Department of Radiology, First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
GE Healthcare, Precision Health Institution, Shanghai, China.
Br J Radiol. 2022 Aug 1;95(1136):20211229. doi: 10.1259/bjr.20211229. Epub 2022 May 30.
To establish a comprehensive model including MRI radiomics and clinicopathological features to predict post-operative disease-free survival (DFS) in early-stage (pre-operative FIGO Stage IB-IIA) cervical cancer.
A total of 183 patients with early-stage cervical cancer admitted to our Jiangsu Province Hospital underwent radical hysterectomy were enrolled in this retrospective study from January 2013 to June 2018 and their clinicopathology and MRI information were collected. They were then divided into training cohort ( = 129) and internal validation cohort ( = 54). The radiomic features were extracted from the pre-operative T1 contrast-enhanced (T1CE) and weighted image of each patient. Least absolute shrinkage and selection operator regression and multivariate Cox proportional hazard model were used for feature selection, and the rad-score (RS) of each patient were evaluated individually. The clinicopathology model, T1CE_RS model, T1CE + T2_RS model, and clinicopathology combined with T1CE_RS model were established and compared. Patients were divided into high- and low-risk groups according to the optimum cut-off values of four models.
T1CE_RS model showed better performance on DFS prediction of early-stage cervical cancer than clinicopathological model (C-index: 0.724 vs 0.659). T1CE+T2_RS model did not improve predictive performance (C-index: 0.671). The combination of T1CE_RS and clinicopathology features showed more accurate predictive ability (C-index=0.773).
The combination of T1CE_RS and clinicopathology features showed more accurate predictive performance for DFS of patients with early-stage (pre-operative IB-IIA) cervical cancer which can aid in the design of individualised treatment strategies and regular follow-up.
A radiomics signature composed of T1CE radiomic features combined with clinicopathology features allowed differentiating patients at high or low risk of recurrence.
建立一个包含 MRI 放射组学和临床病理特征的综合模型，以预测早期（术前 FIGO 分期 IB-IIA）宫颈癌的无病生存（DFS）。
本回顾性研究共纳入 183 例 2013 年 1 月至 2018 年 6 月在我院接受根治性子宫切除术的早期宫颈癌患者，收集其临床病理和 MRI 信息。将其分为训练队列（n=129）和内部验证队列（n=54）。从每位患者的术前 T1 增强（T1CE）和 T2 加权图像中提取放射组学特征。采用最小绝对收缩和选择算子回归及多变量 Cox 比例风险模型进行特征选择，并对每位患者的放射评分（RS）进行评估。建立并比较了临床病理模型、T1CE_RS 模型、T1CE+T2_RS 模型和临床病理联合 T1CE_RS 模型。根据四个模型的最佳截断值将患者分为高风险组和低风险组。
T1CE_RS 模型在预测早期宫颈癌 DFS 方面的表现优于临床病理模型（C 指数：0.724 比 0.659）。T1CE+T2_RS 模型并不能提高预测性能（C 指数：0.671）。T1CE_RS 与临床病理特征的联合显示出更准确的预测能力（C 指数=0.773）。
T1CE_RS 与临床病理特征的联合可以更准确地预测早期（术前 IB-IIA）宫颈癌患者的 DFS，有助于制定个体化治疗策略和定期随访。
由 T1CE 放射组学特征与临床病理特征组成的放射组学特征能够区分复发风险高或低的患者。
