Development and Assessment of Machine Learning Models for Individualized Risk Assessment of Mastectomy Skin Flap
Abbas M Hassan1, Andrea P Biaggi, Malke Asaad
1Department of Plastic & Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Objective:
To develop, validate, and evaluate ML algorithms for predicting MSFN.
Background:
MSFN is a devastating complication that causes significant distress to patients and physicians by prolonging recovery time, compromising surgical outcomes, and delaying adjuvant therapy.
Methods:
We conducted comprehensive review of all consecutive patients who underwent mastectomy and immediate implant-based reconstruction from January 2018 to December 2019. Nine supervised ML algorithms were developed to predict MSFN. Patient data were partitioned into training (80%) and testing (20%) sets.
Results:
We identified 694 mastectomies with immediate implant-based reconstruction in 481 patients. The patients had a mean age of 50 ± 11.5 years, years, a mean body mass index of 26.7 ± 4.8 kg/m 2 , and a median follow-up time of 16.1 (range, 11.9-23.2) months. MSFN developed in 6% (n = 40) of patients. The random forest model demonstrated the best discriminatory performance (area under curve, 0.70), achieved a mean accuracy of 89% (95% confidence interval, 83-94), and identified 10 predictors of MSFN. Decision curve analysis demonstrated that ML models have a superior net benefit regardless of the probability threshold. Higher body mass index, older age, hypertension, subpectoral device placement, nipple-sparing mastectomy, axillary nodal dissection, and no acellular dermal matrix use were all independently associated with a higher risk of MSFN.
Conclusions:
ML algorithms trained on readily available perioperative clinical data can accurately predict the occurrence of MSFN and aid in individualized patient counseling, preoperative optimization, and surgical planning to reduce the risk of this devastating complication.
Insights
Machine learning algorithms accurately predict mastectomy skin flap necrosis (MSFN) using perioperative data. This aids in personalized patient care and surgical planning to reduce complication risks.
Area of Science:
- Plastic Surgery
- Machine Learning in Medicine
- Oncology
Background:
- Mastectomy skin flap necrosis (MSFN) is a severe complication impacting patient recovery and surgical outcomes.
- Predicting and mitigating MSFN is crucial for improving patient quality of life and treatment efficacy.
Approach:
- Developed and validated nine supervised machine learning (ML) algorithms to predict MSFN.
- Utilized a dataset of 694 patients undergoing mastectomy with immediate implant-based reconstruction.
- Partitioned data into 80% training and 20% testing sets for robust model evaluation.
Key Points:
- The random forest ML model achieved the highest predictive performance (AUC 0.70, accuracy 89%).
- Identified key predictors of MSFN including higher BMI, older age, hypertension, subpectoral device placement, nipple-sparing mastectomy, axillary nodal dissection, and lack of acellular dermal matrix.
- ML models demonstrated superior net benefit in decision curve analysis.
Conclusions:
- ML algorithms can accurately predict MSFN using accessible perioperative clinical data.
- These predictive models can enhance patient counseling, preoperative optimization, and surgical planning.
- Implementing ML tools can help reduce the incidence of MSFN.
