Department of Surgery and Cancer, Imperial College, London, UK.
Chelsea and Westminster Hospital NHS Foundation Trust, London, UK.
J Magn Reson Imaging. 2023 Jun;57(6):1922-1933. doi: 10.1002/jmri.28544. Epub 2022 Dec 9.
Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.
To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.
Retrospective.
Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).
FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence.
Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.
A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.
Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively.
The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer.
4 TECHNICAL EFFICACY: Stage 2.
使用临床特征来确定子宫内膜癌患者的生存时间仍然不够精确。MRI 上的特征可以改善生存估计，从而改善治疗计划。
确定 T2 加权 MRI 上的临床特征和影像学特征，这些特征可以在综合模型中用于估计子宫内膜癌患者的生存时间。
回顾性。
413 名子宫内膜癌患者作为训练（N=330，66.41±11.42 岁）和验证（N=83，67.60±11.89 岁）数据，以及 82 名独立患者作为测试数据（63.26±12.38 岁）。
磁场强度/序列：1.5-T 和 3-T 扫描仪，矢状位 T2 加权自旋回波序列。
手动在 T2 加权图像上分割肿瘤区域。从分割掩模中提取特征，并将临床变量（包括年龄、癌症组织学分级和风险评分）纳入 Cox 比例风险（CPH）模型。采用组最小绝对收缩和选择算子方法，从训练和验证数据集确定模型。
应用似然比检验和决策曲线分析比较模型。一致性指数（CI）和接收器工作特征曲线下面积（AUCs）用于评估模型。
三个放射组学特征（两个图像强度和体积特征）和两个临床变量（年龄和癌症分级）被选为综合模型的预测因子。临床模型（仅包含临床变量）的 CI 为 0.797，综合模型（使用训练和验证数据集）的 CI 为 0.818，相关平均 AUC 值分别为 0.805 和 0.853。使用测试数据集，临床模型和综合模型的 CI 分别为 0.792 和 0.882，差异显著，平均 AUC 值分别为 0.624 和 0.727。
提出的带有放射组学特征的 CPH 模型可作为一种工具，以提高子宫内膜癌患者的估计生存时间。
4 级技术功效：2 级。
