Department of Plastic & Reconstructive Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Ann Surg. 2023 Jul 1;278(1):e123-e130. doi: 10.1097/SLA.0000000000005386. Epub 2022 Jan 21.
To develop, validate, and evaluate ML algorithms for predicting MSFN.
MSFN is a devastating complication that causes significant distress to patients and physicians by prolonging recovery time, compromising surgical outcomes, and delaying adjuvant therapy.
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.
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.
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.
开发、验证和评估用于预测 MSFN 的机器学习算法。
MSFN 是一种破坏性的并发症，通过延长恢复时间、影响手术结果和延迟辅助治疗，给患者和医生带来了极大的痛苦。
我们对 2018 年 1 月至 2019 年 12 月期间所有接受乳房切除术和即刻基于植入物的重建的连续患者进行了全面回顾。开发了 9 种监督机器学习算法来预测 MSFN。患者数据被分为训练集（80%）和测试集（20%）。
我们确定了 481 名患者中的 694 例乳房切除术和即刻基于植入物的重建。患者的平均年龄为 50 ± 11.5 岁，平均 BMI 为 26.7 ± 4.8kg/m 2 ，中位随访时间为 16.1（范围为 11.9-23.2）个月。6%（n=40）的患者发生了 MSFN。随机森林模型表现出最佳的区分性能（曲线下面积为 0.70），达到了 89%的平均准确率（95%置信区间为 83-94），并确定了 10 个 MSFN 的预测因子。决策曲线分析表明，无论概率阈值如何，机器学习模型都具有更高的净收益。较高的 BMI、年龄较大、高血压、胸肌下器械放置、保留乳头的乳房切除术、腋窝淋巴结清扫术和不使用脱细胞真皮基质均与更高的 MSFN 风险独立相关。
基于易于获得的围手术期临床数据训练的机器学习算法可以准确预测 MSFN 的发生，并有助于个体化患者咨询、术前优化和手术规划，以降低这种破坏性并发症的风险。
