| Contributors: |
Bertozzi, S.; Londero, A. P.; Vendramelli, G.; Orsaria, M.; Mariuzzi, L.; Pegolo, E.; Di Loreto, C.; Cedolini, C.; Della Mea, V. |
| Description: |
OBJECTIVE: This study aimed to identify risk factors associated with the development of metastases in breast cancer patients, to investigate survival rates, and the relationship between local recurrences and distant metastases. METHODS: This retrospective case-cohort study included women with breast cancer who were treated at a certified Breast Unit between 2001 and 2015. Cases who developed distant metastases were compared to controls based on diagnosis year, stage, and age at diagnosis. Comprehensive information on patient characteristics, tumor biology, and treatment options was gathered. RESULTS: The study included 412 patients who developed distant metastases and 433 controls who remained metastasis-free over a median follow-up of 150 months (interquartile range 87-202). The 20-year overall survival was 99.23% for the control group and 23.62% for those with metastasis (p < 0.01). Significant risk factors for metastasis included lobular invasive carcinoma (odds ratio (OR) 2.26, p < 0.001), triple-negative subtype (OR 4.06, p = 0.002), high tumor grade (OR 2.62, p = 0.004), larger tumor size (OR 1.02, p < 0.001), lymph node involvement (p < 0.001), and loco-regional recurrence (OR 4.32, p < 0.001). Progesterone receptor (PR) expression was protective (OR 0.52, 95% confidence interval 0.34-0.81, p = 0.003). Machine learning models supported these findings, though their clinical significance was limited. CONCLUSIONS: Lobular invasive carcinoma, specific tumor subtypes, high grade, large tumor size, lymph node involvement, and loco-regional recurrence are all significant risk factors for distant metastasis, whereas PR expression is protective. The potential of machine learning in predicting metastasis was explored, showing promise for future personalized risk assessment. |