Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, 200434, China; Department of Radiology, No. 971 Hospital of Navy, 266071, Qingdao, Shandong.
Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, 200434, China.
Acad Radiol. 2022 Apr;29(4):523-535. doi: 10.1016/j.acra.2021.08.013. Epub 2021 Sep 22.
To develop and validate a magnetic resonance imaging (MRI)-based machine learning classifier for evaluating the tumor-stroma ratio (TSR) in patients with pancreatic ductal adenocarcinoma (PDAC).
In this retrospective study, 148 patients with PDAC underwent an MR scan and surgical resection. We used hematoxylin and eosin to quantify the TSR. For each patient, we extracted 1,409 radiomics features and reduced them using the least absolute shrinkage and selection operator logistic regression algorithm. The extreme gradient boosting (XGBoost) classifier was developed using a training set comprising 110 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 38 consecutive patients, admitted between January 2018 and April 2018. We determined the performance of the XGBoost classifier based on its discriminative ability, calibration, and clinical utility.
A log-rank test revealed significantly longer survival in the TSR-low group. The prediction model displayed good discrimination in the training (area under the curve [AUC], 0.82) and validation set (AUC, 0.78). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 77.14%, 75.00%, 0.76%, 0.84%, and 0.65%, respectively, those for the validation set were 58.33%, 92.86%, 0.71%, 0.93%, and 0.57%, respectively.
We developed an XGBoost classifier based on MRI radiomics features, a non-invasive prediction tool that can evaluate the TSR of patients with PDAC. Moreover, it will provide a basis for interstitial targeted therapy selection and monitoring.
开发并验证一种基于磁共振成像（MRI）的机器学习分类器，用于评估胰腺导管腺癌（PDAC）患者的肿瘤基质比（TSR）。
在这项回顾性研究中，148 名 PDAC 患者接受了 MRI 扫描和手术切除。我们使用苏木精和伊红对 TSR 进行定量。对于每位患者，我们提取了 1409 个放射组学特征，并使用最小绝对值收缩和选择算子逻辑回归算法对其进行了简化。使用包含 2016 年 12 月至 2017 年 12 月期间连续 110 名患者的训练集开发了极端梯度提升（XGBoost）分类器。该模型在 2018 年 1 月至 4 月期间连续 38 名患者中进行了验证。我们基于判别能力、校准和临床实用性来确定 XGBoost 分类器的性能。
对数秩检验显示 TSR 低组的生存时间明显更长。预测模型在训练集（曲线下面积 [AUC]，0.82）和验证集（AUC，0.78）中均具有良好的判别能力。训练集的敏感性、特异性、准确性、阳性预测值和阴性预测值分别为 77.14%、75.00%、0.76%、0.84%和 0.65%，验证集分别为 58.33%、92.86%、0.71%、0.93%和 0.57%。
我们开发了一种基于 MRI 放射组学特征的 XGBoost 分类器，这是一种非侵入性预测工具，可用于评估 PDAC 患者的 TSR。此外，它将为间质靶向治疗的选择和监测提供依据。
