双能CT图像深度学习重建算法在胃癌术前T分期中的应用

Application of deep learning image reconstruction algorithm in dual-energy CT scanning for preoperative T sta-ging of gastric cancer

目的:探讨深度学习图像重建(deep learning image reconstruction,DLIR)算法应用于术前双能CT评估胃癌T分期中的价值。方法:回顾分析本院2022年1月至2022年2月期间,经术后病理确诊的45例胃癌患者的术前双能CT检查资料,其中静脉期双能扫描原始数据,以标准重建核分别使用滤波反投影重建(filtered back projection,FBP),自适应统计迭代重建(adaptive statistical iterative reconstruction-V,Asir-V)权重50%(AV-50)和权重100%(AV-100),和DLIR中档(deep learning image reconstruction-mid-range,DLIR-M)算法,重建1.25 mm层厚的50 keV能级虚拟单能图像,随后由具有5年和10年胃肠道肿瘤诊断经验的2名放射科医师协商进行胃癌T分期诊断。45例患者中,术后病理学诊断为早期胃癌(T_(1a)~T_(1b)期)者22例,进展期胃癌(T_(2)~T_(3)期)者23例。以病理结果为金标准,采用受试者操作特征曲线的曲线下面积(area under curve,AUC)比较不同重建算法图像评估胃癌T分期的效能。结果:双能CT检查FBP、AV-50、AV-100和DLIR-M算法在术前判断胃癌T分的受试者操作特征曲线的AUC分别为0.638、0.667、0.577和0.867。DLIR-M图像的AUC高于FBP(P=0.0498)、A-V-50(P=0.0477)、AV-100(P=0.0123)。结论:与传统的FBP、Asir-V等CT重建算法相比,DLIR-M算法能进一步提高双能CT检查在术前判断胃癌T分期的准确率,对临床治疗方案选择更具指导意义。

Objective:To investigate the value of deep learning image reconstruction(DLIR)algorithm in dual-energy CT scanning for preoperative T staging of gastric cancer.Methods:Data from preoperative dual-energy CT of 45 patients with pathology-confirmed gastric cancer during January 2022 to February 2022 were retrospectively analyzed.The raw data of dual-energy scanning in venous phase were reconstructed by filtered back projection(FBP),adaptive statistical iterative reconstruction-V with a weight of 50%(AV-50)and of 100%(AV-100),and deep learning image reconstruction-mid-range(DLIR-M)algorithms.Then these images were used to reconstruct a 50 keV level virtual mono-energy image with a 1.25mm layer thickness.Images were reviewed by two radiologists with 5 and 10 years of experience in T staging of gastrointestinal tumors.It revealed that 22 cases were diagnosed pathologically as having early gastric cancer(T_(1a)-T_(1b))and 23 cases as having advanced gastric cancer(T_(2)-T_(3)).Diagnostic accuracy of different reconstruction algorithms for T staging of gastric cancer were calculated using area under the receiver operating curve(AUC),with pathology results as the golden standard.Results:The AUC of the reconstructed dual-energy CT images based on FBP,AV-50,AV-100 and DLIR-M algorithms were 0.638,0.667,0.577 and 0.867,respectively.The AUC of DLIR-M images was significantly higher than those of FBP(P=0.0498),AV-50(P=0.0477)and AV-100(P=0.0123)images.Conclusions:Compared with traditional reconstruction algorithms of FBP and Asir-V,DLIR algorithm may further improve the accuracy of dual-energy CT scanning in preoperative T staging of gastric cancer.DLIR-M is significant for treatment decision-making.