AI Generated Stromal Biomarkers for DCIS Reccurence Prediction

BackgroundAlthough DCIS has a relatively low recurrence rate, many patients still receive adjuvant radiotherapy or endocrine therapy, raising concerns about overtreatment. Reliable biomarkers are therefore needed to predict an individual patients risk and guide treatment decisions. Recent studies suggest that the composition of the tumour-associated stroma (TAS) affects progression and outcome, highlighting TAS-derived biomarkers as promising candidates for further investigation. MethodsWe trained AI models for cell and tumour segmentation using whole slide digital pathology images acquired as part of a retrospective cohort study. We investigated the effects of cell density within both the tumour and the TAS to determine how they correlated with recurrence in the ipsilateral breast. ResultsWe found that the concentration of DCIS lesions on the slide and the density of mitotic figures inside the TAS region were significantly associated with recurrence risk. Additionally, we found some predictive value in the lymphocyte and red blood cell densities in different tumour regions. Stromal composition was shown to associate with recurrence risk, and density-based biomarkers were identified and used to cluster patients into phenotypes with significantly different risk profiles. ConclusionOur findings highlight the prognostic relevance of stromal composition in DCIS, and we identify novel density-based biomarkers that can be used to identify patients who are more likely to experience a local recurrence after breast-conserving surgery alone. These results may aid in developing future risk-stratification tools for breast cancer patients, thereby reducing overtreatment and improving patient care.