论著

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

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  • 上海交通大学医学院附属瑞金医院放射科,上海 200025

收稿日期: 2022-12-14

  网络出版日期: 2023-08-31

基金资助

国家自然科学基金(82271934)

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

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  • Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

Received date: 2022-12-14

  Online published: 2023-08-31

摘要

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(T1a~T1b期)者22例,进展期胃癌(T2~T3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=0.049 8)、A-V-50(P=0.047 7)、AV-100(P=0.012 3)。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。

本文引用格式

颜凌, 王凌云, 陈勇, 杜联军 . 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[J]. 诊断学理论与实践, 2023 , 22(02) : 154 -159 . DOI: 10.16150/j.1671-2870.2023.02.008

Abstract

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 (T1a-T1b) and 23 cases as having advanced gastric cancer (T2-T3). 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.

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