Cai Mengting, Yao Fei, Ding Jie, Zheng Ruru, Huang Xiaowan, Yang Yunjun, Lin Feng, Hu Zhangyong
Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Department of Gynecology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Front Oncol. 2021 Dec 14;11:749114. doi: 10.3389/fonc.2021.749114. eCollection 2021.
To investigate the prognostic role of radiomic features based on pretreatment MRI in predicting progression-free survival (PFS) of locally advanced cervical cancer (LACC).
All 181 women with histologically confirmed LACC were randomly divided into the training cohort (n = 126) and the validation cohort (n = 55). For each patient, we extracted radiomic features from whole tumors on sagittal T2WI and axial DWI. The least absolute shrinkage and selection operator (LASSO) algorithm combined with the Cox survival analysis was applied to select features and construct a radiomic score (Rad-score) model. The cutoff value of the Rad-score was used to divide the patients into high- and low-risk groups by the X-tile. Kaplan-Meier analysis and log-rank test were used to assess the prognostic value of the Rad-score. In addition, we totally developed three models, the clinical model, the Rad-score, and the combined nomogram.
The Rad-score demonstrated good performance in stratifying patients into high- and low-risk groups of progression in the training (HR = 3.279, 95% CI: 2.865-3.693, < 0.0001) and validation cohorts (HR = 2.247, 95% CI: 1.735-2.759, < 0.0001). Otherwise, the combined nomogram, integrating the Rad-score and patient's age, hemoglobin, white blood cell, and lymph vascular space invasion, demonstrated prominent discrimination, yielding an AUC of 0.879 (95% CI, 0.811-0.947) in the training cohort and 0.820 (95% CI, 0.668-0.971) in the validation cohort. The Delong test verified that the combined nomogram showed better performance in estimating PFS than the clinical model and Rad-score in the training cohort ( = 0.038, = 0.043).
The radiomics nomogram performed well in individualized PFS estimation for the patients with LACC, which might guide individual treatment decisions.
探讨基于治疗前磁共振成像（MRI）的影像组学特征在预测局部晚期宫颈癌（LACC）无进展生存期（PFS）中的预后作用。
181例经组织学确诊的LACC患者被随机分为训练队列（n = 126）和验证队列（n = 55）。对于每位患者，我们在矢状位T2加权成像（T2WI）和轴位扩散加权成像（DWI）上从整个肿瘤中提取影像组学特征。应用最小绝对收缩和选择算子（LASSO）算法结合Cox生存分析来选择特征并构建影像组学评分（Rad-score）模型。通过X-tile软件使用Rad-score的截断值将患者分为高风险组和低风险组。采用Kaplan-Meier分析和对数秩检验来评估Rad-score的预后价值。此外，我们总共建立了三个模型，即临床模型、Rad-score和联合列线图。
Rad-score在训练队列（HR = 3.279，95%可信区间：2.865 - 3.693，P < 0.0001）和验证队列（HR = 2.247，95%可信区间：1.735 - 2.759，P < 0.0001）中，在将患者分层为进展的高风险组和低风险组方面表现良好。此外，整合了Rad-score以及患者年龄、血红蛋白、白细胞和淋巴血管间隙浸润情况的联合列线图显示出显著的区分能力，在训练队列中的曲线下面积（AUC）为0.879（95%可信区间，从0.811至0.947），在验证队列中的AUC为0.820（95%可信区间，0.668 - 0.971）。Delong检验证实，在训练队列中，联合列线图在估计PFS方面比临床模型和Rad-score表现更好（P = 0.038，P = 0.043）。
影像组学列线图在LACC患者的个体化PFS估计中表现良好，这可能指导个体化治疗决策。
