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基于CT影像组学列线图预测非小细胞肺癌Ki-67表达水平的相关研究

Prediction of Ki-67 Expression Level in Non-Small Cell Lung Cancer Based on CT Radiomics Nomogram
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摘要 目的 探讨基于CT的影像组学列线图预测非小细胞肺癌(non-small cell lung cancer,NSCLC)的Ki-67表达水平的相关研究。方法回顾性分析经病理证实、且行Ki-67表达水平检测的144例NSCLC患者的临床及胸部CT影像资料,按照7:3比例随机分成训练组(100例)和验证组(44例),根据病理报告Ki-67表达水平将NSCLC患者划分为低表达组(Ki-67<14%)和高表达组(Ki-67≥14%)。在训练组中,分析Ki-67低表达和高表达患者的临床特征和CT征象,并采用单因素和多因素Logistic回归分析筛选出独立预测因素并建立临床模型。利用胸部平扫CT肺窗图像提取影像组学特征,借助最大绝对值归一化、最优特征筛选(百分比)、根据模型选择及选择算子(LASSO)算法对数据进行降维处理,计算影像组学评分(Rad-score)并构建影像组学模型。将独立预测因素及影像组学评分通过Logistic回归,得出联合列线图模型。采用ROC曲线及曲线下面积(AUC)评价三种模型的预测效能。结果ROC曲线分析训练组及验证组数据显示联合列线图模型AUC分别为0.873(95%C I:0.791-0.931)、0.851(95%CI:0.712-0.940),与临床模型与影像组学模型相比,其对NSCLC的Ki-67表达水平预测效能更好。HosmerLemeshow检验示训练组及验证组的联合模型与实际结局一致性较好(P>0.05)。结论基于CT的影像组学列线图为术前无创预测非小细胞肺癌Ki-67增殖指数提供了一种方法,可为临床医生提供补充信息及选择合适的治疗方案。 Objective To explore the predictive value of CT-based radiomics nomogram for Ki-67 expression level in non-small cell lung cancer(NSCLC).Methods The clinical and chest CT imaging data of 144 patients with NSCLC confirmed by pathology and tested for Ki-67 expression level were retrospectively analyzed.The patients were randomly divided into training group(100 cases) and validation group(44 cases).According to the expression level of Ki-67 in the pathological report,NSCLC patients were divided into low expression group(Ki-67 <14%) and high expression group(Ki-67≥14%).In the training group,the clinical characteristics and CT signs of patients with low and high Ki-67 expression were analyzed,and univariate and multivariate Logistic regression analysis were used to screen out independent predictors and establish a clinical model.Radiomics features were extracted from chest plain CT lung window images,and the maximum absolute value normalization,optimal feature selection(percentage),and model selection and selection operator(LASSO) algorithm were used to reduce the dimension of the data.The radiomics score(Rad-score) was calculated and the radiomics model was established.The independent predictors and radiomics scores were analyzed by Logistic regression to obtain a combined nomogram model.ROC curve and area under the curve(AUC) were used to evaluate the predictive efficacy of the three models.Results ROC curve analysis of the data in the training group and validation group showed that the combined nomogram model had the largest AUC of 0.873(95%CI:0.791-0.931) and 0.851(95%CI:0.712-0.940),respectively.Compared with the clinical model and radiomics model,the combined nomogram model had the largest AUC of 0.873(95%CI:0.791-0.931) and 0.851(95%CI:0.712-0.940),respectively.It has a better predictive value for Ki-67 expression level in NSCLC.Hosmer-Lemeshow test showed that the combined model of training group and validation group was consistent with the actual outcomes(P>0.05).Conclsion The CT-based radiomics nomogram provides a method for preoperative non-invasive prediction of Ki-67 proliferation index in non-small cell lung cancer,which ma kes the evaluation of tumor diffe rentiation more optional,and can provide supplementary information for clinicians and select appropriate treatment plans.
作者 张雪丽 张群芳 李淑华 孟影 黄京城 顾一泓 谢宗玉 ZHANG Xue-li;ZHANG Qun-fang;LI Shu-hua;MENG Ying;HUANG Jing-cheng;GU Yi-hong;XIE Zong-yu(School of Graduate,Bengbu Medical University,Bengbu 233030,Anhui Province,China;Department of Radiology,The First Affiliated Hospital of Bengbu Medical University,Bengbu 233004,Anhui Province,China;Department of Radiology,Mingguang People's Hospital,Mingguang 239400,Anhui Province,China)
出处 《中国CT和MRI杂志》 2024年第10期48-51,共4页 Chinese Journal of CT and MRI
基金 安徽省重点研究与开发计划(2022e07020033) 安徽省高等学校自然科学研究项目(KJ2021A0769)。
关键词 非小细胞肺癌 KI-67 计算机断层扫描 影像组学列线图 Non-Small Cell Lung Cancer Ki-67 Computed Tomography Radiomics Nomogram
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