Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study
Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study
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DOI：
10.1007/s00330-023-09685-y
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发表时间：
2023-05-12
影响因子：
5.9
通讯作者：
Li，Haiming
中科院分区：
文献类型：
--
作者：
Lin，Zijing;Wang，Ting;Li，Haiming
目的 建立基于临床病理因素和 MRI 影像组学特征的融合模型，用于预测子宫内膜癌（EC）患者的复发风险。 方法 本回顾性研究纳入来自 4 个医疗中心的 421 例经组织病理学证实的 EC 患者（复发 101 例，未复发 320 例），分为训练组（n= 235 例）、内部验证组（n= 102 例）、内部验证组（n= 102 例）。和外部验证 (n= 84) 队列。总共，从每位患者不同延伸的区域分别提取了 1702 个放射组学特征。应用极限梯度增强（XGBoost）分类器建立临床病理模型（CM）、放射组学模型（RM）和融合模型（FM）。通过区分、校准和临床实用性来评估所建立模型的性能。通过评估高复发风险和低复发风险患者之间的无复发生存率（RFS）差异，进行Kaplan-Meier分析以进一步确定模型的预后价值。结果与仅基于临床病理学或放射组学特征的模型相比，FM表现出更好的性能，但当瘤周面积（PA）扩展时，其性能下降的趋势。基于瘤内面积（IA）的FM[FM（IA）]在ROC、校准曲线和决策曲线分析方面在预测复发风险方面具有最佳性能。 Kaplan-Meier生存曲线显示，FM（IA）定义的复发高危患者的RFS比复发低危患者差。结论：整合瘤内放射组学特征和临床病理因素的FM可能是EC患者复发风险的有价值的预测因子。临床相关性声明基于我们开发的FM（IA）对EC复发风险的准确预测有助于做出个体化治疗决策，有助于避免治疗不足或过度治疗，从而改善EC患者的预后要点•与临床病理模型和放射组学模型相比，结合临床病理因素和放射组学特征的融合模型表现出最高的性能。•虽然所有融合模型都观察到较高的曲线下面积值，但随着瘤周区域的扩展，性能趋于下降。•识别不同复发风险的患者，开发的模型可用于促进个体化管理。
ObjectivesTo develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC).MethodsA total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n= 235), internal validation (n= 102), and external validation (n= 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan–Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence.ResultsThe FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan–Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence.ConclusionsThe FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients.Clinical relevance statementAn accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients.Key Points•The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model.•Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region.•Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.