评估深度神经网络在胰十二指肠切除术患者术后并发症预测中的价值。
Assessing the value of deep neural networks for postoperative complication prediction in pancreaticoduodenectomy patients.
作者信息
Bonde Mikkel, Bonde Alexander, Kaafarani Haytham, Millarch Andreas, Sillesen Martin
机构信息
Dep. of Organ Surgery and Transplantation, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark.
Div. of Trauma, Emergency Surgery and Surgical Critical Care, Massachusetts General Hospital/Harvard Medical School, Boston, Massachusetts, Unites States of America.
出版信息
PLoS One. 2024 Dec 30;19(12):e0316402. doi: 10.1371/journal.pone.0316402. eCollection 2024.
INTRODUCTION
Pancreaticoduodenectomy (PD) for patients with pancreatic ductal adenocarcinoma (PDAC) is associated with a high risk of postoperative complications (PoCs) and risk prediction of these is therefore critical for optimal treatment planning. We hypothesize that novel deep learning network approaches through transfer learning may be superior to legacy approaches for PoC risk prediction in the PDAC surgical setting.
METHODS
Data from the US National Surgical Quality Improvement Program (NSQIP) 2002-2018 were used, with a total of 5,881,881 million patients, including 31,728 PD patients. Modelling approaches comprised of a model trained on a general surgery patient cohort and then tested on a PD specific cohort (general model), a transfer learning model trained on the general surgery patients with subsequent transfer and retraining on a PD-specific patient cohort (transfer learning model), a model trained and tested exclusively on the PD-specific patient cohort (direct model), and a benchmark random forest model trained on the PD patient cohort (RF model). The models were subsequently compared against the American College of Surgeons (ACS) surgical risk calculator (SRC) in terms of predicting mortality and morbidity risk.
RESULTS
Both the general model and transfer learning model outperformed the RF model in 14 and 16 out of 19 prediction tasks, respectively. Additionally, both models outperformed the direct model on 17 out of the 19 tasks. The transfer learning model also outperformed the general model on 11 out of the 19 prediction tasks. The transfer learning model outperformed the ACS-SRC regarding mortality and all the models outperformed the ACS-SRC regarding the morbidity prediction with the general model achieving the highest Receiver Operator Area Under the Curve (ROC-AUC) of 0.668 compared to the 0.524 of the ACS SRC.
CONCLUSION
DNNs deployed using a transfer learning approach may be of value for PoC risk prediction in the PD setting.
简介
胰十二指肠切除术（PD）用于治疗胰腺导管腺癌（PDAC）患者时，术后并发症（PoC）风险较高，因此对这些风险进行预测对于优化治疗方案至关重要。我们假设，通过迁移学习的新型深度学习网络方法在PDAC手术环境中进行PoC风险预测可能优于传统方法。
方法
使用了来自美国国家外科质量改进计划（NSQIP）2002 - 2018年的数据，共有5881881名患者，其中包括31728名PD患者。建模方法包括：一个在普通外科患者队列上训练然后在PD特定队列上测试的模型（通用模型）；一个在普通外科患者上训练，随后在PD特定患者队列上进行迁移和再训练的迁移学习模型；一个仅在PD特定患者队列上训练和测试的模型（直接模型）；以及一个在PD患者队列上训练的基准随机森林模型（RF模型）。随后，在预测死亡率和发病率风险方面，将这些模型与美国外科医师学会（ACS）手术风险计算器（SRC）进行比较。
结果
在19项预测任务中，通用模型和迁移学习模型分别在14项和16项任务中表现优于RF模型。此外，在19项任务中的17项中，这两个模型均优于直接模型。在19项预测任务中的11项中，迁移学习模型也优于通用模型。在死亡率方面，迁移学习模型优于ACS - SRC，在发病率预测方面，所有模型均优于ACS - SRC，通用模型的受试者操作特征曲线下面积（ROC - AUC）最高，为0.668，而ACS SRC为0.524。
结论
采用迁移学习方法部署的深度神经网络在PD环境中进行PoC风险预测可能具有价值。
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