Abstract 3144: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer

Abstract Introduction: Predictive value of adjuvant chemotherapy for patients with early-stage hormone receptor-positive breast cancer has been suggested by 21-gene expression assay, although its cost-effectiveness has not been well-defined. We have developed the deep learning-based H&amp;E image analyzer named Lunit SCOPE, identifying and quantifying various histologic components from H&amp;E-stained whole slide images We hypothesized that cell proportions analyzed by Lunit SCOPE would be a potential prognostic and predictive biomarker of adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer. Method: We have collected clinical data and H&amp;E slides from de-identified 2,915 early breast cancer patients in Samsung Medical Center, retrospectively. The 898 patients with hormone receptor-positive, T1b ~ T3 and N0 ~ N1mi have been selected to analyze the predictive value of adjuvant chemotherapy. Deep learning-based H&amp;E image analyzer, Lunit SCOPE, has been trained by 1,191 H&amp;E-stained whole slide images from another breast cancer patient cohort. In the whole slide image, biological and histological components such as cancer epithelium, cancer stroma, normal, fat, necrosis, lymphocyte, fibroblast, and other cells, have been annotated by over 10 pathologists. The outputs of Lunit SCOPE are the ratio of cancer epithelium, cancer stroma, normal, necrosis and fat in a whole slide image and intratumoral tumor infiltrating lymphocyte (TIL) and stromal TIL density. The recurrence score (RS) based on the output of Lunit SCOPE has been determined by using multivariate cox regression analysis for disease-free survival (DFS) in the patients without adjuvant chemotherapy. Result: Recurrence score (RS) was proportional to the cancer stroma ratio and stromal TIL density, but inversely proportional to intratumoral TIL density. When the RS cutoff was 0.913, 21.3% (191 out of 898) of patients were classified as high risk group (RS &amp;gt; cutoff). Among those without adjuvant chemotherapy, high risk group presented poor DFS (hazard ratio [HR] 4.23, 95% confidence interval [CI] 1.87-9.59, P = 1.73 x 10-4) and overall survival (OS, HR 4.95, 95% CI 1.39-17.6, P = 6.07 x 10-3) than the low risk group. Adjuvant chemotherapy did not prolong OS in patients with low risk group (HR 1.08, 95% CI 0.38-3.12, P = 0.885). However, interestingly, in those with high risk by Lunit SCOPE, adjuvant chemotherapy prolonged DFS (HR 0.35, 95% CI 0.15-0.86, P = 0.0161) and OS (HR 0.22, 95% CI 0.05-0.95, P = 0.0254), reflecting RS by Lunit SCOPE would be a significant predictive biomarker of adjuvant chemotherapy. Conclusion: Deep learning-based H&amp;E image analyzer, Lunit SCOPE, was possible to analyze the prognosis of breast cancer. Especially, only high risk patients of RS by Lunit SCOPE had survival benefit from adjuvant chemotherapy, which needs to be validated in clinical trials. Citation Format: Soo Youn Cho, Eun Yoon Cho, Kyunghyun Paeng, Geunyoung Jung, Sarah Lee, Sang Yong Song. Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3144.

Topic: Radiomics and Machine Learning in Medical Imaging
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences