Chang Rui, Li Ting, Ma Xiaowei
Department of Obstetrics and Gynecology, The First Hospital of Yulin, Yulin, 719000, Shaanxi, China.
Cancer Diagnosis and Treatment Center, The First Hospital of Yulin, Yulin, 719000, Shaanxi, China.
Open Life Sci. 2024 Jun 11;19(1):20220733. doi: 10.1515/biol-2022-0733. eCollection 2024.
The aim of this research is to explore the application value of Deep residual network model (DRN) for deep learning-based multi-sequence magnetic resonance imaging (MRI) in the staging diagnosis of cervical cancer (CC). This research included 90 patients diagnosed with CC between August 2019 and May 2021 at the hospital. After undergoing MRI examination, the clinical staging and surgical pathological staging of patients were conducted. The research then evaluated the results of clinical staging and MRI staging to assess their diagnostic accuracy and correlation. In the staging diagnosis of CC, the feature enhancement layer was added to the DRN model, and the MRI imaging features of CC were used to enhance the image information. The precision, specificity, and sensitivity of the constructed model were analyzed, and then the accuracy of clinical diagnosis staging and MRI staging were compared. As the model constructed DRN in this research was compared with convolutional neural network (CNN) and the classic deep neural network visual geometry group (VGG), the precision was 67.7, 84.9, and 93.6%, respectively. The sensitivity was 70.4, 82.5, and 91.2%, while the specificity was 68.5, 83.8, and 92.2%, respectively. The precision, sensitivity, and specificity of the model were remarkably higher than those of CNN and VGG models ( < 0.05). As the clinical staging and MRI staging of CC were compared, the diagnostic accuracy of MRI was 100%, while that of clinical diagnosis was 83.7%, showing a significant difference between them ( < 0.05). Multi-sequence MRI under intelligent algorithm had a high diagnostic rate for CC staging, deserving a good clinical application value.
本研究旨在探讨深度残差网络模型（DRN）在基于深度学习的多序列磁共振成像（MRI）用于宫颈癌（CC）分期诊断中的应用价值。本研究纳入了2019年8月至2021年5月期间在该医院确诊为CC的90例患者。患者接受MRI检查后，进行了临床分期和手术病理分期。然后，该研究评估了临床分期和MRI分期的结果，以评估其诊断准确性和相关性。在CC的分期诊断中，在DRN模型中添加了特征增强层，并利用CC的MRI成像特征增强图像信息。分析了构建模型的精度、特异性和敏感性，然后比较了临床诊断分期和MRI分期的准确性。由于本研究构建的DRN模型与卷积神经网络（CNN）和经典深度神经网络视觉几何组（VGG）进行了比较，其精度分别为67.7%、84.9%和93.6%。敏感性分别为70.4%、82.5%和91.2%，而特异性分别为68.5%、83.8%和92.2%。该模型的精度、敏感性和特异性显著高于CNN和VGG模型（<0.05）。比较CC的临床分期和MRI分期，MRI的诊断准确性为100%，而临床诊断的诊断准确性为83.7%，两者之间存在显著差异（<0.05）。智能算法下的多序列MRI对CC分期具有较高的诊断率，具有良好的临床应用价值。
