Research Progress of MRI-based Radiomics and Deep Learning in Lymphovascular Space Invasion of Endometrial Carcinoma
DOI:
https://doi.org/10.54097/4tgers42Keywords:
Endometrial Carcinoma, Magnetic Resonance Imaging, Radiomics, Deep Learning, Lymphovascular Space Invasion, NomogramAbstract
Endometrial carcinoma (EC) is a highly prevalent malignant tumor of the female reproductive system, with a continuously rising global incidence and an increasing trend toward younger age at onset. Lymphovascular space invasion (LVSI) is an important independent prognostic risk factor for endometrial cancer and is closely associated with lymph node metastasis, distant recurrence, and poor overall survival. It is a core indicator for clinical risk stratification, selection of surgical extent, and formulation of postoperative adjuvant therapy. However, LVSI is a microscopic pathological change that can only be definitively diagnosed using postoperative pathological specimens. Subjective imaging interpretation of routine preoperative pelvic MRI cannot directly detect LVSI, and therefore cannot meet the clinical need for accurate preoperative risk assessment. MRI-based radiomics can mine high-throughput quantitative features that are invisible to the naked eye within tumor images, and deep learning can automatically learn hierarchical image features. Together, they provide a new avenue for noninvasive preoperative prediction of LVSI status in endometrial cancer. This article systematically reviews the research progress of MRI-based radiomics and deep learning in the field of LVSI prediction in endometrial cancer, elaborates on the standard radiomics workflow, traditional radiomics models, deep learning models, and the diagnostic performance of combined clinical-imaging prediction models, summarizes key issues in current research such as limited sample size, cross-center domain shift, insufficient reproducibility, and lack of prospective validation, and provides an outlook on future directions including clinical translation, multi-omics integration, and explainable artificial intelligence.
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