Meng Yinghao, Zhang Hao, Li Qi, Liu Fang, Fang Xu, Li Jing, Yu Jieyu, Feng Xiaochen, Zhu Mengmeng, Li Na, Jing Guodong, Wang Li, Ma Chao, Lu Jianping, Bian Yun, Shao Chengwei
Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.
Department of Radiology, No.971 Hospital of Navy, Qingdao, Shandong, China.
Front Oncol. 2021 Nov 8;11:707288. doi: 10.3389/fonc.2021.707288. eCollection 2021.
To develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor-stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).
In this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.
We observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.
The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.
开发并验证一种基于多排螺旋计算机断层扫描（MDCT）的机器学习分类器，用于术前预测胰腺导管腺癌（PDAC）患者的肿瘤-基质比（TSR）表达。
在这项回顾性研究中，227例PDAC患者接受了MDCT扫描和手术切除。我们通过苏木精和伊红染色对TSR进行定量，并分别为每位患者提取1409个动脉期和门静脉期的影像组学特征。此外，我们使用最小绝对收缩和选择算子逻辑回归算法来减少特征。使用由2016年12月至2017年12月期间收治的167例连续患者组成的训练集开发极端梯度提升（XGBoost）模型。该模型在2018年1月至2018年4月期间收治的60例连续患者中进行验证。我们根据其判别能力、校准和临床实用性来确定XGBoost分类器的性能。
我们观察到91例（40.09%）患者的TSR较低，136例（59.91%）患者的TSR较高。对数秩检验显示，TSR低分组患者的生存期明显长于TSR高分组患者。预测模型在训练集（曲线下面积[AUC]=0.93）中显示出良好的判别能力，在验证集（AUC=0.63）中显示出中等的判别能力。训练集的灵敏度、特异度、准确度、阳性预测值和阴性预测值分别为94.06%、81.82%、0.89、0.89和0.90，而验证集的相应值分别为85.71%、48.00%、0.70、0.70和0.71。
基于CT影像组学的XGBoost分类器为预测PDAC患者的TSR和优化风险分层提供了一种潜在有价值的非侵入性工具。
