Mazzei Maria Antonietta, Di Giacomo Letizia, Bagnacci Giulio, Nardone Valerio, Gentili Francesco, Lucii Gabriele, Tini Paolo, Marrelli Daniele, Morgagni Paolo, Mura Gianni, Baiocchi Gian Luca, Pittiani Frida, Volterrani Luca, Roviello Franco
Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy.
Unit of Radiation Therapy, Ospedale del Mare, Naples, Italy.
Quant Imaging Med Surg. 2021 Jun;11(6):2376-2387. doi: 10.21037/qims-20-683.
To predict response to neoadjuvant chemotherapy (NAC) of gastric cancer (GC), prior to surgery, would be pivotal to customize patient treatment. The aim of this study is to investigate the reliability of computed tomography (CT) texture analysis (TA) in predicting the histo-pathological response to NAC in patients with resectable locally advanced gastric cancer (AGC).
Seventy (40 male, mean age 63.3 years) patients with resectable locally AGC, treated with NAC and radical surgery, were included in this retrospective study from 5 centers of the Italian Research Group for Gastric Cancer (GIRCG). Population was divided into two groups: 29 patients from one center (internal cohort for model development and internal validation) and 41 from other four centers (external cohort for independent external validation). Gross tumor volume (GTV) was segmented on each pre- and post-NAC multidetector CT (MDCT) image by using a dedicated software (RayStation), and 14 TA parameters were then extrapolated. Correlation between TA parameters and complete pathological response (tumor regression grade, TRG1), was initially investigated for the internal cohort. The univariate significant variables were tested on the external cohort and multivariate logistic analysis was performed.
In multivariate logistic regression the only significant TA variable was delta gray-level co-occurrence matrix (GLCM) contrast (P=0.001, Nagelkerke R: 0.546 for the internal cohort and P=0.014, Nagelkerke R: 0.435 for the external cohort). Receiver operating characteristic (ROC) curves, generated from the logistic regression of all the patients, showed an area under the curve (AUC) of 0.763.
Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.
在胃癌（GC）手术前预测新辅助化疗（NAC）的反应对于定制患者治疗方案至关重要。本研究旨在探讨计算机断层扫描（CT）纹理分析（TA）在预测可切除的局部晚期胃癌（AGC）患者对NAC的组织病理学反应方面的可靠性。
本回顾性研究纳入了来自意大利胃癌研究组（GIRCG）5个中心的70例（40例男性，平均年龄63.3岁）可切除的局部AGC患者，这些患者接受了NAC和根治性手术。研究对象分为两组：来自一个中心的29例患者（用于模型开发和内部验证的内部队列）和来自其他四个中心的41例患者（用于独立外部验证的外部队列）。使用专用软件（RayStation）在每个NAC治疗前和治疗后的多排螺旋CT（MDCT）图像上分割大体肿瘤体积（GTV），然后推算出14个TA参数。首先在内部队列中研究TA参数与完全病理反应（肿瘤退缩分级，TRG1）之间的相关性。对外部队列测试单变量显著变量，并进行多变量逻辑分析。
在多变量逻辑回归中，唯一显著的TA变量是灰度共生矩阵（GLCM）对比度变化（内部队列P = 0.001，Nagelkerke R：0.546；外部队列P = 0.014，Nagelkerke R：0.435）。根据所有患者的逻辑回归生成的受试者工作特征（ROC）曲线显示曲线下面积（AUC）为0.763。
NAC后GLCM对比度和差异以及GLCM对比度变化TA参数似乎可可靠地识别局部AGC对NAC有反应的患者。
