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&E histopathology whole slide images (WSI), however, local tile level features are typically aggregated without spatial context. Research objective: To investigate the spatial distribution of an AI-based prognostic marker (DeepGrade) within tumour areas to identify patterns of spatial heterogeneity . Materials and Methods: 3325 WSIs from resected breast tumours of patients diagnosed in two hospitals in Sweden were used to predict DeepGrade status on a tile-level. Tumour regions were segmented and divided spatially into a tumour front and a tumour centre. For each tumour region, the total number of tiles, and the proportion of high-risk tiles were calculated. Clusters of high-risk tiles (regions of 25 neighboring tiles) were defined in the tumour front as a representation for tumour aggressiveness. Analyses were performed by tumour subtypes (Luminal A, Luminal B, Her2-enriched and Basal-like). Cox Proportional Hazards analyses was used to evaluate prognostic performance with progression free survial as primary endpoint. Results: The proportion of DeepGrade-high tiles in the centre area were higher in Her2+ and basal-like patients than luminal patients, (49.6% vs 12.8% had > 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.