School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.
CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Med Phys. 2024 Jan;51(1):267-277. doi: 10.1002/mp.16647. Epub 2023 Aug 13.
The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet.
This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value.
A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS).
The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis.
The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.
越来越多的研究证实了结外软组织转移（ESTM）对胃癌（GC）的潜在预后价值。然而，ESTM 的金标准是通过手术后的病理检查确定的，目前还没有术前评估 ESTM 的方法。
本多中心研究旨在开发一种基于深度学习的放射组学模型，以便术前识别 ESTM 并评估其预后价值。
从两个中心共纳入 959 例 GC 患者，分为训练队列（N=551）和测试队列（N=236）进行 ESTM 评估。此外，还纳入了一个外部生存队列（N=172）进行预后分析。基于临床特征和多期 CT 图像建立了 4 种模型，用于术前识别 ESTM，包括深度学习模型、手工制作的放射组学模型、临床模型和联合模型。利用 C 指数、决策曲线和校准曲线评估模型性能。进行生存分析以探索分层总生存（OS）的能力。
联合模型对 ESTM 的区分度较好[训练和测试队列的 C 指数（95%置信区间，CI）分别为 0.770（0.729-0.812）和 0.761（0.718-0.805）]，优于深度学习模型、放射组学模型和临床模型。分层分析表明，该模型不受患者肿瘤大小、存在血管淋巴管侵犯和 Lauren 分类的影响（p<0.05）。此外，模型评分与 OS 具有很强的一致性[内部生存队列的 C 指数（95%CI）为 0.723（0.658-0.789，p<0.0001），外部生存队列为 0.715（0.650-0.779，p<0.0001）]。更有趣的是，单因素分析显示，模型评分与术前诊断遗漏的隐匿远处转移显著相关（p<0.05）。
联合 CT 图像和临床特征的模型对 ESTM 和预后均具有出色的预测能力，有可能成为术前 TNM 分期系统的有效补充。
