Abstract PS3-06-04: Spatial representation of deep-learning markers show additional prognostic value in breast cancer patients

Abstract Introduction: Intra-tumour heterogeneity has been hypothesised to increase risk of recurrence in breast cancer patients but has not been studied systematically. Deep-learning enables systematic extraction of prognostic information from H 80% of centre tiles DeepGrade-high). In the subgroup of luminal patients (2268 patients), those who had a cluster of DeepGrade-high tiles in the tumour front area had higher recurrence propability with a multivariate hazard ratio of 1.97 (CI: 1.19-3.25; p-value=0.008); and within the subgroup of luminal patients with DeepGrade-low status (1356 patients), having a cluster in the tumour front had a univariate hazard ratio of 3.20, and a multivariate hazard ratio of 1.98 (CI: 1.13-3.49, p-value=0.017) when controlling for age, tumour size, lymph node status, and grade. Conclusions: The presence of at least one cluster of high-risk tiles within the tumour front was found to be an independent prognostic factor. More generally, the spatial distribution of high-risk tumour areas in a histopathology slides can have prognostic implications and should be characterised with greater detail in the future. Citation Format: C. Boissin, J. Hartman, M. Rantalainen. Spatial representation of deep-learning markers show additional prognostic value in breast cancer patients abstract. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS3-06-04.