Liu Wenju, Qiao Xu, Ge Hong, Zhang Sheng, Sun Xiaojiang, Li Jiancheng, Chen Weilin, Gu Wendong, Yuan Shuanghu
Department of Radiation Oncology, Shandong University Cancer Center, Jinan, Shandong 250117, P.R. China.
Department of Radiation Oncology, Liaocheng People's Hospital, Liaocheng, Shandong 252000, P.R. China.
Oncol Lett. 2023 Jun 7;26(1):317. doi: 10.3892/ol.2023.13903. eCollection 2023 Jul.
A model for predicting the recurrence pattern of patients with locally advanced non-small cell lung cancer (LA-NSCLC) treated with chemoradiotherapy is of great importance for precision treatment. The present study analyzed whether the comprehensive quantitative values (CVs) of the fluorine-18(F)-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomic features and metastasis tumor volume (MTV) combined with clinical characteristics could predict the recurrence pattern of patients with LA-NSCLC treated with chemoradiotherapy. Patients with LA-NSCLC treated with chemoradiotherapy were divided into training and validation sets. The recurrence profile of each patient, including locoregional recurrence (LR), distant metastasis (DM) and both LR/DM were recorded. In the training set of patients, the primary tumor prior radiotherapy with F-FDG PET/CT and both primary tumors and lymph node metastasis were considered as the regions of interest (ROIs). The CVs of ROIs were calculated using principal component analysis. Additionally, MTVs were obtained from ROIs. The CVs, MTVs and the clinical characteristics of patients were subjected to aforementioned analysis. Furthermore, for the validation set of patients, the CVs and clinical characteristics of patients with LA-NSCLC were also subjected to logistic regression analysis and the area under the curve (AUC) values calculated. A total of 86 patients with LA-NSCLC were included in the analysis, including 59 and 27 patients in the training and validation sets of patients, respectively. The analysis revealed 22 and 12 cases with LR, 24 and 6 cases with DM and 13 and 9 cases with LR/DM in the training and validation sets of patients, respectively. Histological subtype, CV2-5 and CV3-4 were identified as independent variables in the logistic regression analysis (P<0.05). In addition, the AUC values for diagnosing LR, DM and LR/DM were 0.873, 0.711 and 0.826, and 0.675, 0.772 and 0.708 in the training and validation sets of patients, respectively. Overall, the results demonstrated that the spatial and metabolic heterogeneity quantitative values from the primary tumor combined with the histological subtype could predict the recurrence pattern of patients with LA-NSCLC treated with chemoradiotherapy.
建立一个预测接受放化疗的局部晚期非小细胞肺癌(LA-NSCLC)患者复发模式的模型对于精准治疗至关重要。本研究分析了氟-18(F)-氟脱氧葡萄糖(FDG)正电子发射断层扫描(PET)/计算机断层扫描(CT)影像组学特征和转移瘤体积(MTV)的综合定量值(CVs)结合临床特征是否能够预测接受放化疗的LA-NSCLC患者的复发模式。接受放化疗的LA-NSCLC患者被分为训练集和验证集。记录每位患者的复发情况,包括局部区域复发(LR)、远处转移(DM)以及LR/DM。在患者训练集中,放疗前用F-FDG PET/CT检查的原发肿瘤以及原发肿瘤和淋巴结转移灶均被视为感兴趣区域(ROIs)。使用主成分分析计算ROIs的CVs。此外,从ROIs中获取MTVs。对患者的CVs、MTVs和临床特征进行上述分析。此外,对于患者验证集,也对LA-NSCLC患者的CVs和临床特征进行逻辑回归分析并计算曲线下面积(AUC)值。共有86例LA-NSCLC患者纳入分析,其中训练集和验证集分别有59例和27例患者。分析显示,训练集和验证集患者中分别有22例和12例发生LR,24例和6例发生DM,13例和9例发生LR/DM。在逻辑回归分析中,组织学亚型、CV2-5和CV3-4被确定为独立变量(P<0.05)。此外,训练集和验证集患者中诊断LR、DM和LR/DM的AUC值分别为0.873、0.71***和0.826,以及0.675、0.772和0.708。总体而言,结果表明原发肿瘤的空间和代谢异质性定量值结合组织学亚型能够预测接受放化疗的LA-NSCLC患者的复发模式。