基于食管癌动脉期CT图像的深度学习和影像组学特征预测其T2 T3分期

Discriminating between T2 and T3 staging in patients with esophageal cancer using deep learning and radiomic features based on arterial phase CT imaging

目的:探讨基于食管癌增强动脉期CT图像的深度学习及影像组学特征联合临床资料预测其T2、T3分期。方法:回顾性收集2015年5月至2024年4月皖南医学院第一附属医院经病理学确诊的388例食管癌患者的临床及CT资料,按照7:3比例随机分为训练集(271例)和验证集(117例)。提取食管癌增强CT动脉期图像的影像组学及深度学习特征,使用最小绝对收缩和选择算子算法进行降维和筛选特征,并分别建立组学标签得分(Radscore)和深度学习标签得分(Deepscore)。采用单因素及多因素Logistic回归分析筛选独立危险因素,构建临床、影像组学、深度学习和联合模型,并绘制联合模型列线图。使用受试者工作特征曲线下面积(AUC)评价模型的诊断效能并用DeLong检验比较其差异,用决策曲线评价模型的临床净收益,校正曲线评价模型的校准度。结果:经降维后筛选出9个影像组学特征和12个深度学习特征。多因素Logistic回归分析显示肿瘤长度、边界、Radscore及Deepscore为鉴别食管癌T2、T3分期的独立危险因素。联合模型的AUC在训练集为0.867,与临床模型(0.774,P<0.001)、影像组学模型(0.795,P<0.001)和深度学习模型(0.821,P=0.001)之间的差异均有统计学意义;在验证集为0.810,与临床模型(0.653,P=0.002)、影像组学模型(0.719,P=0.033)、深度学习模型(0.750,P=0.009)之间的差异均有统计学意义。决策曲线显示联合模型在训练集及验证集的临床获益均最高,校正曲线显示联合模型在训练集及验证集均拟合良好(P=0.084、0.053)。结论:基于食管癌增强动脉期的CT图像的深度学习和影像组学特征,结合临床特征能较准确地预测其术前的T2、T3分期,可辅助临床制定治疗方案。

Objective:To investigate the application of combined deep learning and radiomic features derived from enhanced arterial phase CT imaging with clinical data to differentiate between T2 and T3 staging in patients with esophageal cancer.Methods:A retrospective study was conducted using clinical and CT data from 388 patients with pathologically confirmed esophageal cancer treated at The First Affiliated Hospital of Wannan Medical College between May 2015 and April 2024.The dataset was randomly divided into a training set(271 cases)and validation set(117 cases)in a 7:3 ratio.Radiomic and deep learning features were extracted from enhanced arterial phase CT images.The least absolute shrinkage and selection operator algorithm was employed for feature reduction and selection,leading to the development of radiomic(Radscore)and deep learning(Deepscore)scores.Univariate and multivariate Logistic regression analyses were conducted to identify independent risk factors,and clinical,radiomic,deep learning,and combined models were constructed.A nomogram was generated for the combined model.The diagnostic performance of the models was evaluated using the area under the receiver operating characteristic curve(AUC)and compared using the DeLong test.Clinical net benefit was assessed through decision curve analysis,and model calibration was evaluated using calibration curves.Results:Nine radiomicand 12 deep learning features were selected after dimensionality reduction.Multivariate Logistic regression identified tumor length,boundary,Radscore,and Deepscore as independent risk factors for distinguishing between T2 and T3 staging.In the training set,the AUC of the combined model was 0.867,which was significantly higher than that of the clinical(0.774,P<0.001),radiomic(0.795,P<0.001),and deep learning(0.821,P=0.001)models.In the validation set,the AUC of the combined model was 0.810,which was significantly higher than that of the clinical(0.653,P=0.002),radiomic(0.719,P=0.033),and deep learn-ing(0.750,P=0.009)models.The decision curve analysis indicated that the combined model provided the highest clinical benefit in both datasets.The calibration curves demonstrated a good fit for both datasets(P=0.084,0.053).Conclusion:The integration of deep learning and radiomic features obtained from enhanced arterial phase CT images with clinical data offers a reliable method for accurately distinguishing between preoperative T2 and T3 staging in esophageal cancer,thereby supporting clinical decision-making for treatment planning.