ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gynecological Oncology
This article is part of the Research TopicRecent Advancements in AI-Assisted Gynecologic Cancer DetectionView all 5 articles
Artificial intelligence-assisted noninvasive preoperative prediction of lymph nodes metastasis in cervical cancer through a clinical-imaging feature combined strategy
Provisionally accepted
- Dongyang Hospital of Wenzhou Medical University, Dongyang, China
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Background: Lymph node metastasis (LNM) of patients with cervical cancer (CC) is correlated with noticeably reduced five-year survival rate. but the role of conventional detection is limited for preoperative diagnosis of LNM. Therefore, we intended to develop a predictive model for LNM by integrating medical images, clinical data along with artificial intelligence-assisted method. Methods: CC patients who underwent radical hysterectomy combined with pelvic lymphadenectomy between January 2013 and October 2024 were retrospectively enrolled in this study. For computed tomography (CT) and ultrasound (US) images, a pre-trained ResNet-18 model on large-scale samples was used to extract representative features, with random cropping data augmentation. For clinical indicators, after normalizing to the range [0,1], a multilayer perceptron block was applied to extract representative features. Then, contrastive learning and feature fusion methods were utilized to integrate similar messages. Finally, a multi-modal contrastive learning framework was developed by consolidating above two parts. The framework was estimated by accuracy, sensitivity, specificity and the area under the receiver operating characteristic curve (AUC). Results: This work consisted of 127 CT images of patients with pathologically diagnosed cervical malignancies. After integrating clinical-imaging feature and artificial intelligence-assisted 2 algorithm, the finally developed LNM predicting model achieved a high accuracy of 92.31% with an AUC of 0.88. Additionally, the model also displayed strong sensitivity (80.0%) and specificity (95.45%) in CC cohorts. Conclusion: This study presented an efficient noninvasive and highly accurate diagnostic tool for LNM, which may significantly enhance surgical decision-making for lymph node dissection in CC patients with LNM.
Keywords: cervical cancer, lymph node metastasis, artificial intelligence, multi-modal contrastive learning, predictive model
Received: 19 Jul 2025; Accepted: 03 Dec 2025.
Copyright: © 2025 Zhang, Fu and Du. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Junqiang Du
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