基于多参数磁共振成像放射组学的模型预测子宫内膜癌5年无进展生存期
Multi-Parameter MR Radiomics Based Model to Predict 5-Year Progression-Free Survival in Endometrial Cancer.
作者信息
Liu Defeng, Yang Linsha, Du Dan, Zheng Tao, Liu Lanxiang, Wang Zhanqiu, Du Juan, Dong Yanchao, Yi Huiling, Cui Yujie
机构信息
Medical Imaging Center, First Hospital of Qinhuangdao, Qinhuangdao, China.
Department of Intervention, First Hospital of Qinhuangdao, Qinhuangdao, China.
出版信息
Front Oncol. 2022 Mar 31;12:813069. doi: 10.3389/fonc.2022.813069. eCollection 2022.
BACKGROUND
Relapse is the major cause of mortality in patients with resected endometrial cancer (EC). There is an urgent need for a feasible method to identify patients with high risk of relapse.
PURPOSE
To develop a multi-parameter magnetic resonance imaging (MRI) radiomics-based nomogram model to predict 5-year progression-free survival (PFS) in EC.
METHODS
For this retrospective study, 202 patients with EC followed up for at least 5 years after hysterectomy. A radiomics signature was extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) and a dynamic contrast-enhanced three-dimensional volumetric interpolated breath-hold examination (3D-VIBE). The radiomics score (RS) was calculated based on the least absolute shrinkage and selection operator (LASSO) regression. We have developed a radiomics based nomogram model (Model) incorporating the RS and clinical and conventional MR (cMR) risk factors. The performance was evaluated by the receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA).
RESULTS
The Model demonstrated a good calibration and satisfactory discrimination, with a mean area under the curve (AUC) of 0.840 and 0.958 in the training and test cohorts, respectively. In comparison with clinical prediction model (Model), the discrimination ability of Model showed an improvement with P < 0.001 for the training cohort and P=0.032 for the test cohort. Compared to the radiomics prediction model (Model), Model discrimination ability showed an improvement for the training cohort with P = 0.021, with no statistically significant difference in the test cohort (P = 0.106). Calibration curves suggested a good fit for probability (Hosmer-Lemeshow test, P = 0.610 and P = 0.956 for the training and test cohorts, respectively).
CONCLUSION
This multi-parameter nomogram model incorporating clinical and cMR findings is a valid method to predict 5-year PFS in patients with EC.
背景
复发是子宫内膜癌（EC）切除术后患者死亡的主要原因。迫切需要一种可行的方法来识别复发风险高的患者。
目的
建立基于多参数磁共振成像（MRI）影像组学的列线图模型，以预测EC患者的5年无进展生存期（PFS）。
方法
在这项回顾性研究中，202例EC患者在子宫切除术后至少随访5年。从T2加权成像（T2WI）、表观扩散系数（ADC）和动态对比增强三维容积内插屏气检查（3D-VIBE）中提取影像组学特征。基于最小绝对收缩和选择算子（LASSO）回归计算影像组学评分（RS）。我们开发了一种基于影像组学的列线图模型（模型），纳入了RS以及临床和传统MR（cMR）风险因素。通过受试者操作特征曲线（ROC）、校准曲线和决策曲线分析（DCA）评估其性能。
结果
该模型显示出良好的校准和令人满意的辨别力，训练队列和测试队列的曲线下平均面积（AUC）分别为0.840和0.958。与临床预测模型（模型）相比，模型的辨别能力有所提高，训练队列的P<0.001，测试队列的P=0.032。与影像组学预测模型（模型）相比，训练队列的模型辨别能力有所提高，P = 0.021，测试队列无统计学显著差异（P = 0.106）。校准曲线表明概率拟合良好（训练队列和测试队列的Hosmer-Lemeshow检验，P分别为0.610和0.956）。
结论
这种结合临床和cMR结果的多参数列线图模型是预测EC患者5年PFS的有效方法。
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