摘要
目的分析基于肿瘤体积的肺癌患者放射治疗效果预测模型的构建与评价。方法选择我院2019年1月~2021年12月收治的173例肺癌患者作为研究对象。受试者接受放疗方案进行干预治疗;治疗前及放疗结束后1月行影像学检查读取病灶体积,计算患者治疗前后最大直径减少率与体积减少率,扫描并分析患者病灶区Ve、K^(trans)、K_(ep);治疗后对患者的临床疗效进行评估。利用Pearson检验对最大直径的减少率以及体积的减少率间的相关性给予分析,采用Logistic回归模型分析Ve、K^(trans)、K_(ep)联合应用预测患者基于肿瘤体积的疗效评估模型,利用ROC曲线对各指标的单独和联合诊断在疗效方面的价值给予预测。结果完全缓解+部分缓解组患者最大直径减少率和体积减少率均低于疾病稳定+疾病进展组(P<0.05);肿瘤最大直径减少率与体积减少率与肺癌患者治疗临床疗效呈正相关关系(P<0.05);完全缓解+部分缓解组Ve、K^(trans)、K_(ep)均高于疾病稳定+疾病进展组(P<0.05);采用MRI指标联合肿瘤体积和最大径预测患者临床疗效的多元回归预测模型为Log(P)=0.685×最大直径减少率+0.651×体检减少率+0.604×Ve+0.612×K^(trans)+0.644×K_(ep)+0.849;采用MRI指标联合肿瘤体积和最大径预测患者临床疗效模型的价值明显高于各指标单独应用(P<0.05)。结论通过评估肺癌患者肿瘤体积及MRI扫描检查可有效分析肺癌患者的预后质量,具有较高的应用价值。
Objective To analyze the construction and evaluation of radiotherapy effect prediction model for lung cancer patients based on tumor volume.Methods A total of 173 patients with lung cancer treated in our hospital from January 2019 to December 2021 were selected as the research objects.The subjects received radiotherapy regimen for intervention treatment.Imaging examination was performed before treatment and 1 month after radiotherapy to read the lesion volume,calculate the maximum diameter reduction rate and volume reduction rate before and after treatment.We scaned and analyzed the lesion areas Ve,K^(trans) and K_(ep).After treatment,the clinical efficacy of the patients was evaluated.Pearson correlation test was used to analyze the correlation between maximum diameter reduction rate and volume reduction rate.Logistic regression model was used to analyze the efficacy evaluation model of Ve,K^(trans) and K_(ep) combined application to predict patients'tumor volume.ROC curve was drawn to analyze the value of each index alone and combined application to predict patients'efficacy.Results The maximum diameter reduction rate and volume reduction rate in CR+PR group were significantly lower than those in SD+PD group(P<0.05).There was a significant positive correlation between the maximum diameter reduction rate and volume reduction rate and the clinical efficacy of lung cancer patients(P<0.05).Ve,K^(trans) and K_(ep) in CR+PR group were significantly higher than those in SD+PD group(P<0.05).The multiple regression prediction model of MRI combined with tumor volume and maximum diameter was log(P)=0.685×maximum diameter reduction rate+0.651×physical examination reduction rate+0.604×Ve+0.612×K^(trans)+0.644×K_(ep)+0.849;The value of using MRI index combined with tumor volume and maximum diameter to predict the clinical efficacy of patients was significantly higher than that of using each index alone(P<0.05).Conclusion The evaluation of tumor volume and MRI scan in patients with lung cancer can effectively analyze the prognosis quality of patients with lung cancer.
作者
王延虎
王慧驰
柴林
洪梅
肖韡
朱诺
李益坤
WANG Yanhu;WANG Huichi;CHAI Lin;HONG Mei;XIAO Wei;ZHU Nuo;LI Yikun(Department of Radiotherapy,Nanjing Chest Hospital,Nanjing 210029,China;Department of Radiotherapy,Eastern Theater General Hospital,Nanjing 210002,China)
出处
《分子影像学杂志》
2023年第2期227-231,共5页
Journal of Molecular Imaging
基金
江苏省卫生计生委医学科研课题(YKK18163)
江苏卫健委"六个一工程"项目(LGY2019006)。
关键词
肺癌
放射治疗
预测模型
肿瘤体积
lung cancer
radiotherapy
prediction model
tumor volume