Jiawen Deng, Myron Moskalyk, Matthew Shammas‐Toma, Ahmed Aoude, Michelle Ghert, Sahir Bhatnagar, Anthony Bozzo
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引用次数: 0
摘要
背景涉及下肢的肿瘤切除和重建通常会导致再次手术,从而影响患者的预后和医疗资源。本研究旨在开发一种机器学习(ML)模型来预测这种再手术风险。本研究按照 TRIPOD + AI 的方法进行,利用 PARITY 试验的数据开发 ML 模型,预测下肢肿瘤切除和重建术后 1 年的再手术风险。在五倍交叉验证的基础上,对六种 ML 算法进行了调整和校准。结果多项式支持向量机(SVM)模型被选为表现最佳的模型。在内部验证过程中,SVM 的 AUC-ROC 为 0.73,Brier 得分为 0.17。使用平衡混淆矩阵所有象限的最佳阈值,SVM 的灵敏度为 0.45,特异度为 0.81。使用高灵敏度阈值时,SVM 的灵敏度为 0.68,特异度为 0.68。总手术时间是预测再次手术风险的最重要特征。
Development of Machine Learning Models for Predicting the 1‐Year Risk of Reoperation After Lower Limb Oncological Resection and Endoprosthetic Reconstruction Based on Data From the PARITY Trial
BackgroundOncological resection and reconstruction involving the lower extremities commonly lead to reoperations that impact patient outcomes and healthcare resources. This study aimed to develop a machine learning (ML) model to predict this reoperation risk.MethodsThis study was conducted according to TRIPOD + AI. Data from the PARITY trial was used to develop ML models to predict the 1‐year reoperation risk following lower extremity oncological resection and reconstruction. Six ML algorithms were tuned and calibrated based on fivefold cross‐validation. The best‐performing model was identified using classification and calibration metrics.ResultsThe polynomial support vector machine (SVM) model was chosen as the best‐performing model. During internal validation, the SVM exhibited an AUC‐ROC of 0.73 and a Brier score of 0.17. Using an optimal threshold that balances all quadrants of the confusion matrix, the SVM exhibited a sensitivity of 0.45 and a specificity of 0.81. Using a high‐sensitivity threshold, the SVM exhibited a sensitivity of 0.68 and a specificity of 0.68. Total operative time was the most important feature for reoperation risk prediction.ConclusionThe models may facilitate reoperation risk stratification, allowing for better patient counseling and for physicians to implement measures that reduce surgical risks.
期刊介绍:
The Journal of Surgical Oncology offers peer-reviewed, original papers in the field of surgical oncology and broadly related surgical sciences, including reports on experimental and laboratory studies. As an international journal, the editors encourage participation from leading surgeons around the world. The JSO is the representative journal for the World Federation of Surgical Oncology Societies. Publishing 16 issues in 2 volumes each year, the journal accepts Research Articles, in-depth Reviews of timely interest, Letters to the Editor, and invited Editorials. Guest Editors from the JSO Editorial Board oversee multiple special Seminars issues each year. These Seminars include multifaceted Reviews on a particular topic or current issue in surgical oncology, which are invited from experts in the field.