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肿瘤内和肿瘤周围放射组学特征均可用于预测子宫内膜癌 MRI 中的淋巴管侵犯和淋巴管转移阳性状态。

Both intra- and peri-tumoral radiomics signatures can be used to predict lymphatic vascular space invasion and lymphatic metastasis positive status from endometrial cancer MR imaging.

机构信息

Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People's Republic of China.

Department of Pathology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, People's Republic of China.

出版信息

Abdom Radiol (NY). 2024 Nov;49(11):4140-4150. doi: 10.1007/s00261-024-04432-3. Epub 2024 Jun 25.

Abstract

OBJECTIVES

To identify lymphatic vascular space invasion (LVSI) and lymphatic node metastasis (LNM) status of endometrial cancer (EC) patients, using radiomics based on MRI images.

METHODS

Five hundred and ninety-eight EC patients between January 2015 and September 2020 from two institutions were retrospectively included. Tumoral regions on DWI, T1CE, and T2W images were manually outlined. Radiomics features were extracted from tumor region and peri-tumor region of different thicknesses. We established sub-models to select features from each smaller category. Using this method, we separately constructed radiomic signatures for intra-tumoral and peri-tumoral images using different sequences. We constructed intra-tumoral and peri-tumoral models by combining their features, and a multi-sequence model by combining logits. Models were trained with 397 patients and validated with 170 internal and 31 external patients.

RESULTS

For LVSI positive/LNM positive status identification, the multi-parameter MRI radiomics model achieved the area under curve (AUC) values of 0.771 (95%CI: [0.692-0.849])/0.801 (95%CI: [0.704, 0.898]) and 0.864 (95%CI: [0.728-1.000])/0.976 (95%CI: [0.919, 1.000]) in internal and external test cohorts, respectively.

CONCLUSIONS

Intra-tumoral and peri-tumoral radiomics signatures based on mpMRI can both be used to identify LVSI or LNM status in EC patients non-invasively. Further studies on LVSI and LNM should pay attention to both of them.

摘要

目的

利用基于 MRI 图像的放射组学方法识别子宫内膜癌(EC)患者的淋巴管血管空间侵犯(LVSI)和淋巴结转移(LNM)状态。

方法

回顾性纳入 2015 年 1 月至 2020 年 9 月来自两个机构的 598 例 EC 患者。手动勾勒出 DWI、T1CE 和 T2W 图像上的肿瘤区域。从肿瘤区域和不同厚度的肿瘤周围区域提取放射组学特征。我们从每个较小类别中选择特征,分别为肿瘤内和肿瘤周围图像建立子模型。使用这种方法,我们分别使用不同序列为肿瘤内和肿瘤周围图像构建放射组学特征。通过组合它们的特征,我们构建了肿瘤内和肿瘤周围模型,并通过组合对数比构建了多序列模型。模型在 397 名患者中进行训练,并在 170 名内部和 31 名外部患者中进行验证。

结果

对于 LVSI 阳性/LNM 阳性状态的识别,多参数 MRI 放射组学模型在内部和外部测试队列中的 AUC 值分别为 0.771(95%CI:[0.692-0.849])/0.801(95%CI:[0.704,0.898])和 0.864(95%CI:[0.728-1.000])/0.976(95%CI:[0.919,1.000])。

结论

基于 mpMRI 的肿瘤内和肿瘤周围放射组学特征均可用于非侵入性识别 EC 患者的 LVSI 或 LNM 状态。进一步的 LVSI 和 LNM 研究应同时关注两者。

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