摘要
目的探讨基于术前CT图像的影像组学预测模型在鉴别非小细胞肺癌（NSCLC）临床分期中的价值。
方法回顾性分析2007年10月至2014年12月广东省人民医院，经手术病理证实、具有完整的术前胸部CT检查资料且术前常规检查等临床资料齐全的657例NSCLC患者，将2007年10月到2012年4月的331例作为训练组，2012年5月到2014年12月的326例作为验证组。所有患者均行胸部CT平扫与增强扫描。按照术中及术后病理结果，对患者进行术后病理分期（PTNM），分为早期（Ⅰ、Ⅱ期）和晚期（Ⅲ、Ⅳ期）。采用基于Matlab 2014a软件的特征提取算法提取影像组学特征并进行特征筛选以建立影像组学标签。通过纳入影像组学标签及患者的临床资料建立多变量logistic回归模型，并进行模型简化及验证。采用ROC评价模型鉴别早期和晚期NSCLC的预测效能。
结果建立的影像组学标签对于鉴别NSCLC术后病理分期具有较好的预测效能，训练组和验证组影像组学标签值鉴别临床分期的ROC下面积（AUC）分别为0.715（95%可信区间为0.709~0.721）和0.724（95%可信区间为0.717~0.731）。影像组学标签、肿瘤最大径、癌胚抗原水平和非小细胞肺癌抗原CYFRA21-1水平均为独立显著的危险因素。预测模型在训练组中鉴别预测效能的AUC为0.787（95%可信区间为0.781~0.793），敏感度为73.4%，特异度为72.2%，阳性预测值为0.707，阴性预测值为0.868；在验证样本中的AUC为0.777（95%可信区间为0.771~0.783），敏感度为91.3%，特异度为67.3%，阳性预测值为0.607，阴性预测值为0.946。
结论通过联合基于术前胸部CT建立的影像组学标签及临床和实验室指标（术前癌胚抗原、CYFRA21-1水平及肿瘤最大径）建立的影像组学预测模型，对术前鉴别早期和晚期NSCLC具有价值。
ObjectiveTo develop and validate a CT-based radiomics predictive model for preoperative predicting the stage of non-small cell lung cancer （NSCLC）.
MethodsIn this retrospective study, 657 patients with histologically confirmed was collected from October 2007 to December 2014. The primary dataset consisted of patients with histologically confirmed NSCLC from October 2007 to April 2012, while independent validation was conducted from May 2012 to December 2014. All the patients underwent non-enhanced and contrast-enhanced CT images scan with a standard protocol. The pathological stage （PTNM） of patients with NSCLC were determined by the intraoperative and postoperative pathological findings, and were divided into early stage （Ⅰ,Ⅱ stage） and advanced stage （Ⅲ,Ⅳ stage）. A list of radiomics features were extracted using the software Matlab 2014a and the corresponding radiomics signature was constructed. Multivariable logistic regression analysis was performed with radiomics signature and clinical variables for developing the prediction model. The model performance was assessed with respect to discrimination using the area under the curve （AUC） of receiver operating characteristic（ROC） analysis.
ResultsThe discrimination performance of radiomics signature yielded a AUC of 0.715[95% confidence interval （CI）：0.709 to 0.721] in the primary dataset and a AUC of 0.724（95%CI：0.717 to 0.731） in the validation dataset. On multivariable logistic regression, radiomics signature, tumor diameter, carcinoembryonic antigen （CEA） level, and cytokeratin 19 fragment （CYFRA21-1） level were showed independently associated with the stage （Ⅰ,Ⅱ stage vs. Ⅲ, Ⅳ stage） of NSCLC. The prediction model showed good discrimination in both primary dataset （AUC=0.787, 95%CI：0.781 to 0.793;sensitivity=73.4%, specificity=72.2%,positive predictive value=0.707,negative predictive value=0.868） and independent validation dataset （AUC=0.777, 95%CI：0.771 to 0.783,sensitivity=91.3%,specificity=67.3%,positive predictive value=0.607, negative predictive value=0.946）.
ConclusionThe radiomics predictive model, which integrated with the radiomics signature and clinical characteristics can be used as a promising and applicable adjunct approach for preoperatively predicting the clinical stage （Ⅰ,Ⅱ stage vs. Ⅲ,Ⅳ stage） of patients with NSCLC.
作者
何兰
黄燕琪
马泽兰
梁翠珊
黄晓媚
程梓轩
梁长虹
刘再毅
He Lan;Huang Yanqi;Ma Zelan;Liang Cuishan;Huang Xiaomei;Cheng Zixuan;Liang Changhong;Liu Zaiyi(School of Medicine, South China University of Technology, Guangzhou 510006, China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2017年第12期906-911,共6页
Chinese Journal of Radiology
基金
国家自然科学基金（81771912,81701782）
