Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China (Y.C., L.H., D.L., X.Y.); Department of Breast Surgery, Guizhou Provincial People's Hospital, Guiyang, China (G.W.); GE Healthcare China, Beijing, China (J.R.); Department of Medical Record, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China (Q.W.); Department of Pathology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China (Y.X.).
Department of Radiology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China (Y.C., L.H., D.L., X.Y.); Department of Breast Surgery, Guizhou Provincial People's Hospital, Guiyang, China (G.W.); GE Healthcare China, Beijing, China (J.R.); Department of Medical Record, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China (Q.W.); Department of Pathology, Shanxi Province Cancer Hospital, Shanxi Medical University, Taiyuan, China (Y.X.).
Acad Radiol. 2022 Aug;29(8):e128-e138. doi: 10.1016/j.acra.2021.11.024. Epub 2021 Dec 24.
To investigate the potential value of radiomics features based on preoperative multiparameter MRI in predicting disease-free survival (DFS) in patients with local advanced rectal cancer (LARC).
We identified 234 patients with LARC who underwent preoperative MRI, including T2-weighted, diffusion kurtosis imaging, and contrast enhanced T1-weighted. All patients were randomly divided into the training (n = 164) and validation (n = 70) cohorts. 414 features were extracted from the tumor from above sequences and the radiomics signature was then generated, mainly based on feature stability and Cox proportional hazards model. Two models, integrating pre- and postoperative variables, were constructed to validate the radiomics signatures for DFS estimation.
The radiomics signature, composed of six DFS-related features, was significantly associated with DFS in the training and validation cohorts (both p < 0.001). The radiomics signature and MR-defined extramural venous invasion (mrEMVI) were identified as the independent predictor of DFS both in the pre- and postoperative models. In both cohorts, the two radiomics-based models exhibited better prediction performance (C-index ≥0.77, all p < 0.05) than the corresponding clinical models, with positive net reclassification improvement and lower Akaike information criterion (AIC). Decision curve analysis also confirmed their clinical usefulness. The radiomics-based models could categorize LARC patients into high- and low-risk groups with distinct profiles of DFS (all p < 0.05).
The proposed radiomics models with pre- and postoperative features have the potential to predict DFS, and may provide valuable guidance for the future individualized management in patients with LARC.
探究基于术前多参数 MRI 的影像组学特征在预测局部晚期直肠癌（LARC）患者无病生存（DFS）中的潜在价值。
本研究共纳入 234 例接受术前 MRI 检查的 LARC 患者，包括 T2 加权成像、扩散峰度成像和对比增强 T1 加权成像序列。所有患者被随机分为训练集（n=164）和验证集（n=70）。从上述序列中提取肿瘤的 414 个特征，并基于特征稳定性和 Cox 比例风险模型生成影像组学特征。构建了两种整合术前和术后变量的模型，以验证影像组学特征预测 DFS 的能力。
由 6 个与 DFS 相关的特征组成的影像组学特征在训练集和验证集中与 DFS 显著相关（均 P＜0.001）。影像组学特征和 MRI 定义的外膜静脉侵犯（mrEMVI）在术前和术后模型中均被确定为 DFS 的独立预测因子。在两个队列中，两种基于影像组学的模型的预测性能均优于相应的临床模型（C 指数≥0.77，均 P＜0.05），且具有阳性净重新分类改善和更低的 Akaike 信息准则（AIC）。决策曲线分析也证实了其临床应用价值。基于影像组学的模型可将 LARC 患者分为具有不同 DFS 特征的高风险和低风险组（均 P＜0.05）。
基于术前和术后特征的影像组学模型具有预测 DFS 的潜力，可能为 LARC 患者的个体化管理提供有价值的指导。
