Development and validation of a prognostic model for stage IV breast cancer based on primary tumor resection with machine learning methods: retrospective cohort study

Category	Detail
Condition	Stage IV Breast Cancer
Key Mechanisms	Primary Tumor Resection (PTR) and machine learning for survival prediction
Target Population	Female patients diagnosed with stage IV breast cancer
Care Setting	Clinical evaluation and prognostic assessment

PTR is associated with improved overall survival (OS) and cancer-specific survival (CSS) in stage IV breast cancer patients.
The Support Vector Machine (SVM) model demonstrated high accuracy in predicting survival outcomes.
Patients with tumors ≤5 cm, N2 status, or HER2 overexpression showed better survival after PTR.
Trimodality therapy combining PTR, chemotherapy, and radiotherapy yielded the best survival outcomes.
A user-friendly web application was developed for clinical use of the prognostic model.

Consider PTR for select patients with stage IV breast cancer based on tumor size, nodal status, and HER2 status.

Employ machine learning models to predict survival outcomes and assist in clinical decision-making.

Assess potential risks associated with PTR in the context of metastatic disease.

Female patients diagnosed with stage IV breast cancer from 2005 to 2015.

PTR may confer survival benefits in specific subgroups of patients.

Utilize machine learning models to identify candidates for PTR.
Incorporate clinical indicators such as tumor size and HER2 status in treatment planning.
Implement a multidisciplinary approach involving surgery, chemotherapy, and radiotherapy.

Clinical Scorecard: Creation and assessment of a prognostic model for stage IV breast cancer utilizing primary tumor resection and machine learning techniques: a retrospective cohort analysis