Accurate assessment of axillary lymph nodes in patients with breast cancer is essential for prognosis and treatment planning. Staging and surgical management have evolved from axillary lymph node dissection to sentinel lymph node biopsy to minimize morbidity. However, sentinel lymph node biopsy has non-negligible morbidity, and more than 70% of biopsies are negative, calling into question its routine use. Magnetic resonance imaging (MRI) can be used to detect and stage lymph node metastases in situ, but its sensitivity and specificity are moderate to poor. Few studies have employed artificial intelligence to detect lymph node metastases on MRI images, and none have used an integrative multidata approach (IMA), defined as modeling the combination of clinical and laboratory data with multiparametric MRI.

The primary objective of this retrospective observational study is to improve the accuracy of detecting lymph node involvement in breast cancer using IMA. The secondary objective is to allow longitudinal monitoring of the effects of neoadjuvant therapy on lymph node involvement

Inclusion Criteria:

• Patients diagnosed with breast cancer who underwent MRI and biopsy or surgery of axillary lymph nodes as part of their diagnosis and treatment
• Patients of all genders, ages, and stage I-III of breast cancer
• Patients who underwent neoadjuvant therapy and have longitudinal imaging data available for analysis (for secondary outcome analysis)

Exclusion Criteria:

• Patients whose MRI images were of insufficient quality for analysis
• Patients who had a previous history of breast cancer
• Patients with a history of axillary surgery or lymph node dissection prior to the current diagnosis of breast cancer
• Patients who received neoadjuvant therapy at another institution

The study population will consist of patients with breast cancer who underwent MRI and biopsy or surgery of axillary lymph nodes as part of their diagnosis and treatment at the involved centers.

Patients of all genders, ages, and stages I-III of breast cancer will be included in the study. Patients with incomplete data or whose MRI images were of insufficient quality for analysis will be excluded.