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Abstract #1403

MRI-based Habitat Radiomics for Evaluating Lymph Node Metastasis in Renal Cell Carcinoma

Xu Bai1, Haiyi Wang1, Xu Fu2, Honghao Xu1, Shaopeng Zhou1, Sicheng Yi1, Lizhi Xie3, Haili Liu1, Xuetao Mu1, Mengmeng Zhang1, Huiyi Ye1, and Xin Ma1
1Chinese PLA General Hospital, Beijing, China, 2School of Engineering Medicine, Beihang University, Beijing, China, 3MR Research China, GE Healthcare, Beijing, China

Synopsis

Keywords: Diagnosis/Prediction, Cancer, Renal cell carcinoma; Lymph node metastasis; Habitat; Radiomics

Motivation: The prognostic property of regional lymph node metastasis (RLNM) has been widely recognized, but the diagnostic workup has stagnated for renal cell carcinoma (RCC).

Goal(s): This study aimed to develop a machine learning model using MRI-based habitat radiomics to enhance the preoperative assessment of RLNM in RCC.

Approach: A multi-center retrospective study.

Results: Using 25 optimal habitat radiomics features combined with the diameter of lymph node, the support vector machine model achieved areas under the receiver operating characteristic curves of 0.87 and 0.89 in the internal and external test cohorts, respectively, both exceeding those of the node diameter alone.

Impact: The MRI-based habitat radiomics combined model demonstrates a robust non-invasive capability for assessing regional lymph node metastasis in renal cell carcinoma, providing significant insights for clinical staging, surgical decision-making, and prognostic evaluation.

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