邢静静,刘译阳,周悦,詹鹏超,王睿,柴亚如,吕培杰,高剑波.术前CT影像组学联合CT及病理特征预测局部进展期食管鳞癌术后早期复发[J].中国医学影像技术,2024,40(6):863~868
术前CT影像组学联合CT及病理特征预测局部进展期食管鳞癌术后早期复发
CT radiomics combined with CT and preoperative pathological features for predicting postoperative early recurrence of local advanced esophageal squamous cell carcinoma
投稿时间:2024-01-20  修订日期:2024-02-19
DOI:10.13929/j.issn.1003-3289.2024.06.014
中文关键词:  食管肿瘤  癌,鳞状细胞  体层摄影,X线计算机  影像组学  肿瘤复发,局部
英文关键词:esophageal neoplasms  carcinoma, squamous cell  X-ray computed, tomography  radiomics  neoplasm recurrence, local
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作者单位E-mail
邢静静 郑州大学第一附属医院放射科, 河南 郑州 450052  
刘译阳 郑州大学第一附属医院放射科, 河南 郑州 450052  
周悦 郑州大学第一附属医院放射科, 河南 郑州 450052  
詹鹏超 郑州大学第一附属医院放射科, 河南 郑州 450052  
王睿 郑州大学第一附属医院放射科, 河南 郑州 450052  
柴亚如 郑州大学第一附属医院放射科, 河南 郑州 450052  
吕培杰 郑州大学第一附属医院放射科, 河南 郑州 450052  
高剑波 郑州大学第一附属医院放射科, 河南 郑州 450052 cjr.gaojianbo@vip.163.com 
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中文摘要:
      目的 观察术前CT影像组学联合病理及CT特征预测局部进展期食管鳞癌(LAESCC)早期复发(ER)的价值。方法 回顾性分析334例LAESCC,按7 ∶ 3比例将患者分为训练集(n=234)或验证集(n=100),对其进行随访,记录术后有无ER(即术后12个月内肿瘤复发)。采用单因素及多因素logistic回归比较训练集有、无ER患者临床、CT表现及术前病理资料,筛选ER独立危险因素,构建CT-术前病理模型。基于训练集静脉期CT图像提取及筛选LAESCC影像组学特征并建立影像组学模型,以之联合独立危险因素建立联合模型。绘制受试者工作特征(ROC)曲线,计算曲线下面积(AUC),评估各模型诊断效能。结果 334例中,168例ER、166例无ER;训练集有、无ER均为117例、验证集51例ER、49例无ER。CT显示LAESCC长度、cT分期、cN分期及术前病理分化程度均为ER独立危险因素(P均<0.05);CT-术前病理模型在训练集和验证集的AUC分别为0.759和0.783。共选出10个最佳影像组学特征,以之建立的影像组学模型在训练集和验证集的AUC分别为0.770和0.730,联合模型在训练集和验证集的AUC分别为0.838和0.826。联合模型在训练集的AUC高于术前CT-病理模型及影像组学模型(P均<0.01)。结论 CT影像组学联合CT及术前病理特征能有效预测LAESCC术后ER。
英文摘要:
      Objective To investigate the value of CT radiomics combined with CT and preoperative pathological features for predicting postoperative early recurrence (ER) of local advanced esophageal squamous cell carcinoma (LAESCC). Methods Data of 334 patients with LAESCC were retrospectively analyzed. The patients were divided into training set (n=234) and verification set (n=100) at the ratio of 7:3 and were followed up to observe ER (recurrence within 12 months after surgery) or not. Univariate and multivariate logistic regression were used to analyze clinical,CT and preoperative pathological features of LAESCC in patients with or without ER in training set. The independent risk factors of ER were screened, and a CT-preoperative pathology model was constructed. Based on venous phase CT in training set, the radiomics features of lesions were extracted and screened to establish radiomics model, and finally a combined model was established based on radiomics model and the independent risk factors. Receiver operating characteristic (ROC) curves were drawn, and the area under the curve (AUC) was calculated to evaluate the diagnostic efficacy of each model. Results Among 334 cases, 168 were found with but 166 without ER. In training set, 117 cases were found with while the rest 117 without ER, while in verification set, 51 were found with but 49 without ER. The length of lesions, cT stage and cN stage shown on CT and tumor differentiation degree displayed with preoperative pathology were all independent risk factors for ER of LAESCC (all P<0.05). The AUC of CT-preoperative pathology model in training set and validation set was 0.759 and 0.783, respectively. Ten best radiomics features of LAESCC were selected, and AUC of the established radiomics model in training set and validation set was 0.770 and 0.730, respectively. The AUC of combined model in training and validation set was 0.838 and 0.826, respectively. The AUC of CT radiomics combined with CT and preoperative pathological features in training set was higher than that of CT-preoperative pathologymodel and radiomics model (both P<0.01). Conclusion CT radiomics combined with CT and preoperative pathological features could effectively predict postoperative ER of LAESCC.
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