Department of Gynecology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China.
Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, PR China.
Br J Radiol. 2022 Jan 1;95(1129):20210838. doi: 10.1259/bjr.20210838. Epub 2021 Nov 29.
To investigate the prognostic role of magnetic resonance imaging (MRI)-based radiomics signature and clinical characteristics for overall survival (OS) and disease-free survival (DFS) in the early-stage cervical cancer.
A total of 207 cervical cancer patients (training cohort: = 144; validation cohort: = 63) were enrolled. 792 radiomics features were extracted from T2W and diffusion-weighted imaging (DWI). 19 clinicopathological parameters were collected from the electronic medical record system. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to select significant features to construct prognostic model for OS and DFS. Kaplan-Meier (KM) analysis and log-rank test were applied to identify the association between the radiomics score (Rad-score) and survival time. Nomogram discrimination and calibration were evaluated as well. Associations between radiomics features and clinical parameters were investigated by heatmaps.
A radiomics signature derived from joint T2W and DWI images showed better prognostic performance than that from either T2W or DWI image alone. Higher Rad-score was associated with worse OS ( < 0.05) and DFS ( < 0.05) in the training and validation set. The joint models outperformed both radiomics model and clinicopathological model alone for 3-year OS and DFS estimation. The calibration curves reached an agreement. Heatmap analysis demonstrated significant associations between radiomics features and clinical characteristics.
The MRI-based radiomics nomogram showed a good performance on survival prediction for the OS and DFS in the early-stage cervical cancer. The prediction of the prognostic models could be improved by combining with clinical characteristics, suggesting its potential for clinical application.
This is the first study to build the radiomics-derived models based on T2W and DWI images for the prediction of survival outcomes on the early-stage cervical cancer patients, and further construct a combined risk scoring system incorporating the clinical features.
探讨基于磁共振成像（MRI）的放射组学特征与临床特征对早期宫颈癌总生存期（OS）和无病生存期（DFS）的预后价值。
共纳入 207 例宫颈癌患者（训练队列 n = 144；验证队列 n = 63）。从 T2W 和弥散加权成像（DWI）中提取 792 个放射组学特征。从电子病历系统中收集 19 个临床病理参数。采用最小绝对值收缩和选择算子（LASSO）回归分析筛选出显著特征，构建用于 OS 和 DFS 的预测模型。Kaplan-Meier（KM）分析和对数秩检验用于确定放射组学评分（Rad-score）与生存时间之间的关系。同时评估了列线图的判别和校准能力。通过热图分析研究放射组学特征与临床参数之间的关系。
联合 T2W 和 DWI 图像的放射组学特征比单独 T2W 或 DWI 图像的特征具有更好的预后性能。在训练集和验证集中，较高的 Rad-score 与较差的 OS（<0.05）和 DFS（<0.05）相关。联合模型在预测 3 年 OS 和 DFS 方面优于单独的放射组学模型和临床病理模型。校准曲线达到了一致性。热图分析表明放射组学特征与临床特征之间存在显著关联。
基于 MRI 的放射组学列线图在预测早期宫颈癌 OS 和 DFS 方面具有良好的性能。通过与临床特征相结合，可以提高预后模型的预测能力，提示其在临床应用中的潜力。
这是第一项基于 T2W 和 DWI 图像构建放射组学模型以预测早期宫颈癌患者生存结局的研究，并进一步构建了一个结合临床特征的联合风险评分系统。
