Zhang Juan, Su Chao, Zhang Yuyang, Gao Rongji, Lu Xiaomei, Liang Jing, Liu Haiwei, Tian Song, Zhang Yitao, Ye Zhaoxiang
Department of Radiology, Tianjin Medical University Cancer Institute and Hospital; National Clinical Research Center for Cancer; Tianjin's Clinical Research Center for Cancer; Key Laboratory of Cancer Prevention and Therapy; Tianjin Key Laboratory of Digestive Cancer; State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine, Tianjin, China, Tianjin, China.
Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Shandong, China.
Eur Radiol. 2025 May;35(5):2794-2805. doi: 10.1007/s00330-024-11163-y. Epub 2024 Nov 13.
To develop a nomogram based on clinical features and spectral quantitative parameters to preoperatively predict the Lauren classification for locally advanced gastric cancer (LAGC).
Patients diagnosed with LAGC by postoperative pathology who underwent abdominal triple-phase enhanced spectral computed tomography (CT) were prospectively enrolled in this study between June 2023 and December 2023. All the patients were categorized into intestinal- and diffuse-type groups according to the Lauren classification. Traditional characteristics, including demographic information, serum tumor markers, gastroscopic pathology, and image semantic features, were collected. Spectral quantitative parameters, including iodine concentration (IC), effective atomic number (Zeff), and slope of the energy spectrum curve from 40 keV to 70 keV (λ), were measured three times for each patient by two blinded radiologists in arterial/venous/delayed phases (AP/VP/DP). Differences in traditional features and spectral quantitative parameters between the two groups were compared using univariable analysis. Independent predictors of the Lauren classification of LAGC were screened using multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve analysis was used to assess the discriminating capability. Ultimately, the nomogram, including clinical features and spectral CT quantitative parameters, was developed.
Gender, nIC in AP (APnIC), and λ in DP (λd) were independent predictors for Lauren classification. The nomogram based on these indicators produced the best performance with an area under the curve of 0.841 (95% confidence interval: 0.749-0.932), specificity of 85.3%, accuracy of 76.4%, and sensitivity of 68.4%.
The nomogram based on clinical features and spectral CT quantitative parameters exhibits great potential in the preoperative and non-invasive assessment of Lauren classification for LAGC.
Question Can the proposed nomogram, integrating clinical features and spectral quantitative parameters, preoperatively predict the Lauren classification in locally advanced gastric cancer (LAGC)? Findings The nomogram, based on gender, arterial phase normalized iodine concentration, and slope of the energy spectrum curve in the delayed phase showed satisfactory predictive ability. Clinical relevance The established nomogram could contribute to guiding individualized treatment strategies and risk stratification in patients by predicting the Lauren classification for LAGC before surgery.
基于临床特征和光谱定量参数开发一种列线图，以术前预测局部进展期胃癌（LAGC）的劳伦分类。
2023年6月至2023年12月期间，前瞻性纳入术后病理诊断为LAGC且接受腹部三期增强光谱计算机断层扫描（CT）的患者。所有患者根据劳伦分类分为肠型和弥漫型组。收集传统特征，包括人口统计学信息、血清肿瘤标志物、胃镜病理和图像语义特征。两名盲法放射科医生在动脉期/静脉期/延迟期（AP/VP/DP）对每位患者的光谱定量参数进行三次测量，包括碘浓度（IC）、有效原子序数（Zeff）以及40 keV至70 keV能量谱曲线的斜率（λ）。采用单因素分析比较两组之间传统特征和光谱定量参数的差异。使用多因素逻辑回归分析筛选LAGC劳伦分类的独立预测因素。采用受试者操作特征（ROC）曲线分析评估鉴别能力。最终，开发出包括临床特征和光谱CT定量参数的列线图。
性别、动脉期归一化碘浓度（APnIC）和延迟期斜率（λd）是劳伦分类的独立预测因素。基于这些指标的列线图表现最佳，曲线下面积为0.841（95%置信区间：0.749 - 0.932），特异性为85.3%，准确率为76.4%，灵敏度为68.4%。
基于临床特征和光谱CT定量参数的列线图在术前对LAGC劳伦分类的无创评估中具有巨大潜力。
问题 所提出的整合临床特征和光谱定量参数的列线图能否术前预测局部进展期胃癌（LAGC）的劳伦分类？ 发现 基于性别、动脉期归一化碘浓度和延迟期能量谱曲线斜率的列线图显示出令人满意的预测能力。 临床意义 所建立的列线图可通过术前预测LAGC的劳伦分类，有助于指导患者的个体化治疗策略和风险分层。
