| 肺结节CT征象模型预测浸润性腺癌病理分级研究 |
| 投稿时间：2024-10-16 修订日期：2024-12-20 点此下载全文 |
| 引用本文：梅子君,姬凯,岳军艳.肺结节CT征象模型预测浸润性腺癌病理分级研究[J].医学研究杂志,2025,54(6):76-81 |
| DOI：
10.11969/j.issn.1673-548X.2025.06.014 |
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| 基金项目:河南省医学科技攻关计划联合共建项目(LHGJ20200487) |
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| 中文摘要:目的 利用肺结节CT征象构建二元Logistic回归模型,预测浸润性腺癌的病理分级。方法 回顾性分析2021年1月～2023年2月河南理工大学第一附属医院及新乡医学院第一附属医院术后病理为浸润性腺癌的磨玻璃结节303例的临床资料、病理分型及影像学资料。根据病理结果将303个病灶分成两组即低级别组(以贴壁、腺泡或乳头状型为主型腺癌,没有或低于20%的高级别模式)262例和高级别组(任何大于20%高级别成分的腺癌)41例。两组间定量参数比较采用Mann-Whitney U检验,定性参数的比较采用χ2检验。采用Logistic回归模型筛选独立预测因子,使用曲线下面积(area under the curve,AUC)值、校准曲线和决策分析曲线评价模型的区分度、校准度和临床使用价值。结果 单因素分析结果显示,性别、空气支气管征、空泡征、血管集束征、胸膜凹陷征、长径、短径及CT强化净增值,差异有统计学意义(P＜0.05),位置、毛刺征、实性成分比差异无统计学意义(P＞0.05)；Logistic回归分析显示,长径、CT强化净增值、血管集束征、胸膜凹陷征、空泡征是构建预测浸润性腺癌病理分级模型的独立预测因子。受试者工作特征(receiver operating characteristic,ROC)曲线分析结果显示,Logistic回归模型AUC值是0.846,敏感度为81.25%,特异性为86.52%。结论 CT征象构建的模型预测浸润性腺癌病理分级有较好的预测能力及稳定性。 |
| 中文关键词:浸润性肺腺癌 CT征象 病理分级 |
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| Research on the Prediction of the Pathological Grade of Invasive Lung Adenocarcinoma by the CT Signs Model of Pulmonary Nodules. |
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| Abstract:Objective A binary Logistic regression model was developed to forecast the pathological grade of invasive adenocarcinoma by utilizing the CT characteristics of lung nodules. Methods A retrospective analysis was conducted on the clinical data, pathological types, and imaging findings of 303 cases of ground-glass nodules diagnosed with postoperative pathological infiltrative adenocarcinoma at the First Affiliated Hospital of Henan Polytechnic University and the First Affiliated Hospital of Xinxiang Medical College from January 2021 to February 2023. Based on the pathological results, these lesions were categorized into two groups:the low-grade group (comprising 262 cases characterized by adherent, acinar, or papillary types as predominant forms of adenocarcinoma with no more than 20% high-grade pattern) and the high-grade group (consisting of 41 cases exhibiting any form of adenocarcinoma with over 20% high-grade components). The Mann-Whitney U test was employed to compare quantitative parameters between both groups, while qualitative parameters were analyzed using the χ2 test. Additionally, binary Logistic regression models were utilized to identify independent predictors; further evaluation included area under curve (AUC) values, calibration curves, and decision analysis curves to assess model differentiation, calibration accuracy, and clinical applicability. Results Univariate analysis revealed that gender, air bronchial sign, vacuole sign, vascular cluster sign, pleural depression sign, long diameter, short diameter, and CT-enhanced net increment exhibited statistical significance (P<0.05), whereas location, burr sign, and solid component ratio did not demonstrate statistical significance (P>0.05). Binary Logistic regression analysis identified long diameter, CT-enhanced net increment, vascular cluster sign, pleural depression sign, and vacuole sign as independent predictors of the pathological grade model for invasive adenocarcinoma. The results of ROC curve analysis indicated that the AUC value of the Logistic regression model was 0.846 with a sensitivity of 81.25% and specificity of 86.52%. Conclusion The logistic regression model based on CT signs has excellent ability and stability in predicting the pathological grade of invasive adenocarcinoma. |
| keywords:Invasive lung adenocarcinoma CT signs Pathological grade |
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