A Scoring System That Predicts Difficult Lipoma Resection: Logistic Regression and Tenfold Cross-Validation Analysis

Area of Science:

• Surgical Oncology
• Dermatology
• Radiology

Background:

• Lipomas are common benign tumors, typically easy to remove surgically.
• However, some lipomas present surgical challenges due to their location or characteristics.
• Preoperative identification of difficult resections is crucial for surgical planning.

Purpose of the Study:

• To identify clinical and radiological risk factors for difficult lipoma resection.
• To develop a clinically useful scoring system for predicting preoperative surgical difficulty.

Main Methods:

• Retrospective analysis of 86 lipoma resection cases (2016-2018).
• Surgical difficulty defined by tissue separation challenges or inability to remove in one piece.
• Multivariate logistic regression and Receiver Operating Characteristic (ROC) analysis used to identify predictors and validate a scoring system.

Main Results:

• 36% of lipoma resections were classified as surgically difficult.
• Key predictors identified: subfascial intramuscular location, broad contact with structures, in-flowing vessels, and unclear boundaries.
• A 0-4 point scoring system demonstrated 82.4% accuracy, with scores >= 2 predicting difficulty (55% sensitivity, 98% specificity).

Conclusions:

• A novel scoring system effectively predicts lipoma resection difficulty.
• This tool can aid surgeons in preoperative preparation, potentially facilitating smoother surgical procedures.