Abstract #0170

Evaluating Microstructures in Endometrial Cancer Using Diffusion-Relaxation Correlated Spectroscopic Imaging

Yongming Dai¹, Dongmei Wu², Tianshu Yang³, Wentao Hu⁴, Peng Hu⁵, Hui Liu⁶, and Gaofeng Shi⁶

¹School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China, ²Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China, ³Department of Radiology, Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China, ⁴Renji hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China, ⁵School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, Christmas Island, ⁶Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, China, Shijiazhuang, China

Synopsis

Keywords: Uterus, Diffusion Modeling

Motivation: Conventional MRI techniques often fail to capture the detailed microstructural complexities of Endometrial Cancer (EC).

Goal(s): To evaluate the efficacy of diffusion relaxation correlated spectroscopic imaging (DR-CSI) in assessing EC and to compare its diagnostic performance with conventional MRI techniques.

Approach: We enrolled 62 patients with histopathologically confirmed EC. DR-CSI data were analyzed to generate a four-compartment D-T2 spectra, yielding corresponding volume fraction metrics. The relationships between imaging parameters and histopathologic results were evaluated.

Results: DR-CSI derived parameters closely associated with histopathological status including histological grades (V_II, V_III); lymph node metastasis status (VF_IV), and myometrial invasion statuses (VF_I and ADC).

Impact: DR-CSI offers a novel and effective method for quantifying microstructural compartments in EC, providing superior diagnostic accuracy compared to conventional techniques. The ability to capture detailed microstructural information from DR-CSI metrics holds promise for offering deeper insights into tumor heterogeneity.

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