Department of Internal Medicine and Liver Research Institute, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.
Department of Internal Medicine, Dongguk University College of Medicine, Dongguk University Ilsan Hospital, Goyang-si, Korea.
Sci Rep. 2024 Oct 25;14(1):25359. doi: 10.1038/s41598-024-75977-y.
This study aimed to develop a machine learning (ML) model for predicting pulmonary embolism (PE) in patients with gastrointestinal cancers, a group at increased risk for PE. We conducted a retrospective, multicenter study analyzing patients who underwent computed tomographic pulmonary angiography (CTPA) between 2010 and 2020. The study utilized demographic and clinical data, including the Wells score and D-dimer levels, to train a random forest ML model. The model's effectiveness was assessed using the area under the receiver operating curve (AUROC). In total, 446 patients from hospital A and 139 from hospital B were included. The training set consisted of 356 patients from hospital A, with internal validation on 90 and external validation on 139 patients from hospital B. The model achieved an AUROC of 0.736 in hospital A and 0.669 in hospital B. The ML model significantly reduced the number of patients recommended for CTPA compared to the conventional diagnostic strategy (hospital A; 100.0% vs. 91.1%, P < 0.001, hospital B; 100.0% vs. 93.5%, P = 0.003). The results indicate that an ML-based prediction model can reduce unnecessary CTPA procedures in gastrointestinal cancer patients, highlighting its potential to enhance diagnostic efficiency and reduce patient burden.
本研究旨在开发一种机器学习 (ML) 模型，用于预测胃肠道癌患者的肺栓塞 (PE)，这些患者发生 PE 的风险增加。我们进行了一项回顾性、多中心研究，分析了 2010 年至 2020 年间接受计算机断层肺动脉造影 (CTPA) 的患者。该研究利用人口统计学和临床数据，包括 Wells 评分和 D-二聚体水平，训练随机森林 ML 模型。使用接收者操作特征曲线下的面积 (AUROC) 评估模型的有效性。共有来自医院 A 的 446 名患者和来自医院 B 的 139 名患者纳入研究。训练集包括来自医院 A 的 356 名患者，对 90 名患者进行内部验证，对来自医院 B 的 139 名患者进行外部验证。该模型在医院 A 中的 AUROC 为 0.736，在医院 B 中的 AUROC 为 0.669。与传统诊断策略相比，ML 模型显著减少了推荐进行 CTPA 的患者数量（医院 A：100.0% 比 91.1%，P<0.001；医院 B：100.0% 比 93.5%，P=0.003）。结果表明，基于 ML 的预测模型可以减少胃肠道癌患者不必要的 CTPA 程序，突出了其提高诊断效率和减轻患者负担的潜力。
