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Published on: October 25, 2011
The qualitative and quantitative characteristics of serous endometrial carcinoma on MRI: applying a novel nomogram
Rennan Ling1, Hongtao Jin2, He Zhang3
1Department 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.
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 (p = 0.03). The signal intensity (SI)T2Ratio, SIcontrastRatio, LesionareaRatio, and VolumeareaRatio in the SEC group were 1.35 ± 0.36, 0.77 ± 0.18, 0.25 ± 0.24, and 0.22 ± 0.26, respectively. The SIT2Ratio, SIcontrastRatio, and VolumeareaRatio showed statistically significant differences between SEC and EEC (p < 0.05). The highest discriminative index for distinguishing SEC from EEC was SIcontrastRatio 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.
