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Abstract #1404

Tumor ecology model based on quantitative parameters of DCE-MRI for predicting lymph node metastasis in rectal cancer

Yun Sun1, Kai Ai2, and Gang Huang3
1Gansu University Of Chinese Medicine, Lanzhou, China, 2Department of Clinical and Technical Support, Philips Healthcare, Xi’an, China, 3Gansu province people hospital, Lanzhou, China

Synopsis

Keywords: fMRI Analysis, fMRI Analysis

Motivation: Lymph node metastasis (LNM) significantly impacts treatment decisions and prognosis in rectal cancer (RC).

Goal(s): This study investigates the construction of a landscape index model of the RC sub-habitat using DCE-MRI parameter maps to predict LNM in RC prior to treatment.

Approach: DCE-MRI-derived Ktrans maps segmented each tumor into two habitat subregions based on Ktrans values. Landscape pattern indices were introduced to quantify the spatial features of habitat imaging, and a diagnostic model for LNM was constructed using machine learning.

Results: In the validation cohort, the landscape index model at the subregion 1achieved an AUC of 0.840 and demonstrated biological interpretability.

Impact: Accurately identifying the LN status of RC patients prior to initial treatment is crucial for determining treatment strategies. The proposed quantitative DEC-MRI tumor ecology model is a promising tool for predicting LNM in RC.

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