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

Author:

Wang Yida1ORCID,Liu Wei2,Lu Yuanyuan3,Ling Rennan4,Wang Wenjing3,Li Shengyong1ORCID,Zhang Feiran5,Ning Yan5,Chen Xiaojun2,Yang Guang1ORCID,Zhang He6ORCID

Affiliation:

1. Shanghai Key Laboratory of Magnetic Resonance East China Normal University Shanghai China

2. Department of Gynecology, Obstetrics and Gynecology Hospital Fudan University Shanghai China

3. Department of Radiology, Shanghai First Maternity and Infant Hospital, School of Medicine Tongji University Shanghai China

4. Department of Radiology, Shenzhen People's Hospital, Second Clinical Medical College of Jinan University First Affiliated Hospital of Southern University of Science and Technology Shanghai China

5. Department of Pathology, Obstetrics and Gynecology Hospital Fudan University Shanghai China

6. Department of Radiology, Obstetrics and Gynecology Hospital Fudan University Shanghai China

Abstract

BackgroundEarly 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.PurposeTo develop a deep learning (DL) model to simultaneously identify of LNM and LVSI of EC from mpMRI images.Study TypeRetrospective.PopulationSix 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/SequenceT2‐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.AssessmentEC 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 TestsDice 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.ResultsEC 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 ConclusionThe proposed model has potential to help clinicians to identify LNM and LVSI status of EC patients and improve treatment planning.Evidence Level3Technical EfficacyStage 2

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Publisher

Wiley

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