Clinical decision-making for breast cancer relies on patient-stratification based on clinicopathologic factors, like stage, Ki67-Index (KI) and histological grade. The Nottingham Grading System (NGS) provides prognostic information by combining glandular differentiation, nuclear pleomorphism and mitotic activity scores in breast tumors. In NGS, mitotic-index (MI) is defined as number of mitotic cells per 10 HPFs. KI is a universal prognostic indicator but not part of NGS. Although NGS is widely used owing to its significant prognostic value, its accuracy in predicting patient outcomes is limited. We have previously demonstrated that by rationally integrating KI and MI into a new metric called Ki67-Adjusted Mitotic Score (KAMS), which indicates proportion of mitotic cells amongst Ki67-positive cells, we could glean a new layer of prognostic information. We found that for NGII and III patients, high KAMS predicted relatively better overall survival (OS) than low KAMS. We therefore asked if a KAMS-based classifier would improve patient stratification to enable their funneling towards more optimal treatment choices. Ideal KAMS thresholds were established by selecting values which created the widest survival stratification that was statistically significant (Log-Rank test). For NGII and III patients, an above-threshold KAMS was deemed as low-risk and a below-threshold KAMS as high-risk for the purpose of grade adjustment. Thus adjusted NGI consisted of original NGI patients along with KAMS-determined low-risk original NGII patients. Adjusted NGII patients were composed of KAMS-determined high-risk patients originally in NGII along with KAMS-determined low-risk original NGIII patients. Finally, adjusted NGIII comprised exclusively of high-risk patients from original NGIII. The adjusted system represented a wider separation between OS curves with adjusted NGI, II and II OS being 95.48%, 87.62% and 78.18%, respectively. OS groups between adjusted Grade I and II, and I and III (p<0.001) showed statistical significance, indicating that KAMS can function as an effective risk-stratification biomarker.