CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Radiology Department, Peking University Cancer Hospital & Institute, Beijing, China.
Ann Oncol. 2020 Jul;31(7):912-920. doi: 10.1016/j.annonc.2020.04.003. Epub 2020 Apr 15.
Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative determination method is not accurate enough.
We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis.
The DLRN showed good discrimination of the number of LNM on all cohorts [overall C-indexes (95% confidence interval): 0.821 (0.785-0.858) in the primary cohort, 0.797 (0.771-0.823) in the external validation cohorts, and 0.822 (0.756-0.887) in the international validation cohort]. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n = 271).
A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.
术前评估淋巴结转移（LNM）数量是局部进展期胃癌（LAGC）个体化治疗的基础。然而，常规使用的术前确定方法不够准确。
我们从中国的五个中心和意大利的一个中心共纳入了 730 例 LAGC 患者，并将其分为一个主要队列、三个外部验证队列和一个国际验证队列。我们基于多期 CT 图像建立了深度学习放射组学列线图（DLRN），用于术前预测 LAGC 的 LNM 数量。我们全面测试了 DLRN，并与三种最先进的方法进行了比较。此外，我们还研究了 DLRN 在生存分析中的价值。
DLRN 在所有队列中均显示出良好的 LNM 数量区分能力[主要队列中整体 C 指数（95%置信区间）：0.821（0.785-0.858），外部验证队列中为 0.797（0.771-0.823），国际验证队列中为 0.822（0.756-0.887）]。列线图的表现明显优于常规使用的临床 N 分期、肿瘤大小和临床模型（P<0.05）。此外，DLRN 与 LAGC 患者的总生存显著相关（n=271）。
基于深度学习的放射组学列线图对 LAGC 的 LNM 具有良好的预测价值。在以分期为导向的胃癌治疗中，这种术前列线图可为 LAGC 的个体化治疗提供基线信息。
