ISSN 1004-4140
CN 11-3017/P
胡淑敏, 祁冬. 基于动脉期影像组学特征联合临床-CT特征构建列线图模型在鉴别肺鳞癌与肺腺癌中的应用价值[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-11. DOI: 10.15953/j.ctta.2024.105.
引用本文: 胡淑敏, 祁冬. 基于动脉期影像组学特征联合临床-CT特征构建列线图模型在鉴别肺鳞癌与肺腺癌中的应用价值[J]. CT理论与应用研究(中英文), xxxx, x(x): 1-11. DOI: 10.15953/j.ctta.2024.105.
Hu S M, Qi D. The value of constructing a nomogram model based on arterial-phase imaging radiomics features combined with clinical-CT features in differentiating lung squamous carcinoma and adenocarcinoma[J]. CT Theory and Applications, xxxx, x(x): 1-11. DOI: 10.15953/j.ctta.2024.105. (in Chinese).
Citation: Hu S M, Qi D. The value of constructing a nomogram model based on arterial-phase imaging radiomics features combined with clinical-CT features in differentiating lung squamous carcinoma and adenocarcinoma[J]. CT Theory and Applications, xxxx, x(x): 1-11. DOI: 10.15953/j.ctta.2024.105. (in Chinese).

基于动脉期影像组学特征联合临床-CT特征构建列线图模型在鉴别肺鳞癌与肺腺癌中的应用价值

The value of constructing a nomogram model based on arterial-phase imaging radiomics features combined with clinical-CT features in differentiating lung squamous carcinoma and adenocarcinoma

  • 摘要: 目的:探讨基于动脉期影像组学特征联合临床-CT特征构建列线图模型在鉴别肺鳞癌(SCC)与肺腺癌(ADC)中的应用价值。方法:回顾性收集2021年8月-2023年9月在我院进行穿刺病理活检或手术的肺癌患者85例作为训练集,并同时收集2023年5月-2024年6月我院经病理证实的肺癌患者40例作为验证集,所有患者均行胸部CT增强检查。根据病理结果将训练集分为SCC组(n=29)和ADC组(n=56)。比较两组患者一般临床资料和CT图像特征的差异,采用单因素和多因素Logistic回归分析筛选出独立预测因素,并构建临床-CT模型。应用ITK Snap软件提取训练集动脉期图像影像组学特征,依次采用组内相关系数(ICC)、Roruta特征筛选和最小绝对收缩和选择算子(LASSO)对提取的影像组学特征进行降维处理,筛选出有意义的特征,采用Logistic回归构建动脉期影像组学模型,并计算该模型的影像组学评分(Rad-score)。以多因素Logistic回归分析筛选出临床-CT特征有意义的自变量与Rad-score构建联合模型,并绘制列线图。应用ROC曲线、校正曲线、H-L检验、Delong检验及临床决策曲线(DCA)对临床-CT模型、影像组学模型及列线图模型进行评价。结果:单因素分析结果显示,分叶征、坏死空洞征均多于ADC组(均P<0.05),癌胚抗原(CEA)、血管集束征、胸膜牵拉及毛刺征均少于ADC组(均P<0.05)。将上述自变量纳入多因素Logistic进一步筛选,结果显示,CEA、分叶征、胸膜牵拉及毛刺征为独立危险因素,基于此构建临床-CT模型的训练集和验证集AUC值分别为0.623和0.786。影像组学特征经降维后共筛选出的有意义特征有8个,分别为一阶特征3个、二阶特征5个。经ROC曲线分析显示,影像组学模型训练集和验证集AUC值分别为0.830和0.846;列线图模型训练集和验证集AUC值分别为0.913和0.922。经Delong检验显示,列线图模型AUC值均明显高于临床-CT模型和影像组学模型(均P<0.05);Hosmer-Lemeshow检验结果显示,临床-CT模型、影像组学模型及列线图模型的拟合度均良好;校准曲线分析显示,列线图模型的预测概率曲线与理想曲线最接近,预测精准度更好;DCA分析结果显示,列线图模型的曲线下面积最大,临床净收益最高。结论:基于动脉期影像组学特征联合临床-CT特征构建列线图模型在鉴别SCC与ADC中具有一定的诊断价值,为无创鉴别SCC与ADC提供一种新的诊断方式。

     

    Abstract: Objective: To investigate the value of constructing a nomogram model based on arterial-phase imaging radiomics features combined with clinical-CT features for the differentiation between squamous lung cancer (SCC) and adenocarcinoma of the lung (ADC). Methods: Retrospectively, 85 patients with lung cancer who underwent puncture pathology biopsy or surgery in our hospital, from August 2021 to September 2023, were collected as a training set. Concurrently, 40 patients with pathologically confirmed lung cancer in our hospital, from May 2023 to June 2024, were collected as a validation set. All patients underwent chest CT enhancement. The training set was divided into the SCC group (n=29) and the ADC group (n=56) based on the pathology. General clinical data and CT image characteristics of the two groups of patients were compared and differences were identified. Independent predictors were screened using unifactorial and multifactorial logistic regression analyses, and a clinical-CT model was constructed. ITK Snap software was applied to extract the radiomics features of the arterial-phase images, and the intragroup correlation coefficient (ICC), Roruta feature screening, and least absolute shrinkage and selection operator (LASSO) were sequentially used to downsize the extracted radiomics features, screen out the meaningful features, construct the arterial-phase image radiomics model using Logistic regression, and compute the model's image radiomics score (Rad-score). A multifactor logistic regression analysis was used to screen the independent variables with meaningful clinical-CT characteristics and Rad-score to construct a joint model, and a nomogram graph was plotted. ROC curves, calibration curves, H-L test, Delong test, and clinical decision curves (DCA) were applied to evaluate the clinical-CT, radiomics, and nomogram models. Results: The results of univariate analysis showed that there were more lobular signs and necrotic cavity signs, and fewer carcinoembryonic antigen (CEA), vascular cluster signs, pleural pulling, and burr signs in the SCC than in the ADC group (all P < 0.05). The above independent variables were included in the multifactorial Logistic analysis for further screening, and the results showed that CEA, lobular sign, pleural pull, and burr sign were independent risk factors. The area under the curve (AUC) values for the training and validation sets of the clinical-CT model constructed based on this were 0.623 and 0.786, respectively. A total of eight meaningful features were screened after dimensionality reduction of the radiomics features, which were three first-order features and five second-order features. The ROC curve analysis showed that the AUC values for the training and validation sets of the radiomics model were 0.830 and 0.846, respectively; and the AUC values for the training and validation sets of the nomogram model were 0.913 and 0.922, respectively. The Delong test showed that the AUC values of the nomogram model were all significantly higher than those of the clinical-CT model and the radiomics model (all P < 0.05); the Hosmer-Lemeshow test showed that the clinical-CT model, the radiomics model, and the nomogram model were all well fitted; calibration curve analysis showed that the predictive probability curve of the nomogram model was closest to the ideal curve, with better predictive accuracy; and DCA analysis showed that the AUC of the nomogram model was the largest, with the highest net clinical benefit. Conclusion: Constructing a nomogram model based on arterial-phase imaging radiomics features combined with clinical-CT features has some diagnostic value in differentiating SCC from ADC, providing a new diagnostic modality for noninvasive differentiation.

     

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