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Abstract #1548

Fully automatic lesion segmentation and deep myometrial invasion prediction method for endometrial cancer based on MRI

Kaihua Gao1, Hui Wu2, and Wenjia Wang3
1the Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China, 2the Affiliated Hospital of Inner Mongolia Medical University, Hohhot , Inner Mongolia, China, 3GE HealthCare MR Research, Beijing, China, China

Synopsis

Keywords: Diagnosis/Prediction, Uterus

Motivation: The judgment of myometrial invasion depth by traditional imaging is subjectively affected by observers. Manual segmentation is labor-intensive and not feasible in daily work.

Goal(s): This study explores the use of nnU-Net for automatic segmentation of endometrial cancer, and uses radiomics to predict endometrial cancer deep myometrial invasion.

Approach: 127 endometrial cancer patients were assigned to two cohorts in a 7:3 ratio. 50 cases of data from the training group were selected to establish a nnU-Net segmentation model.

Results: Dice score of nnU-Net were 0.956 and 0.922 in the training and validation group. The AUCs of combined model were 0.903 and 0.894 respectively.

Impact: NnU-Net has the potential to automatically identify and segment endometrial cancer lesions. The combined model integrating radiomics features and clinical risk factors has a better ability to identify deep myometrial invasion.

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Keywords