Department of General Surgery, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, Fujian, People's Republic of China.
JAMA Surg. 2019 Mar 1;154(3):e185249. doi: 10.1001/jamasurg.2018.5249. Epub 2019 Mar 20.
Lymph node status is the primary determinant in treatment decision making in early gastric cancer (EGC). Current evaluation methods are not adequate for estimating lymph node metastasis (LNM) in EGC.
To develop and validate a prediction model based on a fully quantitative collagen signature in the tumor microenvironment to estimate the individual risk of LNM in EGC.
DESIGN, SETTING, AND PARTICIPANTS: This retrospective study was conducted from August 1, 2016, to May 10, 2018, at 2 medical centers in China (Nanfang Hospital and Fujian Provincial Hospital). Participants included a primary cohort (n = 232) of consecutive patients with histologically confirmed gastric cancer who underwent radical gastrectomy and received a T1 gastric cancer diagnosis from January 1, 2008, to December 31, 2012. Patients with neoadjuvant radiotherapy, chemotherapy, or chemoradiotherapy were excluded. An additional consecutive cohort (n = 143) who received the same diagnosis from January 1, 2011, to December 31, 2013, was enrolled to provide validation. Baseline clinicopathologic data of each patient were collected. Collagen features were extracted in specimens using multiphoton imaging, and the collagen signature was constructed. An LNM prediction model based on the collagen signature was developed and was internally and externally validated.
The area under the receiver operating characteristic curve (AUROC) of the prediction model and decision curve were analyzed for estimating LNM.
In total, 375 patients were included. The primary cohort comprised 232 consecutive patients, in whom the LNM rate was 16.4% (n = 38; 25 men [65.8%] with a mean [SD] age of 57.82 [10.17] years). The validation cohort consisted of 143 consecutive patients, in whom the LNM rate was 20.9% (n = 30; 20 men [66.7%] with a mean [SD] age of 54.10 [13.19] years). The collagen signature was statistically significantly associated with LNM (odds ratio, 5.470; 95% CI, 3.315-9.026; P < .001). Multivariate analysis revealed that the depth of tumor invasion, tumor differentiation, and the collagen signature were independent predictors of LNM. These 3 predictors were incorporated into the new prediction model, and a nomogram was established. The model showed good discrimination in the primary cohort (AUROC, 0.955; 95% CI, 0.919-0.991) and validation cohort (AUROC, 0.938; 95% CI, 0.897-0.981). An optimal cutoff value was selected in the primary cohort, which had a sensitivity of 86.8%, a specificity of 93.3%, an accuracy of 92.2%, a positive predictive value of 71.7%, and a negative predictive value of 97.3%. The validation cohort had a sensitivity of 90.0%, a specificity of 90.3%, an accuracy of 90.2%, a positive predictive value of 71.1%, and a negative predictive value of 97.1%. Among the 375 patients, a sensitivity of 87.3%, a specificity of 92.1%, an accuracy of 91.2%, a positive predictive value of 72.1%, and a negative predictive value of 96.9% were found.
This study's findings suggest that the collagen signature in the tumor microenvironment is an independent indicator of LNM in EGC, and the prediction model based on this collagen signature may be useful in treatment decision making for patients with EGC.
淋巴结状态是早期胃癌（EGC）治疗决策的主要决定因素。目前的评估方法不足以评估 EGC 中的淋巴结转移（LNM）。
开发和验证一种基于肿瘤微环境中完全定量胶原特征的预测模型，以估计 EGC 中 LNM 的个体风险。
设计、地点和参与者：这是一项回顾性研究，于 2016 年 8 月 1 日至 2018 年 5 月 10 日在中国的 2 家医疗中心（南方医院和福建省医院）进行。参与者包括一个主要队列（n=232），由 232 名经组织学证实患有胃癌且接受根治性胃切除术并从 2008 年 1 月 1 日至 2012 年 12 月 31 日诊断为 T1 胃癌的连续患者组成。排除接受新辅助放疗、化疗或放化疗的患者。招募了另一个连续队列（n=143），这些患者在 2011 年 1 月 1 日至 2013 年 12 月 31 日期间接受了相同的诊断，以提供验证。收集每位患者的基线临床病理数据。使用多光子成像技术从标本中提取胶原特征，并构建胶原特征。基于胶原特征开发了 LNM 预测模型，并进行了内部和外部验证。
分析预测模型和决策曲线的接收者操作特征曲线（AUROC），以评估 LNM 的估计值。
共有 375 名患者入选。主要队列包括 232 名连续患者，其中 LNM 率为 16.4%（n=38；25 名男性[65.8%]，平均[SD]年龄为 57.82[10.17]岁）。验证队列包括 143 名连续患者，其中 LNM 率为 20.9%（n=30；20 名男性[66.7%]，平均[SD]年龄为 54.10[13.19]岁）。胶原特征与 LNM 有统计学显著相关性（比值比，5.470；95%CI，3.315-9.026；P<.001）。多变量分析显示，肿瘤浸润深度、肿瘤分化和胶原特征是 LNM 的独立预测因素。这 3 个预测因素被纳入新的预测模型，并建立了一个列线图。该模型在主要队列中显示出良好的鉴别力（AUROC，0.955；95%CI，0.919-0.991）和验证队列（AUROC，0.938；95%CI，0.897-0.981）。在主要队列中选择了一个最佳截断值，该值具有 86.8%的敏感性、93.3%的特异性、92.2%的准确性、71.7%的阳性预测值和 97.3%的阴性预测值。验证队列的敏感性为 90.0%，特异性为 90.3%，准确性为 90.2%，阳性预测值为 71.1%，阴性预测值为 97.1%。在 375 名患者中，敏感性为 87.3%，特异性为 92.1%，准确性为 91.2%，阳性预测值为 72.1%，阴性预测值为 96.9%。
本研究结果表明，肿瘤微环境中的胶原特征是 EGC 中 LNM 的独立指标，基于该胶原特征的预测模型可能有助于 EGC 患者的治疗决策。
