Fully Automated Identification of Lymph Node Metastases and Lymphovascular Invasion in Endometrial Cancer From Multi‐Parametric MRI by Deep Learning

Background Early and accurate identification of lymphatic node metastasis (LNM) and lymphatic vascular space invasion (LVSI) for endometrial cancer (EC) patients is important for treatment design, but difficult on multi‐parametric MRI (mpMRI) images. Purpose To develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images. Study Type Retrospective. Population Six hundred twenty‐one patients with histologically proven EC from two institutions, including 111 LNM‐positive and 168 LVSI‐positive, divided into training, internal, and external test cohorts of 398, 169, and 54 patients, respectively. Field Strength/Sequence T2‐weighted imaging (T2WI), contrast‐enhanced T1WI (CE‐T1WI), and diffusion‐weighted imaging (DWI) were scanned with turbo spin‐echo, gradient‐echo, and two‐dimensional echo‐planar sequences, using either a 1.5 T or 3 T system. Assessment EC lesions were manually delineated on T2WI by two radiologists and used to train an nnU‐Net model for automatic segmentation. A multi‐task DL model was developed to simultaneously identify LNM and LVSI positive status using the segmented EC lesion regions and T2WI, CE‐T1WI, and DWI images as inputs. The performance of the model for LNM‐positive diagnosis was compared with those of three radiologists in the external test cohort. Statistical Tests Dice similarity coefficient (DSC) was used to evaluate segmentation results. Receiver Operating Characteristic (ROC) analysis was used to assess the performance of LNM and LVSI status identification. P value <0.05 was considered significant. Results EC lesion segmentation model achieved mean DSC values of 0.700 ± 0.25 and 0.693 ± 0.21 in the internal and external test cohorts, respectively. For LNM positive/LVSI positive identification, the proposed model achieved AUC values of 0.895/0.848, 0.806/0.795, and 0.804/0.728 in the training, internal, and external test cohorts, respectively, and better than those of three radiologists (AUC = 0.770/0.648/0.674). Data Conclusion The proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning. Evidence Level 3 Technical Efficacy Stage 2