未经证实:结直肠癌(CRC)是一组异质性的恶性肿瘤,具有不同的临床特征。这些特征与静脉血栓栓塞症(VTE)的关联尚待阐明。机器学习(ML)模型非常适合改善CRC中的VTE预测,因为它们能够接收大量特征的特征并理解数据集以获得隐含的相关性。
UNASSIGNED:从2019年8月至2022年8月的4,914例结直肠癌患者中提取了数据,并纳入了1,191例原发肿瘤部位接受手术治疗的患者。分析的变量包括患者水平因素,癌症水平的因素,和实验室测试结果。使用十倍交叉验证方法对数据集的30%进行模型训练,并使用总数据集进行模型验证。主要结果是术后30天发生VTE。六种ML算法,包括逻辑回归(LR),随机森林(RF),极端梯度提升(XGBoost),加权支持向量机(SVM),多层感知(MLP)网络,和长短期记忆(LSTM)网络,用于模型拟合。模型评价基于六个指标,包括受试者工作特征曲线-曲线下面积(ROC-AUC),灵敏度(SEN),特异性(SPE),阳性预测值(PPV),负预测值(NPV),和Brier得分.使用两个先前的VTE模型(Caprini和Khorana)作为基准。
UNASSIGNED:术后VTE发生率为10.8%。前十大重要预测因素包括淋巴结转移,C反应蛋白,肿瘤分级,贫血,原发肿瘤位置,性别,年龄,D-二聚体水平,凝血酶时间,和肿瘤分期。在我们的结果中,XGBoost模型表现出最好的性能,ROC-AUC为0.990,SEN为96.9%,训练数据集中的SPE为96.1%,ROC-AUC为0.908,SEN为77.5%,验证数据集中的SPE为93.7%。所有ML模型都优于以前开发的模型(Caprini和Khorana)。
UNASSIGNED:本研究使用六种ML算法开发了术后VTE预测模型。XGBoostVTE模型可能为临床VTE预防决策提供补充工具,所提出的风险因素可以为CRC患者的VTE风险分层提供一些启示。
UNASSIGNED: Colorectal cancer (CRC) is a heterogeneous group of malignancies distinguished by distinct clinical features. The association of these features with venous thromboembolism (VTE) is yet to be clarified. Machine learning (ML) models are well suited to improve VTE prediction in CRC due to their ability to receive the characteristics of a large number of features and understand the dataset to obtain implicit correlations.
UNASSIGNED: Data were extracted from 4,914 patients with colorectal cancer between August 2019 and August 2022, and 1,191 patients who underwent surgery on the primary tumor site with curative intent were included. The variables analyzed included patient-level factors, cancer-level factors, and laboratory test results. Model training was conducted on 30% of the dataset using a ten-fold cross-validation method and model validation was performed using the total dataset. The primary outcome was VTE occurrence in postoperative 30 days. Six ML algorithms, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), weighted support vector machine (SVM), a multilayer perception (MLP) network, and a long short-term memory (LSTM) network, were applied for model fitting. The model evaluation was based on six indicators, including receiver operating characteristic curve-area under the curve (ROC-AUC), sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and Brier score. Two previous VTE models (Caprini and Khorana) were used as the benchmarks.
UNASSIGNED: The incidence of postoperative VTE was 10.8%. The top ten significant predictors included lymph node metastasis, C-reactive protein, tumor grade, anemia, primary tumor location, sex, age, D-dimer level, thrombin time, and tumor stage. In our results, the XGBoost model showed the best performance, with a ROC-AUC of 0.990, a SEN of 96.9%, a SPE of 96.1% in training dataset and a ROC-AUC of 0.908, a SEN of 77.5%, a SPE of 93.7% in validation dataset. All ML models outperformed the previously developed models (Caprini and Khorana).
UNASSIGNED: This
study developed postoperative VTE predictive models using six ML algorithms. The XGBoost VTE model might supply a complementary tool for clinical VTE prophylaxis decision-making and the proposed risk factors could shed some light on VTE risk stratification in CRC patients.