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
Jiawen Deng, Myron Moskalyk, Matthew Shammas‐Toma, Ahmed Aoude, Michelle Ghert, Sahir Bhatnagar, Anthony Bozzo
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引用次数: 0
Abstract
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.