Jiangnan University, Wuxi City, Jiangsu Province, China.
Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu Province, China.
J Xray Sci Technol. 2023;31(1):49-61. doi: 10.3233/XST-221291.
To investigate the feasibility of predicting the early response to neoadjuvant chemotherapy (NAC) in advanced gastric cancer (AGC) based on CT radiomics nomogram before treatment.
The clinicopathological data and pre-treatment portal venous phase CT images of 180 consecutive AGC patients who received 3 cycles of NAC are retrospectively analyzed. They are randomly divided into training set (n = 120) and validation set (n = 60) and are categorized into effective group (n = 83) and ineffective group (n = 97) according to RECIST 1.1. Clinicopathological features are compared between two groups using Chi-Squared test. CT radiomic features of region of interest (ROI) for gastric tumors are extracted, filtered and minimized to select optimal features and develop radiomics model to predict the response to NAC using Pyradiomics software. Furthermore, a nomogram model is constructed with the radiomic and clinicopathological features via logistic regression analysis. The receiver operating characteristic (ROC) curve analysis is used to evaluate model performance. Additionally, the calibration curve is used to test the agreement between prediction probability of the nomogram and actual clinical findings, and the decision curve analysis (DCA) is performed to assess the clinical usage of the nomogram model.
Four optimal radiomic features are selected to construct the radiomics model with the areas under ROC curve (AUC) of 0.754 and 0.743, sensitivity of 0.732 and 0.750, specificity of 0.729 and 0.708 in the training set and validation set, respectively. The nomogram model combining the radiomic feature with 2 clinicopathological features (Lauren type and clinical stage) results in AUCs of 0.841 and 0.838, sensitivity of 0.847 and 0.804, specificity of 0.771 and 0.794 in the training set and validation set, respectively. The calibration curve generates a concordance index of 0.912 indicating good agreement of the prediction results between the nomogram model and the actual clinical observation results. DCA shows that patients can receive higher net benefits within the threshold probability range from 0 to 1.0 in the nomogram model than in the radiomics model.
CT radiomics nomogram is a potential useful tool to assist predicting the early response to NAC for AGC patients before treatment.
探讨治疗前 CT 影像组学列线图预测晚期胃癌（AGC）新辅助化疗（NAC）早期疗效的可行性。
回顾性分析 180 例接受 3 周期 NAC 的连续 AGC 患者的临床病理资料及治疗前门静脉期 CT 图像。将其随机分为训练集（n=120）和验证集（n=60），根据 RECIST 1.1 标准分为有效组（n=83）和无效组（n=97）。采用卡方检验比较两组间的临床病理特征。采用 Pyradiomics 软件提取、筛选并最小化胃肿瘤感兴趣区（ROI）的 CT 影像组学特征，以选择最佳特征并建立预测 NAC 反应的影像组学模型。进一步通过 logistic 回归分析，基于影像组学和临床病理特征构建列线图模型。采用受试者工作特征（ROC）曲线分析评估模型性能。此外，采用校准曲线检验列线图预测概率与实际临床结果的一致性，采用决策曲线分析（DCA）评估列线图模型的临床应用价值。
筛选出 4 个最优的影像组学特征构建影像组学模型，其在训练集和验证集中的 ROC 曲线下面积（AUC）分别为 0.754 和 0.743，敏感度分别为 0.732 和 0.750，特异度分别为 0.729 和 0.708。将影像组学特征与 2 个临床病理特征（Lauren 分型和临床分期）相结合构建的列线图模型，在训练集和验证集中的 AUC 分别为 0.841 和 0.838，敏感度分别为 0.847 和 0.804，特异度分别为 0.771 和 0.794。校准曲线产生的一致性指数为 0.912，表明列线图模型与实际临床观察结果的预测结果具有良好的一致性。DCA 显示，在列线图模型中，阈值概率范围为 0 至 1.0 时，患者的净获益高于影像组学模型。
CT 影像组学列线图是一种有潜力的工具，可用于预测治疗前晚期胃癌患者对 NAC 的早期反应。
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