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MRI 上浆液性子宫内膜癌的定性和定量特征:应用一种新型列线图预测侵袭性组织学类型

The qualitative and quantitative characteristics of serous endometrial carcinoma on MRI: applying a novel nomogram for predicting an aggressive histological type.

作者信息

Ling Rennan, Jin Hongtao, Zhang He

机构信息

Department of Radiology, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, 1st Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.

Department of Pathology, Shenzhen People's Hospital, 2nd Clinical Medical College of Jinan University, 1st Affiliated Hospital of Southern University of Science and Technology, Shenzhen, China.

出版信息

Front Oncol. 2025 Mar 14;15:1472250. doi: 10.3389/fonc.2025.1472250. eCollection 2025.

Abstract

OBJECTIVES

To comprehensively describe MRI characteristics of serous endometrial carcinoma (SEC) and distinguish SEC from endometrioid endometrial carcinoma (EEC).

METHODS

We retrospectively recruited 62 patients from a tertiary center with pathologically proven endometrioid cancers (37 SEC and 25 EEC) as the training set. MRI image interpretation was blindly interpreted by two experienced radiologists with consensus reading. Both qualitative and quantitative characteristics on MRI were recorded case by case. Histological findings were retrieved from the hospital information system. Fifty-four samples (27 SEC and 27 EEC) from the external hospital were treated as the testing set.

RESULTS

The qualitative MRI characteristics had no statistical difference between the SEC and EEC groups in the training set. SEC more often invaded the deep myometrium than EEC ( = 0.03). The signal intensity (SI)Ratio, SIRatio, LesionRatio, and VolumeRatio in the SEC group were 1.35 ± 0.36, 0.77 ± 0.18, 0.25 ± 0.24, and 0.22 ± 0.26, respectively. The SIRatio, SIRatio, and VolumeRatio showed statistically significant differences between SEC and EEC ( < 0.05). The highest discriminative index for distinguishing SEC from EEC was SIRatio with an area under the curve (AUC) of 0.7533 (95% CI: 0.627-0.878). A predictive nomogram achieved an AUC of 0.814 (95% CI: 0.614-0.968), a sensitivity of 1.0, and a specificity of 0.60 in the testing set.

CONCLUSIONS

This study developed and validated a nomogram model to predict SEC patients based on clinical and quantitative MRI features, which can be used in distinguishing SEC from EEC.

摘要

目的

全面描述浆液性子宫内膜癌(SEC)的MRI特征,并将SEC与子宫内膜样腺癌(EEC)区分开来。

方法

我们从一家三级中心回顾性招募了62例经病理证实为子宫内膜样癌的患者(37例SEC和25例EEC)作为训练集。由两名经验丰富的放射科医生对MRI图像进行盲法解读,并达成共识。逐例记录MRI的定性和定量特征。从医院信息系统中获取组织学结果。将来自外院的54个样本(27例SEC和27例EEC)作为测试集。

结果

训练集中SEC组和EEC组的MRI定性特征无统计学差异。SEC比EEC更常侵犯子宫肌层深部(P = 0.03)。SEC组的信号强度(SI)比值、SIRatio、病变比值和体积比值分别为1.35±0.36、0.77±0.18、0.25±0.24和0.22±0.26。SEC和EEC之间的SIRatio、SIRatio和体积比值显示出统计学显著差异(P<0.05)。区分SEC和EEC的最高鉴别指数是SIRatio,曲线下面积(AUC)为0.7533(95%CI:0.627 - 0.878)。在测试集中,预测列线图的AUC为0.814(95%CI:0.614 - 0.968),灵敏度为1.0,特异性为0.60。

结论

本研究开发并验证了一种基于临床和定量MRI特征预测SEC患者的列线图模型,可用于区分SEC和EEC。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b5e2/11949794/cd954adb3c2f/fonc-15-1472250-g001.jpg

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