Huang Yue, Zhang Han, Ding Qingzhu, Chen Dehua, Zhang Xiang, Weng Shangeng, Liu Guozhong
Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital, Fujian Medical University, Fuzhou, Fujian, China.
Fujian Abdominal Surgery Research Institute, The First Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Front Oncol. 2024 Nov 13;14:1419297. doi: 10.3389/fonc.2024.1419297. eCollection 2024.
The aim of this study was to evaluate the prognostic potential of combining clinical features and radiomics with multiple machine learning (ML) algorithms in pancreatic ductal adenocarcinoma (PDAC).
A total of 116 patients with PDAC who met the eligibility criteria were randomly assigned to a training or validation cohort. Seven ML algorithms, including Supervised Principal Components, stepwise Cox, Random Survival Forest, CoxBoost, Least absolute shrinkage and selection operation (Lasso), Ridge, and Elastic network, were integrated into 43 algorithm combinations. Forty-three radiomics models were constructed separately using radiomics features extracted from arterial phase (AP), venous phase (VP), and combined arterial and venous phase (AP+VP) images. The concordance index (C-index) of each model was calculated. The model with the highest mean C-index was identified as the best model for calculating the radiomics score (Radscore). Univariate and multivariate Cox analyses were used to identify independent prognostic indicators and create a clinical model for prognosis prediction. The multivariable Cox regression was used to combine Radscore with clinical features to create a combined model. The efficacy of the model was evaluated using the C-index, calibration curves, and decision curve analysis (DCA).
The model based on the Lasso+StepCox[both] algorithm constructed using AP+VP radiomics features showed the best predictive ability among the 114 radiomics models. The C-indices of the model in the training and validation cohorts were 0.742 and 0.722, respectively. Based on the results of the univariate and multivariate Cox regression analyses, sex, Tumor-Node-Metastasis (TNM) stage, and systemic inflammation response index were included to build the clinical model. The combined model, incorporating three clinical factors and AP+VP-Radscore, achieved the highest C-indices of 0.764 and 0.746 in the training and validation cohorts, respectively. In terms of preoperative prognosis prediction for PDAC, the calibration curve and DCA showed that the combined model had a good consistency and greatest net benefit.
A combined model of clinical features and AP+VP-Radscore screened using multiple ML algorithms has an excellent ability to predict the prognosis of PDAC and may provide a noninvasive and effective method for clinical decision-making.
本研究旨在评估将临床特征和放射组学与多种机器学习（ML）算法相结合在胰腺导管腺癌（PDAC）中的预后预测潜力。
共有116例符合纳入标准的PDAC患者被随机分配到训练队列或验证队列。七种ML算法，包括监督主成分分析、逐步Cox分析、随机生存森林、CoxBoost、最小绝对收缩和选择算子（Lasso）、岭回归和弹性网络，被整合到43种算法组合中。分别使用从动脉期（AP）、静脉期（VP）以及动脉期和静脉期联合（AP+VP）图像中提取的放射组学特征构建43个放射组学模型。计算每个模型的一致性指数（C指数）。将平均C指数最高的模型确定为计算放射组学评分（Radscore）的最佳模型。采用单因素和多因素Cox分析确定独立预后指标并创建用于预后预测的临床模型。使用多变量Cox回归将Radscore与临床特征相结合以创建联合模型。使用C指数、校准曲线和决策曲线分析（DCA）评估模型的效能。
在114个放射组学模型中，基于使用AP+VP放射组学特征构建的Lasso+StepCox[两者]算法的模型显示出最佳预测能力。该模型在训练队列和验证队列中的C指数分别为0.742和0.722。基于单因素和多因素Cox回归分析结果，纳入性别、肿瘤-淋巴结-转移（TNM）分期和全身炎症反应指数以构建临床模型。结合三个临床因素和AP+VP-Radscore的联合模型在训练队列和验证队列中分别达到了最高的C指数0.764和0.746。在PDAC术前预后预测方面，校准曲线和DCA表明联合模型具有良好的一致性和最大净效益。
使用多种ML算法筛选的临床特征与AP+VP-Radscore的联合模型具有出色的预测PDAC预后的能力，可能为临床决策提供一种无创且有效的方法。
