Xu Yitian, Yang Yan, Cheng Feichi, Luo Zai, Zhang Yuan, Zhang Pengshan, Qiu Jiahui, Qiu Zhengjun, Huang Chen
Department of Gastrointestinal Surgery, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Hongkou District, Shanghai, P. R. China.
Department of Gastrointestinal Surgery, First Affiliated Hospital of Bengbu Medical College, Bengbu, Anhui, P. R. China.
Gastroenterol Rep (Oxf). 2024 Oct 11;12:goae083. doi: 10.1093/gastro/goae083. eCollection 2024.
Tumor-stroma percentage (TSP) is a prognostic risk factor in numerous solid tumors. Despite this, the prognostic significance of TSP in gastric cancer (GC) remains underexplored. Through the development of a personalized predictive model and a semi-automatic identification system, our study aimed to fully unlock the predictive potential of TSP in GC.
We screened GC patients from Shanghai General Hospital (SGH) between 2012 and 2019 to develop and validate a nomogram. Univariate and multivariate Cox proportional hazards regression analyses were employed to identify independent prognostic factors influencing the prognosis for GC patients. The nomogram was further validated externally by using a cohort from Bengbu Medical College (BMC). All patients underwent radical gastrectomy, with those diagnosed with locally advanced GC receiving adjuvant chemotherapy. The primary outcome measured was overall survival (OS). The semi-automatic identification of the TSP was achieved through a computer-aided detection (CAD) system, denoted as TSP-cad, while TSP identified by pathologists was labeled as TSP-visual.
A total of 813 GC patients from SGH and 59 from BMC were enrolled in our study. TSP-visual was identified as an adverse prognostic factor for OS in GC and was found to be associated with pathological Tumor Node Metastasis staging system (pTNM) stage, T stage, N stage, perineural invasion (PNI), lymphovascular invasion (LVI), TSP-visual, tumor size, and other factors. Multivariate Cox regression using the training cohort revealed that TSP-visual (hazard ratio [HR], 2.042; 95% confidential interval [CI], 1.485-2.806; <0.001), N stage (HR, 2.136; 95% CI, 1.343-3.397; =0.010), PNI (HR , 1.791; 95% CI, 1.270-2.526; =0.001), and LVI (HR, 1.482; 95% CI, 1.021-2.152; =0.039) were independent predictors. These factors were incorporated into a novel nomogram, which exhibited strong predictive accuracy for 5-year OS in the training, internal validation, and external validation cohorts (area under the curve = 0.744, 0.759, and 0.854, respectively). The decision curve analysis of the nomogram and concordance indexes across the three cohorts outperformed the traditional pTNM (<0.05). Additionally, TSP-cad assessment using a rapid multi-dynamic algorithm demonstrated good agreement with TSP-visual.
The novel nomogram based on TSP could effectively identify individuals at risk of a poor prognosis among patients with GC. TSP-cad is anticipated to enhance the evaluation process of TSP.
肿瘤间质百分比（TSP）是众多实体瘤的预后风险因素。尽管如此，TSP在胃癌（GC）中的预后意义仍未得到充分探索。通过开发个性化预测模型和半自动识别系统，我们的研究旨在充分挖掘TSP在GC中的预测潜力。
我们筛选了2012年至2019年期间上海交通大学医学院附属瑞金医院（SGH）的GC患者，以开发和验证列线图。采用单因素和多因素Cox比例风险回归分析来确定影响GC患者预后的独立预后因素。通过使用蚌埠医学院（BMC）的队列对列线图进行外部验证。所有患者均接受了根治性胃切除术，被诊断为局部晚期GC的患者接受辅助化疗。测量的主要结局是总生存期（OS）。TSP的半自动识别通过计算机辅助检测（CAD）系统实现，称为TSP-cad，而病理学家识别的TSP标记为TSP-visual。
我们的研究共纳入了813例来自SGH的GC患者和59例来自BMC的患者。TSP-visual被确定为GC患者OS的不良预后因素，并且发现其与病理肿瘤淋巴结转移分期系统（pTNM）分期、T分期、N分期、神经周围侵犯（PNI）、淋巴管侵犯（LVI）、TSP-visual、肿瘤大小及其他因素相关。使用训练队列进行的多因素Cox回归显示，TSP-visual（风险比[HR]，2.042；95%置信区间[CI]，1.485-2.806；<0.001）、N分期（HR，2.136；95%CI，1.343-3.397；=0.010）、PNI（HR，1.791；95%CI，1.270-2.526；=0.001）和LVI（HR，1.482；95%CI，1.021-2.152；=0.039）是独立预测因素。这些因素被纳入一个新的列线图，该列线图在训练、内部验证和外部验证队列中对5年OS表现出很强的预测准确性（曲线下面积分别为0.744、0.759和0.854）。列线图的决策曲线分析和三个队列的一致性指数均优于传统的pTNM（<0.05）。此外，使用快速多动态算法的TSP-cad评估与TSP-visual显示出良好的一致性。
基于TSP的新型列线图可以有效地识别GC患者中预后不良风险的个体。预计TSP-cad将加强TSP的评估过程。
