Machine learning-based identification of contrast-enhancement phase of computed tomography scans.
Machine learning-based identification of contrast-enhancement phase of computed tomography scans.
复制标题
DOI：
10.1371/journal.pone.0294581
复制
发表时间：
2024
期刊：
影响因子：
3.7
通讯作者：
中科院分区：
文献类型：
--
作者：
对比增强计算机断层扫描（CECT）通常用于评价不同的临床情况，包括肝细胞癌（HCC）的检测和表征。定量医学图像分析已经成为一个指数增长的科学领域。许多研究报告了对比增强阶段的变化对从CT扫描中提取的定量成像特征的再现性的影响。相位增强的识别和标记是一项耗时的任务，目前需要一种准确的自动标记算法来识别CT扫描的增强相位。在这项研究中，我们研究了机器学习算法标记59名使用动态对比增强CT协议扫描的HCC患者数据集中的阶段的能力。地面实况标签由放射科专家提供。在主动脉、门静脉和肝脏内定义感兴趣区域。从这些感兴趣区域提取平均密度值，并用于机器学习建模。使用准确度、曲线下面积（AUC）和Matthew相关系数（MCC）评价模型。我们在外部数据集（76名患者）上测试了算法。我们的研究结果表明，几种监督学习算法（逻辑回归，随机森林等）。执行类似，我们开发的算法可以准确地分类对比度增强的相位。
Contrast-enhanced computed tomography scans (CECT) are routinely used in the evaluation of different clinical scenarios, including the detection and characterization of hepatocellular carcinoma (HCC). Quantitative medical image analysis has been an exponentially growing scientific field. A number of studies reported on the effects of variations in the contrast enhancement phase on the reproducibility of quantitative imaging features extracted from CT scans. The identification and labeling of phase enhancement is a time-consuming task, with a current need for an accurate automated labeling algorithm to identify the enhancement phase of CT scans. In this study, we investigated the ability of machine learning algorithms to label the phases in a dataset of 59 HCC patients scanned with a dynamic contrast-enhanced CT protocol. The ground truth labels were provided by expert radiologists. Regions of interest were defined within the aorta, the portal vein, and the liver. Mean density values were extracted from those regions of interest and used for machine learning modeling. Models were evaluated using accuracy, the area under the curve (AUC), and Matthew’s correlation coefficient (MCC). We tested the algorithms on an external dataset (76 patients). Our results indicate that several supervised learning algorithms (logistic regression, random forest, etc.) performed similarly, and our developed algorithms can accurately classify the phase of contrast enhancement.
登录
查看更多内容
影响因子：
3.3
作者：
Dercle, Laurent;Ma, Jingchen;Xie, Chuanmiao;Chen, Ai-ping;Wang, Deling;Luk, Lyndon;Revel-Mouroz, Paul;Otal, Philippe;Peron, Jean-Marie;Rousseau, Herve;Lu, Lin;Schwartz, Lawrence H.;Mokrane, Fatima-Zohra;Zhao, Binsheng
通讯作者：
Zhao, Binsheng
影响因子：
3.5
作者：
Ibragimov B;Toesca D;Chang D;Koong A;Xing L
通讯作者：
Xing L
影响因子：
45.3
作者：
Choi, Haesun;Charnsangavej, Chuslip;Benjamin, Robert S.
通讯作者：
Benjamin, Robert S.
影响因子：
5
作者：
Bae, Kyongtae T.;Shah, Amisha J.;Hildebolt, Charles F.
通讯作者：
Hildebolt, Charles F.
影响因子：
--
作者：
Cui, Yong;Li, Zhong-Wu;Sun, Ying-Shi
通讯作者：
Sun, Ying-Shi