Value of optimal MRI radiomics parameters and clinicopathological factors in predicting disease⁃free survival of early⁃stage cervical cancer patients
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摘要:
目的：对比最优磁共振组学参数及临床病理参数预测早期宫颈癌患者无病生存期的诊断效能。方法：回顾性分析2013年1月—2018年6月行根治性子宫切除及淋巴结清扫术的186例早期宫颈癌患者[术前国际妇产科联盟（Federation International of Gynecology and Obstetrics，FIGO）分期ⅠB~ⅡA]，搜集患者多模态磁共振组学参数、临床特征、术后病理特征及患者术后无病生存期，用最小决策收缩和选择算子（LASSO）及比例风险回归模型来计算影像组学评分（Rad-score），构建临床病理特征模型（包括有意义的临床特征和病理特征）、影像组学模型（T1CE、DWI、T2WI、T1CE+DWI、T1CE+T2WI、DWI+T2WI 和T1CE+DWI+T2WI的影像组学评分）及联合模型对早期宫颈癌患者无病生存期的预测效能进行相互比较。结果：在预测早期宫颈癌无病生存期中，T1CE序列的组学模型（C指数：训练集0.798，验证集0.758）优于临床病理模型（C指数：训练集0.746， 验证集0.641）。联合模型（病理类型、淋巴结转移及T1CE的影像组学评分）拥有最高的诊断效能（C指数：训练集0.848，验证集 0.784）。结论：基于T1CE的影像组学评分联合临床病理特征对早期宫颈癌无病生存期具有较高的预测效能。
Abstract:
Objective：To determine the optimal preoperative magnetic resonance imaging（MRI）radiomics parameters and compare the values in predicting disease-free survival（DFS）of early-stage cervical cancer patients with clinicopathological factors. Methods：A total of 186 patients with early-stage cervical cancers（preoperative FIGO stage ⅠB-ⅡA），who underwent radical hysterectomy with lymphadenectomy during January 2013 and June 2018，were retrospectively reviewed. Multi-sequence MR data，clinicopathologic data， and DFS data were collected；least - absolute shrinkage and selection operator（LASSO）and the Cox proportional hazard regression model were applied to construct a Rad - score. Clinicopathological models（with significant clinicopathological features），radiomics models（Rad-scores for T1CE，DWI，T2WI，T1CE+DWI，T1CE+T2WI，DWI+T2WI，and T1CE+DWI+T2WI），and combined models （the Rad - score model combined with significant clinicopathological features）were compared using a multivariable Cox model. Results：The T1CE radiomics model was the most stable and optimal model（C-index for training，0.798；for validation，0.758）of all the radiomics models. The radiomics model of T1CE showed higher prognostic performance than the clinicopathological model（C-index for training，0.746；for validation，0.641）. The combined model（histological type of adenocarcinoma，lymph-node metastasis together with Rad-score for T1CE）showed the highest prognostic performance in estimating DFS（C-index for training，0.848；for validation，0.784）. Conclusion：An MRI-derived Rad-score of T1CE combined with a clinicopathological model is optimal in predicting DFS in early-stage cervical cancers.
