Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of China, Suzhou, 215163, Jiangsu, China.
Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, Jiangsu, China.
Gastric Cancer. 2022 Nov;25(6):1050-1059. doi: 10.1007/s10120-022-01328-3. Epub 2022 Aug 6.
Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients.
A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC).
The DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05).
A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.
准确预测局部晚期胃癌（LAGC）患者新辅助化疗（NACT）耐药性对于及时手术和优化治疗至关重要。我们旨在评估深度学习（DL）在 CT 图像上预测 LAGC 患者 NACT 耐药性的有效性。
本回顾性研究纳入了来自三家医院的 633 例接受 NACT 的 LAGC 患者。训练和内部验证队列是从中心 1 中随机选择的，分别包含 242 例和 104 例患者。外部验证队列 1 包含中心 2 的 128 例患者，外部验证队列 2 包含中心 3 的 159 例患者。首先，使用 ResNet-50 开发了一种 DL 模型来预测 LAGC 患者的 NACT 耐药性，并评估了梯度加权类激活映射（Grad-CAM）的可视化效果。然后，通过结合 DL 特征和临床特征构建了一个集成模型。最后，使用受试者工作特征（ROC）曲线下面积（AUC）在内部和外部验证队列中测试了性能。
DL 模型在验证队列中的 AUC 分别为 0.808（95%CI 0.724-0.893）、0.755（95%CI 0.660-0.850）和 0.752（95%CI 0.678-0.825），均高于临床模型。此外，集成模型的表现明显优于临床模型（P<0.05）。
基于 CT 的 DL 模型在预测 LAGC 患者 NACT 耐药性方面表现出良好的性能，可为个体化治疗提供有价值的信息。
