摘要
目的探讨计算机断层扫描(CT)纹理分析对肺腺癌患者新辅助化疗预后的评估价值。方法选择2018年1月至2021年1月于商丘市长征人民医院接受新辅助化疗的70例肺腺癌患者为研究对象。患者均接受2个周期的新辅助化疗。根据化疗2个周期时的效果将患者分为预后不良组和预后良好组,分析CT纹理参数对肺腺癌患者新辅助化疗预后的评估价值。结果化疗2个周期,70例患者中41例(58.57%)预后不良。预后不良组CT纹理参数平均灰度、熵值大于预后良好组(P<0.05)。受试者工作特征(ROC)曲线显示,平均灰度、熵单独及联合评估肺腺癌患者新辅助化疗预后不良的AUC分别为0.852、0.836、0.863,当平均灰度、熵分别取92.050、5.935时,可获得最佳评估价值。结论CT纹理分析对肺腺癌患者新辅助化疗预后具有一定评估价值。
Objective To explore the value of computed tomography(CT)texture analysis in evaluating the prognosis of patients with lung adenocarcinoma after neoadjuvant chemotherapy.Methods Seventy patients with lung adenocarcinoma who received neoadjuvant chemotherapy at Changzheng People’s Hospital in Shangqiu from January 2018 to January 2021 were selected as the research objects.All patients received 2 cycles of neoadjuvant chemotherapy.According to the effect of 2 cycles of chemotherapy,patients were divided into poor prognosis group and good prognosis group.The value of CT texture parameters in evaluating the prognosis of patients with lung adenocarcinoma after neoadjuvant chemotherapy was analyzed.Results After 2 cycles of chemotherapy,41 out of 70 patients(58.57%)had a poor prognosis.The average gray scale and entropy value of CT texture parameters in poor prognosis group were greater than those in good prognosis group(P<0.05).The receiver operating characteristic(ROC)curve showed that the average gray scale and entropy alone and in combination to assess the poor prognosis of patients with lung adenocarcinoma after neoadjuvant chemotherapy were 0.852,0.836 and 0.863,respectively.When the average gray scale and entropy were respectively 92.050 and 5.935,the evaluation value was the best.Conclusion CT texture analysis has a certain value in evaluating the prognosis of patients with lung adenocarcinoma after neoadjuvant chemotherapy.
作者
祝令武
ZHU Lingwu(CT Room,Changzheng People’s Hospital of Shangqiu,Shangqiu 476000,China)
出处
《河南医学研究》
CAS
2021年第31期5901-5904,共4页
Henan Medical Research
关键词
肺腺癌
新辅助化疗
预后
计算机断层扫描
纹理分析
平均灰度
熵
lung adenocarcinoma
neoadjuvant chemotherapy
prognosis
computed tomography
texture analysis
average gray scale
entropy