Cao Mengxuan, Hu Can, Li Feng, He Jingyang, Li Enze, Zhang Ruolan, Shi Wenyi, Zhang Yanqiang, Zhang Yu, Yang Qing, Zhao Qianyu, Shi Lei, Xu Zhiyuan, Cheng Xiangdong
Department of Gastric Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang.
Key Laboratory of Prevention, Diagnosis and Therapy of Upper Gastrointestinal Cancer of Zhejiang Province, Hangzhou.
Int J Surg. 2024 Dec 1;110(12):7598-7606. doi: 10.1097/JS9.0000000000001627.
The postoperative recurrence of gastric cancer (GC) has a significant impact on the overall prognosis of patients. Therefore, accurately predicting the postoperative recurrence of GC is crucial.
This retrospective study gathered data from 2813 GC patients who underwent radical surgery between 2011 and 2017 at two medical centers. Follow-up was extended until May 2023, and cases were categorized as recurrent or nonrecurrent based on postoperative outcomes. Clinical pathological information and imaging data were collected for all patients. A new deep learning signature (DLS) was generated using pretreatment computed tomography images, based on a pretrained baseline (a customized Resnet50), for predicting postoperative recurrence. The deep learning fusion signature (DLFS) was created by combining the score of DLS with the weighted values of identified clinical features. The predictive performance of the model was evaluated based on discrimination, calibration, and clinical usefulness. Survival curves were plotted to investigate the differences between DLFS and prognosis.
In this study, 2813 patients with GC were recruited and allocated into training, internal validation, and external validation cohorts. The DLFS was developed and assessed for its capability in predicting the risk of postoperative recurrence. The DLFS exhibited excellent performance with AUCs of 0.833 (95% CI: 0.809-0.858) in the training set, 0.831 (95% CI: 0.792-0.871) in the internal validation set, and 0.859 (95% CI: 0.806-0.912) in the external validation set, along with satisfactory calibration across all cohorts ( P >0.05). Furthermore, the DLFS model significantly outperformed both the clinical model and DLS ( P <0.05). High-risk recurrent patients exhibit a significantly poorer prognosis compared to low-risk recurrent patients ( P <0.05).
The integrated model developed in this study, focusing on GC patients undergoing radical surgery, accurately identifies cases at high-risk of postoperative recurrence and highlights the potential of DLFS as a prognostic factor for GC patients.
胃癌（GC）术后复发对患者的总体预后有重大影响。因此，准确预测GC术后复发至关重要。
这项回顾性研究收集了2011年至2017年期间在两个医疗中心接受根治性手术的2813例GC患者的数据。随访延长至2023年5月，根据术后结果将病例分为复发或未复发。收集了所有患者的临床病理信息和影像数据。基于预训练基线（定制的Resnet50），使用术前计算机断层扫描图像生成了一种新的深度学习特征（DLS），用于预测术后复发。通过将DLS评分与已识别临床特征的加权值相结合，创建了深度学习融合特征（DLFS）。基于区分度、校准度和临床实用性对模型的预测性能进行评估。绘制生存曲线以研究DLFS与预后之间的差异。
在本研究中，招募了2813例GC患者，并将其分配到训练、内部验证和外部验证队列中。开发并评估了DLFS预测术后复发风险的能力。DLFS表现出优异的性能，训练集中的AUC为0.833（95%CI：0.809 - 0.858），内部验证集中为0.831（95%CI：0.792 - 0.871），外部验证集中为0.859（95%CI：0.806 - 0.912），并且在所有队列中校准度均令人满意（P>0.05）。此外，DLFS模型显著优于临床模型和DLS（P<0.05）。与低风险复发患者相比，高风险复发患者的预后明显更差（P<0.05）。
本研究中开发的综合模型聚焦于接受根治性手术的GC患者，准确识别出术后复发高风险病例，并突出了DLFS作为GC患者预后因素的潜力。
