Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, China.
CAS Key Laboratory of Molecular Imaging, Beijing Key Laboratory of Molecular Imaging, the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Cancer Med. 2023 Jan;12(2):1051-1063. doi: 10.1002/cam4.4953. Epub 2022 Jun 28.
To develop and validate a deep learning-based pathological risk score (RS) with an aim of predicting patients' prognosis to investigate the potential association between the information within the whole slide image (WSI) and cervical cancer prognosis.
A total of 251 patients with the International Federation of Gynecology and Obstetrics (FIGO) Stage IA1-IIA2 cervical cancer who underwent surgery without any preoperative treatment were enrolled in this study. Both the clinical characteristics and WSI of each patient were collected. To construct a prognosis-associate RS, high-dimensional pathological features were extracted using a convolutional neural network with an autoencoder. With the score threshold selected by X-tile, Kaplan-Meier survival analysis was applied to verify the prediction performance of RS in overall survival (OS) and disease-free survival (DFS) in both the training and testing datasets, as well as different clinical subgroups.
For the OS and DFS prediction in the testing cohort, RS showed a Harrell's concordance index of higher than 0.700, while the areas under the curve (AUC) achieved up to 0.800 in the same cohort. Furthermore, Kaplan-Meier survival analysis demonstrated that RS was a potential prognostic factor, even in different datasets or subgroups. It could further distinguish the survival differences after clinicopathological risk stratification.
In the present study, we developed an effective signature in cervical cancer for prognosis prediction and patients' stratification in OS and DFS.
开发并验证一种基于深度学习的病理风险评分（RS），旨在预测患者的预后，以研究全切片图像（WSI）内信息与宫颈癌预后之间的潜在关联。
本研究共纳入 251 例国际妇产科联合会（FIGO）IA1-IIA2 期宫颈癌患者，这些患者在术前未接受任何治疗即接受了手术。收集每位患者的临床特征和 WSI。为构建预后相关 RS，使用具有自动编码器的卷积神经网络提取高维病理特征。通过 X-tile 选择评分阈值，对 RS 在训练和测试数据集以及不同临床亚组中的总生存期（OS）和无病生存期（DFS）的预测性能进行 Kaplan-Meier 生存分析验证。
对于测试队列的 OS 和 DFS 预测，RS 的 Harrell 一致性指数高于 0.700，而在同一队列中的曲线下面积（AUC）达到 0.800。此外，Kaplan-Meier 生存分析表明，RS 是一个潜在的预后因素，即使在不同的数据集或亚组中也是如此。它可以进一步区分临床病理风险分层后的生存差异。
在本研究中，我们开发了一种针对宫颈癌 OS 和 DFS 预后预测和患者分层的有效特征。
