基于机器学习方法的临床放射组学联合模型在Ⅰ期肺癌STAS术前预测中的价值

Value of a combined clinical radiomics model based on machine-learning method in preoperative prediction of spread through air spaces in stageⅠlung cancer

目的探讨基于机器学习方法的临床放射组学联合模型在Ⅰ期肺癌气道播散(STAS)术前预测中的价值。方法回顾性分析2020年11月至2023年11月于淮南阳光新康医院就诊的165例经组织病理学检查证实的Ⅰ期肺癌患者的临床、影像及病理资料,其中男性91例、女性74例,年龄(61.6±11.2)岁,范围28~88岁。根据术后组织病理学检查结果将所有患者分为STAS阴性组和STAS阳性组。采用ITK-SNAP软件对165例患者术前CT图像中肺癌靶区进行勾画并提取放射组学特征,并采用完全随机分组法按照7∶3比例分为训练组(115例)和验证组(50例)。采用组内和组间相关系数、单因素分析、Spearman相关性分析及最小绝对收缩和选择算子(LASSO)回归分析对筛选出来的特征进行降维处理,计算特征权重系数并进行线性组合计算放射组学得分。根据Logistic方法建立放射组学模型。采用单因素及多因素Logistic回归分析筛选预测Ⅰ期肺癌STAS的独立危险因素,根据赤池信息准则(AIC),采用Logistic回归分析得到AIC最小的临床模型。联合临床模型与放射组学模型构建预测Ⅰ期肺癌STAS的临床放射组学联合模型,绘制列线图对模型进行可视化处理,采用受试者工作特征(ROC)曲线评估模型的诊断效能。结果165例Ⅰ期肺癌患者中,STAS阴性组85例、STAS阳性组80例。放射组学模型在训练组及验证组中的曲线下面积(AUC)分别为0.895(95%CI:0.826~0.950)和0.814(95%CI:0.680~0.929),灵敏度分别为0.893和0.875,特异度分别为0.797和0.731。单因素及多因素Logistic回归分析结果显示,CT值、毛刺征是预测Ⅰ期肺癌STAS的独立危险因素。临床模型在训练组及验证组中的AUC分别为0.849(95%CI:0.772~0.914)和0.822(95%CI:0.700~0.923),灵敏度分别为0.786和0.583,特异度分别为0.814和0.962。构建的临床放射组学联合模型在训练组及验证组中的AUC分别为0.943(95%CI:0.894~0.982)和0.880(95%CI:0.769~0.971),灵敏度分别为0.964和0.750,特异度分别为0.847和0.962。结论基于机器学习方法的临床放射组学联合模型可在术前预测Ⅰ期肺癌STAS,有望辅助临床对Ⅰ期肺癌的精准诊断、治疗和管理策略的选择。

Objective To explore the value of a combined clinical radiomics model based on machine-learning method in the preoperative prediction of spread through air spaces(STAS)in stageⅠlung cancer.Methods The clinical,imaging,and pathological data of 165 patients with stageⅠlung cancer confirmed by histopathology were retrospectively analyzed in Huainan Yangguang xinkang Hospital from November 2020 to November 2023.The patients included 91 males and 74 females,aged(61.6±11.2)years,ranging from 28 to 88 years.All patients were divided into STASnegative and STAS-positive groups according to the results of postoperative histopathology.The target areas of lung cancer in preoperative CT images of 165 patients were mapped,and radiomic features were extracted using ITK-SNAP software.The patients were divided into the training group(115 cases)and the verification group(50 cases)according to a 7∶3 ratio through completerandomization method.Intra-and inter-group correlation coefficients,univariate analysis,Spearman correlation analysis,and least absolute shrinkage and selection operator(LASSO)regression analysis were used to reduce the dimensionality of the selected features.The feature weight coefficients were calculated,and linear combinations were used to calculate the Radscore.The radiomics model was established according to Logistic method.Independent risk factors for STAS of stageⅠlung cancer were screened by univariate and multivariate Logistic regression analysis.According to Akaechi information criteria(AIC),a clinical model with minimal AIC was obtained by Logistic regression analysis.A combined clinical radiomic model for predicting STAS of stageⅠlung cancer was constructed by combining clinical model and radiomic model,and a nomogram was drawn to visualize the model.A receiver operating characteristic curve was drawn to evaluate the diagnostic efficiency of the model.Results A total of 165 patients with stageⅠlung cancer were included,specifically 85 in the STAS-negative group and 80 in the STAS-positive group.The area under the curve(AUC)of the radiomics model was 0.895(95%CI:0.826–0.950)and 0.814(95%CI:0.680–0.929)in the training and verification groups,respectively.The sensitivity was 0.893 and 0.875,and the specificity was 0.797 and 0.731,respectively.Univariate and multivariate Logistic regression analysis showed that CT value and burr sign were independent risk factors for predicting STAS in stageⅠlung cancer.The AUC of the clinical model in the training and verification groups was 0.849(95%CI:0.772–0.914)and 0.822(95%CI:0.700–0.923),respectively.The sensitivity was 0.786 and 0.583,and the specificity was 0.814 and 0.962,respectively.The AUC of the combined clinical radiomics model was 0.943(95%CI:0.894–0.982)and 0.880(95%CI:0.769–0.971)in the training and verification groups,respectively.The sensitivity was 0.964 and 0.750,and the specificity was 0.847 and 0.962,respectively.Conclusions The combined clinical radiomics model developed based on machine-learning method can predict STAS of stageⅠlung cancer before surgery.This model can be expected to assist in the selection of accurate diagnosis,treatment,and management strategies for stageⅠlung cancer.