We aim to investigate added values of computer-aided segmentation and radiomic machine learning based on diffusion-weighted magnetic resonance (MR) imaging for predicting nodal metastasis in endometrial cancer. Decision-tree machine learning comprised the apparent diffusion coefficient (ADC), whole tumor volumetric and lymph nodes (LNs) segmentations, MR morphological measurement, and relevant clinical parameters. We concluded that a combination of clinical and MR radiomics generates a prediction model for LNmetastasis in endometrial cancer, with diagnostic performance surpassing the conventional ADC and size criteria.
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