University of Science and Technology of China, Hefei, 230026, Anhui, China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, No.88 Keling Road, Suzhou New District, Suzhou, 215163, Jiangsu, China.
Eur Radiol. 2021 Jan;31(1):403-410. doi: 10.1007/s00330-020-07091-2. Epub 2020 Aug 2.
Epithelial ovarian cancers (EOC) can be divided into type I and type II according to etiology and prognosis. Accurate subtype differentiation can substantially impact patient management. In this study, we aimed to construct an MR image-based radiomics model to differentiate between type I and type II EOC.
In this multicenter retrospective study, a total of 294 EOC patients from January 2010 to February 2019 were enrolled. Quantitative MR imaging features were extracted from the following axial sequences: T2WI FS, DWI, ADC, and CE-T1WI. A combined model was constructed based on the combination of these four MR sequences. The diagnostic performance was evaluated by ROC-AUC. In addition, an occlusion test was carried out to identify the most critical region for EOC differentiation.
The combined radiomics model exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.806 and 0.847, respectively). The occlusion test revealed that the most critical region for differential diagnosis was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection.
MR image-based radiomics modeling can differentiate between type I and type II EOC and identify the most critical region for differential diagnosis.
• Combined radiomics models exhibited superior diagnostic capability over all four single-parametric radiomics models, both in internal and external validation cohorts (AUC of 0.834 and 0.847, respectively). • The occlusion test revealed that the most crucial region for differentiating type Ι and type ΙΙ EOC was the border zone between the solid and cystic components, or the less compact areas of solid component on direct visual inspection on T2WI FS. • The light-combined model (constructed by T2WI FS, DWI, and ADC sequences) can be used for patients who are not suitable for contrast agent use.
根据病因和预后，上皮性卵巢癌（EOC）可分为 I 型和 II 型。准确的亚型分化可显著影响患者的管理。本研究旨在构建基于 MRI 的放射组学模型以区分 I 型和 II 型 EOC。
本多中心回顾性研究纳入了 2010 年 1 月至 2019 年 2 月期间的 294 名 EOC 患者。从以下轴位序列中提取定量 MRI 特征：T2WI FS、DWI、ADC 和 CE-T1WI。基于这四个 MR 序列的组合构建了一个综合模型。通过 ROC-AUC 评估诊断性能。此外，还进行了闭塞测试以确定 EOC 鉴别诊断的最关键区域。
与所有四个单参数放射组学模型相比，组合放射组学模型在内部和外部验证队列中的诊断能力均更高（AUC 分别为 0.806 和 0.847）。闭塞测试显示，用于鉴别诊断的最关键区域是实体和囊性成分之间的边界区域，或直接观察 T2WI FS 时实体成分的不致密区域。
基于 MRI 的放射组学建模可区分 I 型和 II 型 EOC，并确定鉴别诊断的最关键区域。
与所有四个单参数放射组学模型相比，组合放射组学模型在内部和外部验证队列中的诊断性能均更高（AUC 分别为 0.834 和 0.847）。
闭塞测试显示，用于鉴别诊断的最关键区域是实体和囊性成分之间的边界区域，或直接观察 T2WI FS 时实体成分的不致密区域。
由 T2WI FS、DWI 和 ADC 序列构建的轻组合模型可用于不适合使用造影剂的患者。
