Department of Radiation Oncology, Institut Jean Godinot, Reims, France.
Radiology, Centre Hospitalier Universitaire de Reims, France.
Radiother Oncol. 2019 Jun;135:153-160. doi: 10.1016/j.radonc.2019.03.011. Epub 2019 Mar 27.
Baseline contrast-enhanced computed tomography (CT)-derived texture analysis in locally advanced rectal cancer could help offer the best personalized treatment. The purpose of this study was to determine the value of baseline-CT texture analysis in the prediction of downstaging in patients with locally advanced rectal cancer.
We retrospectively included all consecutive patients treated with neoadjuvant chemoradiation therapy (CRT) followed by surgery for locally advanced rectal cancer. Tumor texture analysis was performed on the baseline pre-CRT contrast-enhanced CT examination. Based on the selected model of downstaging with a penalized logistic regression in a training set, a radiomics score (Radscore) was calculated as a linear combination of selected features. A multivariable prognostic model that included Radscore and clinical factors was created.
Of the 121 patients included in the study, 109 patients (90%) had T3-T4 cancer and 99 (82%) had N+ cancer. A downstaging response was observed in 96 patients (79%). In the training set (79 patients), the best model (ELASTIC-NET method) reduced the 36 texture features to a combination of 6 features. The multivariate analysis retained the Radscore (odds ratio [OR] = 13.25; 95% confidence interval [95% CI], 4.06-71.64; p < 0.001) and age (OR = 1.10/1 year; 1.03-1.20; p = 0.008) as independent factors. In the test set, the area under the curve was estimated to be 0.70 (95% CI, 0.48-0.92).
This study presents a prognostic score for downstaging, from initial computed tomography-derived texture analysis in locally advanced rectal cancer, which may lead to a more personalized treatment for each patient.
局部进展期直肠癌患者基线期增强 CT 衍生的纹理分析有助于提供最佳的个体化治疗方案。本研究旨在探讨基线 CT 纹理分析在预测局部进展期直肠癌降期中的价值。
我们回顾性纳入了所有接受新辅助放化疗(CRT)后手术治疗的局部进展期直肠癌患者。对基线期 CRT 前增强 CT 检查进行肿瘤纹理分析。基于在训练集中进行的降期模型,通过惩罚性逻辑回归选择特征,计算放射组学评分(Radscore)。创建一个包含 Radscore 和临床因素的多变量预后模型。
在本研究中,共纳入了 121 例患者,其中 109 例(90%)患者的肿瘤为 T3-T4 期,99 例(82%)患者的肿瘤为 N+期。96 例(79%)患者的肿瘤降期。在训练集(79 例患者)中,最佳模型(ELASTIC-NET 方法)将 36 个纹理特征简化为 6 个特征的组合。多变量分析保留了 Radscore(比值比 [OR] = 13.25;95%置信区间 [95%CI],4.06-71.64;p < 0.001)和年龄(OR = 1.10/1 岁;1.03-1.20;p = 0.008)为独立因素。在验证集中,曲线下面积估计为 0.70(95%CI,0.48-0.92)。
本研究提出了一种基于局部进展期直肠癌初始 CT 纹理分析的降期预测预后评分,这可能为每位患者提供更个体化的治疗方案。