Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
Department of Gastrointestinal Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, Henan, China.
Eur J Surg Oncol. 2024 Apr;50(4):108020. doi: 10.1016/j.ejso.2024.108020. Epub 2024 Feb 13.
To establish a spectral CT-based nomogram for predicting early neoadjuvant chemotherapy (NAC) response for locally advanced gastric cancer (LAGC).
This study prospectively recruited 222 cases (177 male and 45 female patients, 9.59 ± 9.54 years) receiving NAC and radical gastrectomy. Triple enhanced spectral CT scans were performed before NAC initiation. According to post-operative tumor regression grade (TRG), patients were classified into responders (TRG = 0 + 1) or non-responders (TRG = 2 + 3), and split into a primary (156) and validation (66) dataset at 7:3 ratio chronologically. We compared clinicopathological data, follow-up information, iodine concentration (IC), normalized ICs (nICs) in arterial/venous/delayed phases (AP/VP/DP) between responders and non-responders. Independent risk factors of response were screened by multivariable logistic regression and adopted for model construction. Model was visualized by nomograms and its capability was determined through receiver operating characteristic (ROC) curves. Log-rank survival analysis was conducted to explore associations between TRG, nomogram and patients' survival.
This work identified Borrmann classification, ICDP, and nICDP were independent risk factors of response outcomes. A spectral CT-based nomogram was built accordingly and achieved an area under the curve (AUC) of 0.797 (0.692-0.879) and 0.741(0.661-0.811) for the primary and validation dataset, respectively, higher than AUC of individual parameters alone. The nomogram was related to disease-free survival in the validation dataset (Hazard ratio (HR): 5.19 [1.18-12.93], P = 0.02).
The spectral CT-based nomogram provides an efficient tool for predicting the pathologic response outcomes of GC after NAC and disease-free survival risk stratification.
建立基于光谱 CT 的列线图，以预测局部晚期胃癌（LAGC）新辅助化疗（NAC）的早期应答。
本研究前瞻性招募了 222 例（男性 177 例，女性 45 例，年龄 9.59±9.54 岁）接受 NAC 和根治性胃切除术的患者。在 NAC 开始前进行三碘增强光谱 CT 扫描。根据术后肿瘤消退分级（TRG），患者分为应答者（TRG=0+1）或无应答者（TRG=2+3），并按 7:3 的比例按时间顺序分为原始（156 例）和验证（66 例）数据集。我们比较了应答者和无应答者之间的临床病理数据、随访信息、碘浓度（IC）、动脉/静脉/延迟期的归一化 IC（nIC）（AP/VP/DP）。通过多变量逻辑回归筛选应答的独立危险因素，并用于模型构建。通过列线图可视化模型，并通过接受者操作特征（ROC）曲线确定其能力。对数秩生存分析用于探讨 TRG、列线图与患者生存之间的关系。
本研究确定了 Borrmann 分类、ICDP 和 nICDP 是应答结果的独立危险因素。因此，建立了基于光谱 CT 的列线图，其在原始和验证数据集的曲线下面积（AUC）分别为 0.797（0.692-0.879）和 0.741（0.661-0.811），高于单独参数的 AUC。该列线图与验证数据集的无病生存有关（危险比（HR）：5.19[1.18-12.93]，P=0.02）。
基于光谱 CT 的列线图为预测 GC 患者 NAC 后的病理应答结果和无病生存风险分层提供了一种有效的工具。
