基于磁共振成像（MRI）的放射组学方法和深度学习方法在宫颈腺癌（AC）中的作用尚未被探索。在此，我们的目标是开发基于 MRI 放射组学和 AC 患者临床特征的预后预测模型。收集并分析了 197 例宫颈 AC 患者的临床和病理信息。对于每位患者，从 T2 加权 MRI 图像中提取了 107 个放射组学特征。使用 Spearman 相关性和随机森林 (RF) 算法进行特征选择，并使用支持向量机 (SVM) 技术构建预测模型。深度学习模型还通过卷积神经网络 (CNN) 使用 T2 加权 MRI 图像和临床病理特征进行训练。使用显着特征分析卡普兰-迈耶曲线。此外，还使用另一组56例AC患者的信息进行独立验证。总共107个放射组学特征和6个临床病理特征（年龄、FIGO分期、分化、浸润深度、淋巴管间隙侵犯（LVSI）和淋巴结转移） (LNM) 被纳入分析中。在预测 3 年、4 年和 5 年 DFS 时，仅根据放射组学特征训练的模型获得的 AUC 值分别为 0.659 (95% CI: 0.620-0.716)、0.791 (分别为 95%CI：0.603-0.922）和 0.853（95%CI：0.745-0.912），然而，结合放射组学和临床病理学特征的组合模型的 AUC 值为 0.934（95%CI：0.934），优于放射组学模型。对于深度学习模型，基于 MRI 的模型的 AUC 分别为 0.857、0.777 和 0.828。分别进行3年DFS、4年DFS和5年DFS预测，并且组合的深度学习模型获得了改进的性能，AUC为0.903。 0.862 和 0.969。在独立测试集中，组合模型的 3 年 DFS、4 年 DFS 和 5 年 DFS 预测的 AUC 分别为 0.873、0.858 和 0.914。我们证明了整合基于 MRI 的放射组学和临床病理学的预后价值宫颈腺癌的特点。与临床数据结合时，放射组学和深度学习模型都显示出改进的预测性能，强调了多模式方法在患者管理中的重要性。© 2024。作者。

The roles of magnetic resonance imaging (MRI) -based radiomics approach and deep learning approach in cervical adenocarcinoma (AC) have not been explored. Herein, we aim to develop prognosis-predictive models based on MRI-radiomics and clinical features for AC patients.Clinical and pathological information from one hundred and ninety-seven patients with cervical AC was collected and analyzed. For each patient, 107 radiomics features were extracted from T2-weighted MRI images. Feature selection was performed using Spearman correlation and random forest (RF) algorithms, and predictive models were built using support vector machine (SVM) technique. Deep learning models were also trained with T2-weighted MRI images and clinicopathological features through Convolutional Neural Network (CNN). Kaplan-Meier curve was analyzed using significant features. In addition, information from another group of 56 AC patients was used for the independent validation.A total of 107 radiomics features and 6 clinicopathological features (age, FIGO stage, differentiation, invasion depth, lymphovascular space invasion (LVSI), and lymph node metastasis (LNM) were included in the analysis. When predicting the 3-year, 4-year, and 5-year DFS, the model trained solely on radiomics features achieved AUC values of 0.659 (95%CI: 0.620-0.716), 0.791 (95%CI: 0.603-0.922), and 0.853 (95%CI: 0.745-0.912), respectively. However, the combined model, incorporating both radiomics and clinicopathological features, outperformed the radiomics model with AUC values of 0.934 (95%CI: 0.885-0.981), 0.937 (95%CI: 0.867-0.995), and 0.916 (95%CI: 0.857-0.970), respectively. For deep learning models, the MRI-based models achieved an AUC of 0.857, 0.777 and 0.828 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively. And the combined deep learning models got a improved performance, the AUCs were 0.903. 0.862 and 0.969. In the independent test set, the combined model achieved an AUC of 0.873, 0.858 and 0.914 for 3-year DFS, 4-year DFS and 5-year DFS prediction, respectively.We demonstrated the prognostic value of integrating MRI-based radiomics and clinicopathological features in cervical adenocarcinoma. Both radiomics and deep learning models showed improved predictive performance when combined with clinical data, emphasizing the importance of a multimodal approach in patient management.© 2024. The Author(s).