Construction of the preoperative staging prediction model for cervical cancer based on deep learning and MRI: a retrospective study.

Background: Cervical cancer remains a significant global health concern, particularly for women. Accurate preoperative staging is crucial for treatment planning and long-term prognosis. Traditional staging methods rely on manual imaging analysis, which is subjective and time-consuming. Deep learning-based automated staging models offer a promising approach to enhance both accuracy and efficiency. Methods: This study retrospectively analyzed preoperative MRI scans (T1 and T2 stages) from 112 cervical cancer patients. Seven deep learning models-DenseNet, FBNet, HRNet, RegNet, ResNet50, ShuffleNet, and ViT-were trained and validated using standardized preprocessing, data augmentation, and manual annotation techniques. Convolutional neural networks were employed to extract multidimensional imaging features, forming the basis of an automated staging prediction model. Results: Among all tested models, HRNet demonstrated the best performance, achieving an accuracy of 69.70%, recall of 68.89%, F1-score of 68.98%, and precision of 69.62%. ShuffleNet ranked second, with slightly lower performance, while ViT exhibited the weakest predictive ability. The ROC curve analysis confirmed HRNet's superior classification capability, with an AUC of 0.7778, highlighting its effectiveness in small-sample datasets. Conclusion: This study confirms that deep learning models utilizing MRI images can enable automated cervical cancer staging with improved accuracy and efficiency. HRNet, in particular, demonstrates strong potential as a clinical decision-support tool, contributing to the advancement of precision medicine and personalized treatment strategies for cervical cancer.