我们的目的是使用基线和再分期增强计算机断层扫描 (CT) 图像和临床特征构建和验证深度学习 (DL) 放射组学列线图，以预测局部晚期胃癌 (LAGC) 转移淋巴结对新辅助化疗 (NACT) 的反应我们前瞻性地招募了 112 名在 2021 年 1 月至 2022 年 8 月期间接受 NACT 的 LAGC 患者。应用纳入和排除标准后，98 名患者按 7:3 随机分配到训练队列 (n = 68) 和验证队列 (n = 30) 。我们基于 NACT 前后 CT 图像的三个阶段建立并比较了三个放射组学特征，即放射组学基线、放射组学增量和放射组学重新阶段。然后，我们开发了临床模型、DL 模型和列线图来预测 NACT 后 LAGC 的反应。我们分别使用受试者工作特征曲线和决策曲线分析评估每个模型的预测准确性和临床有效性。放射组学-delta 特征是三个放射组学特征中最好的预测因子。因此，我们开发并验证了 DL delta 放射组学列线图 (DLDRN)。在验证队列中，DLDRN 产生的受试者工作曲线下面积为 0.94（95% 置信区间，0.82-0.96），并证明了对 NACT 良好反应的充分区分。此外，DLDRN 显着优于临床模型和 DL 模型 (p<0.001)。通过决策曲线分析证实了 DLDRN 的临床实用性。在 LAGC 患者中，DLDRN 有效预测了转移淋巴结的治疗反应，可为个体化治疗提供有价值的信息。© 2023。外科肿瘤学会。

We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC).We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively.The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis.In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.© 2023. Society of Surgical Oncology.