Frontiers in oncology · 2025
Artificial intelligence-assisted noninvasive preoperative prediction of lymph nodes metastasis in cervical cancer through a clinical-imaging feature combined strategy.
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Zhang Jingjing, Fu Chunlong, Du Junqiang
Аннотация
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, fine-tuned 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 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.
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, fine-tuned 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 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.