Deep Learning Improves Diagnostic Performance of Lymph Node Metastasis in Cervical Cancer by Using Magnetic Resonance Imaging

Abstract

Background: Accurate identification of lymph node metastasis (LNM) preoperatively in patients with cervical cancer (CC) can avoid unnecessary surgical intervention and benefit treatment planning. In this study, we aimed to develop a deep learning (DL) model using magnetic resonance imaging (MRI) for non-invasive and preoperative LNM prediction in CC. Methods: This research retrospectively enrolled 479 patients with CC from three centres, and divided patients into a primary cohort (n = 338 from two centres) and an independent validation cohort (n = 141 from another centre). We proposed an end-to-end DL model to identify LNM in CC using MRI, compared the performance among three MRI sequences, and explored the effect of intratumoural and peritumoural regions. Moreover, we built a hybrid model to combine the DL model and clinical information, and assessed its prognostic value in predicting disease-free survival (DFS) of CC by Kaplan-Meier analysis. Findings: Among the three sequences, the DL model which used contrast-enhanced T1-weighted imaging and combined both intratumoural and peritumoural regions (defined as CET1WItumour+peri) showed the best performance (AUC = 0.844 in the validation cohort, P < 0.0001 between the positive and negative LNM patients). These results were further improved in the hybrid model that combined CET1WItumour+peri; and clinical lymph node status (AUC = 0.933 in the validation cohort). Moreover, the H-score from the hybrid model was significantly associated with DFS of CC. Interpretation: DL improves the diagnostic performance of LNM in patients with CC. The hybrid model can be served as an easy-to-use tool to diagnose LNM preoperatively and non-invasively. Funding Statement: This paper is supported by the National Natural Science Foundation of China [Grant No. 81922040, 81930053, 81227901, 61702087]; the Beijing Natural Science Foundation [Grant No. 7182109]; the National Key RD the Strategic Priority Research Program of Chinese Academy of Sciences [Grant No. XDB32030200, XDB01030200]; the Youth Innovation Promotion Association CAS [Grant No. 2019136]. Declaration of Interests: The authors declare no conflicts of interest. Ethics Approval Statement: Ethical approval was obtained for this retrospective study at every participating centre, and the informed consent from patients was waived.