Machine learning prediction of breast cancer local recurrence localization, and distant metastasis after local recurrences.

ABSTRACT: Local recurrences (LR) can occur within residual breast tissue, chest wall, skin, or newly formed scar tissue. Artificial intelligence (AI) technologies can extract a wide range of tumor features from large datasets helping in oncological decision-making. Recently, machine learning (ML) models have been developed to predict breast cancer recurrence or distant metastasis (DM). However, there is still a lack of models that consider the localization of LR as a tumor feature. To address this gap, here, we analysed data from 154 patients including pathological, clinical, and follow-up data (with an average follow-up of 133.16 months) on both primary tumors (PT) and recurrences. By using ML methods we predicted the localization of LR and the occurrence of DM after LR. The performance (ROC AUC) o

SUBMITTER: Kovacs KA 

PROVIDER: S-EPMC11811162 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature