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A machine learning-based predictive model for complication risks in vacuum-assisted breast biopsy.

Sun Pingdong1,2,3, Shao Xinran1, Shen Yunzhi2

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Machine learning accurately predicts bruising after vacuum-assisted breast biopsy (VABB), improving patient care. This tool helps surgeons anticipate complications, enhancing preoperative planning and patient counseling for better outcomes.

Keywords:
breast tumormachine learningpostoperative complicationsprediction modelvacuum-assisted breast biopsy

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Area of Science:

  • Medical imaging and interventional radiology
  • Machine learning in healthcare
  • Breast oncology and diagnostics

Background:

  • Ultrasound-guided vacuum-assisted breast biopsy (VABB) is standard for benign breast lesions.
  • Postoperative complications like bruising and residual tumors remain clinical challenges.
  • Current risk assessment for VABB complications lacks precision.

Purpose of the Study:

  • Develop and validate machine learning models to predict VABB complications.
  • Improve precision in risk assessment for postoperative adverse events.
  • Enhance preoperative planning and patient counseling for VABB procedures.

Main Methods:

  • Multicenter retrospective study of 1,064 VABB procedures (2017-2025).
  • Developed six machine learning models using 12 preoperative variables.
  • Random forest algorithm showed superior performance in cross-validation and external validation.

Main Results:

  • Machine learning model achieved high accuracy in predicting bruising (AUC 0.971, 96.7%) and operative duration.
  • Key predictors identified: tumor size, blood flow grade, and distance to pectoralis muscle.
  • Model demonstrated generalizability with strong external validation (AUC 0.945).

Conclusions:

  • A clinically validated machine learning tool accurately predicts common VABB complications, especially bruising.
  • Incorporating tumor and anatomical data aids in mitigating adverse outcomes.
  • The model can enhance surgical decision-making and patient recovery expectations.