目的:微血管侵犯(MVI)是公认的与肝细胞癌(HCC)患者预后较差相关的生物标志物。双能量计算机断层扫描(DECT)是一种高度敏感的技术,可以确定肿瘤中的碘浓度(IC)并提供内部微循环灌注的间接评估。这项研究旨在评估DECT与实验室数据的结合是否可以改善术前MVI预测。
方法:这项回顾性研究纳入了119例术前在两个医疗中心接受DECT肝血管造影的患者。为了比较MVI阴性和阳性组的DECT参数和实验室检查结果,使用Mann-WhitneyU检验。此外,进行主成分分析(PCA)以确定基本成分。采用Mann-WhitneyU检验确定MVI组的PC评分是否不同。最后,使用一般线性分类器评估各主成分(PC)评分的分类能力.
结果:甲胎蛋白(AFP)水平存在显着差异(P<0.05),归一化动脉期IC,主数据集和验证数据集中MVI组之间的标准化入口阶段IC。PC1-PC4占主要数据集中方差的67.9%,载荷为24.1%,16%,15.4%,和12.4%,分别。在主数据集和验证数据集中,PC3和PC4在MVI组之间有显著差异,曲线下面积值分别为0.8410和0.8373。
结论:基于不同因子负荷的DECT碘浓度和实验室特征的重组可以很好地预测术前MVI。
结论:利用主成分分析,双能计算机断层扫描碘浓度与实验室特征的融合,考虑到不同的因子负荷,在准确分类微血管侵犯方面显示出实质性的希望。建立这种组合的努力有限,为理解相关研究工作中的数据提供了一种新的范式。
OBJECTIVE: Microvascular invasion (MVI) is a recognized biomarker associated with poorer prognosis in patients with hepatocellular carcinoma. Dual-energy computed tomography (DECT) is a highly sensitive technique that can determine the iodine concentration (IC) in tumour and provide an indirect evaluation of internal microcirculatory perfusion. This study aimed to assess whether the combination of DECT with laboratory data can improve preoperative MVI prediction.
METHODS: This retrospective study enrolled 119 patients who underwent DECT liver angiography at 2 medical centres preoperatively. To compare DECT parameters and laboratory findings between MVI-negative and MVI-positive groups, Mann-Whitney U test was used. Additionally, principal component analysis (PCA) was conducted to determine fundamental components. Mann-Whitney U test was applied to determine whether the principal component (PC) scores varied across MVI groups. Finally, a general linear classifier was used to assess the classification ability of each PC score.
RESULTS: Significant differences were noted (P < .05) in alpha-fetoprotein (AFP) level, normalized arterial phase IC, and normalized portal phase IC between the MVI groups in the primary and validation datasets. The PC1-PC4 accounted for 67.9% of the variance in the primary dataset, with loadings of 24.1%, 16%, 15.4%, and 12.4%, respectively. In both primary and validation datasets, PC3 and PC4 were significantly different across MVI groups, with area under the curve values of 0.8410 and 0.8373, respectively.
CONCLUSIONS: The recombination of DECT IC and laboratory features based on varying factor loadings can well predict MVI preoperatively.
CONCLUSIONS: Utilizing PCA, the amalgamation of DECT IC and laboratory features, considering diverse factor loadings, showed substantial promise in accurately classifying MVI. There have been limited endeavours to establish such a combination, offering a novel paradigm for comprehending data in related research endeavours.