Keywords: Cancer, Cancer, Rectal cancer, Deep learning radiomics, Magnetic resonance imaging, Synchronous liver metastasis
Motivation: Accurate synchronous liver metastasis (SLM) risk stratification is important for treatment planning and prognosis improvement.
Goal(s): Our goal is to establish a non-invasive and quantitative prediction model of synchronous liver metastases (SLM) in rectal cancer (RC) to help with accurate staging.
Approach: The deep learning (DL) model was fitted based on multi-parameter MRI of primary cancer combined with Clinical features (CF) features, and 5-fold cross-validation and external validation were performed.
Results: We demonstrated that the combination of CF and DL features achieved a satisfactory predictive performance for SLM, and also confirmed the generalizability of this model by external validation.
Impact: The discovery of the DL model would change treatment strategies. For patients with high-risk metastasis, a more aggressive systemic examination and shorter follow-up should be considered and may contribute to improved outcomes.
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