Methods: H&E slides of breast tumors from patients who were diagnosed with ER+ early stage invasive breast cancer from Tata Memorial Centre, India (SA: 69 (20 recurrence)) and from University Hospitals Cleveland Medical Center (NA: 121 (20 recurrence)), along with outcome information were collected. All slides were digitized on either a Ventana DP 200 or a Roche Ventana iScan HT slide scanner. For each image, a conditional Generative Adversarial Network model was employed to segment the individual nuclei, which were used to generate 241 nuclear features including nuclear architecture, shape, orientation disorder, and texture features. Half of the patients (95) were randomly selected as the training set (Stra) with the remaining patients as the hold-out validation set (Stest). Three elastic net regularized Cox regression models (MSA, MNA, MSA+NA) were trained fitting between the nuclear features and disease free survival (DFS) respectively for SA subset, NA subset, and SA & NA set in Stra. The top five prognostic features were identified respectively from each of the three models (MSA, MNA, MSA+NA) which were further validated on Stest to evaluate their prognostic value in prediction of DFS. Results: We found that the prognostic features identified by MNA and MSA+NA were mostly shape features (three out of five for MNA, four out of five for MSA+NA), while the prognostic features identified by the model specifically trained with the SA population (MSA) were mostly texture features (three out of five), possibly reflecting chromatin patterns in the cell. MSA yielded a better performance (Hazard Ratio=4.99 (p=0.00928, CI=1.32-18.9) from log-rank test between model derived high and low risk categories) on SA population in Stest compared to MNA and MSA+NA. Conclusion: We found that nuclear histomorphometry features were different between breast cancer patients from South Asia and those from North America. The prognostic capability of the computational pathology-based models for South Asian women could be significantly improved by taking into account population-specific information. An additional independent validation set is needed to confirm the preliminary findings presented here.

Citation Format: Haojia Li, Kaustav Bera, Paula Toro, Pingfu Fu, Vidya Rao, Shabina Siddique, Aparna Harbhajanka, Haley Sechrist, Zelin Zhang, Sangeeta Desai, Vani Parmar, Anant Madabhushi. Computerized image analysis of nuclear morphological features reveals differences in phenotype and prognosis of disease free survival of early stage ER+ breast cancers for South Asian and North American women [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr PS4-45.