Background: Tumor microenvironment (TME) characteristics are gaining acceptance as important biomarkers across all subtypes of breast cancer (BC). For example, stromal Tumor Infiltrating Lymphocytes (sTILs) have been demonstrated to be predictive in triple-negative and HER2+ BC on immune therapies (Hudeček, 2020), and TILs have been associated with recurrence-risk in HER2- BC (Kolberg-Liedtke, 2020). However, standardizing TME biomarkers remains a challenge to these prognostic & predictive studies (Kos, 2020). Methods: Diagnostic H&E-stained pathology images from 506 HER2- breast cancer patients were acquired from TCGA sources. Pre-trained convolutional neural networks were used to classify each 100μm2 region as containing tumoral, stromal, and lymphocyte-infiltrated tissue as well as map their spatial co-distribution. Nine TME summary features were derived from these spatial maps, including total lymphocyte area, tumor-infiltrating lymphocytes (iTILs), stromal-infiltrating lymphocytes (sTILs), and tumor-adjacent lymphocytes (aTILs). Prognostic models relating these TME features to risk were fitted using Cox multiple-regression trained on 60% of patients and tested in the remaining 40%. Additional Cox models that incorporate seven standard clinicopathological features such as TNM staging, age, ethnicity, treatment type, and hormone-receptor status were also analyzed to establish independence of the TME features. Results: A prognostic model developed using 9 TME-summarizing features accurately stratified unseen patients (HR=0.67 p=0.002) into high-risk (N=95) and low-risk (N=107) categories. Interestingly, tumor-adjacent stroma was significantly associated with higher risk (proportional HR=1.02, p=0.005) whereas tumor-infiltrating stroma was associated with lower risk (proportional HR=0.97, p=0.02). Incorporating standard clinicopathological features increased prognostic performance in test patients (HR=0.63, p=0.0005). The prognostic effect of tumor-adjacent and tumor-infiltrating stroma remained significant in multiple regression with clinicopathological features (p=0.003 & p=0.02 respectively). Conclusions: Here we present using machine-vision to automate and standardize describing the TME, as well as demonstrate the prognostic potential of these descriptions by successfully stratifying risk in HER2- breast cancer. These TME descriptions add independent prognostic power to standard clinicopathological features. Machine-vision tools that produce interpretable features such as this can inform current pathology practices, as well as provide facile and scalable biomarkers for clinical studies going forward. Citation Format: Mustafa I. Jaber, Liudmila Beziaeva, Stephen C. Benz, Shahrooz Rabizadeh, Patrick Soon-Shiong, Christopher W. Szeto. Deep-learning image-based features stratify risk in HER2- breast cancer patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 3171.