Abstract #3600

Using intra-and peri-tumoral radiomics features to identify LMN and LVSI in endometrial cancer from MRI images

Shengyong Li¹, He Zhang², Yida Wang¹, Yang Song³, and Guang Yang¹

¹Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, ²Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China, ³MR Scientific Marketing, Siemens Healthineers Ltd, Shanghai, China

Synopsis

Keywords: Diagnosis/Prediction, Cancer

Motivation: Preoperational identification of lymph node metastasis (LNM) and lymphatic vascular space invasion (LVSI) of endometrial cancer from MRI is important to treatment planning.

Goal(s): To explore power of intra/peri-tumor radiomic features from DWI, T1CE and T2W images to identify LVSI and LNM.

Approach: We developed radiomics models with intra/peri-tumor features from different MRI images and compared their performance.We developed radiomics models for intra- and peri-tumoral features and compare performance.

Results: For LVSI, T2W model using both intra- and peri-tumoral features achieved AUC values of 0.790/0.696 in internal/external test cohorts. For LNM, the combined model achieved AUC values of 0.801/0.976 in internal/external test cohorts.

Impact: The radiomics signatures built with intra- and peri-tumoral features extracted from DWI, T1CE, T2W sequences can yield satisfactory predictions for both LVSI and LNM status in endometrial cancer.

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