An intraoperative nomogram for predicting secondary margin positivity in breast conserving surgery utilizing frozen...

Area of Science:

• Oncology
• Surgical Pathology
• Biostatistics

Background:

• Breast conserving surgery (BCS) is a standard breast cancer treatment.
• Intraoperative frozen section analysis (FSA) is crucial for margin assessment in BCS.
• FSA-assisted excisional biopsy is common in developing countries, necessitating accurate margin assessment.

Purpose of the Study:

• To develop a predictive model for secondary margin positivity in BCS with FSA-assisted excisional biopsy.
• To identify patients at high risk for positive margins requiring re-excision.
• To reduce surgical complications and healthcare costs by optimizing re-excision decisions.

Main Methods:

• Patient data from 348 cases were divided into training (256) and testing (92) sets.
• Least Absolute Shrinkage and Selection Operator (LASSO) regression identified key variables for model construction.
• Model performance was assessed using ROC curves, calibration plots, and Decision Curve Analyses (DCAs).

Main Results:

• A predictive model with a nomogram was developed using six significant variables identified by LASSO.
• The model demonstrated strong discrimination (AUC 0.79 training, 0.83 testing) and good calibration.
• An optimal threshold (97 points) achieved high sensitivity and specificity in both training and testing sets.

Conclusions:

• A novel predictive model for secondary margin positivity in BCS with FSA-assisted excisional biopsy was successfully developed.
• The model exhibits excellent discriminative ability, calibration, and clinical utility.
• This tool can aid surgeons in decision-making regarding re-excision, potentially improving patient outcomes and reducing costs.