目的:评估基于CT影像组学的共识聚类分析在食管鳞状细胞癌(ESC)患者风险分层和术后无进展生存期(PFS)预测中的有效性。
方法:我们进行了一项回顾性研究,涉及2016年1月至2021年3月间诊断为ESC的546例患者。所有患者均行术前增强CT检查。从增强的CT图像来看,提取了影像组学特征,并应用共识聚类算法根据这些特征对患者进行分组。进行统计分析以检查聚类结果与基因蛋白表达之间的关系。组织病理学特征,和患者3年PFS。我们对连续数据应用了Kruskal-Wallis检验,分类数据的卡方或费舍尔精确检验,和PFS的对数秩检验。
结果:这项研究确定了四组:第1组(n=100,18.3%),第2组(n=197,36.1%),第3组(n=205,37.5%),和第4组(n=44,8.1%)。癌基因乳腺癌易感基因1(BRCA1)在第4簇(75%)中表达最高,显示四种亚型之间的显着差异,P值为0.035。程序性死亡-1(PD-1)的表达在簇1中最高(51%),P值为0.022。2组血管侵犯最常见(28.9%),P值为0.022。大多数T3-4期患者属于第2组(67%),P值为0.003。Kaplan-Meier生存分析显示四组间PFS差异有统计学意义(P=0.013)。其中,第1组患者预后最好,而第二组的人情况最差。
结论:本研究强调了基于增强CT影像组学特征的共识聚类分析在识别影像组学特征之间关联方面的有效性。组织病理学特征,和不同集群的预后。这些发现为临床医生准确有效地评估食管癌的预后提供了有价值的见解。
OBJECTIVE: To assess the efficacy of
consensus cluster analysis based on CT radiomics in stratifying risk and predicting postoperative progression-free survival (PFS) in patients diagnosed with esophageal squamous cell carcinoma (ESC).
METHODS: We conducted a retrospective study involving 546 patients diagnosed with ESC between January 2016 and March 2021. All patients underwent preoperative enhanced CT examinations. From the enhanced CT images, radiomics features were extracted, and a
consensus clustering algorithm was applied to group the patients based on these features. Statistical analysis was performed to examine the relationship between the clustering results and gene protein expression, histopathological features, and patients\' 3-year PFS. We applied the Kruskal-Wallis test for continuous data, chi-square or Fisher\'s exact tests for categorical data, and the log-rank test for PFS.
RESULTS: This study identified four groups: Cluster 1 (n = 100, 18.3%), Cluster 2 (n = 197, 36.1%), Cluster 3 (n = 205, 37.5%), and Cluster 4 (n = 44, 8.1%). The cancer gene Breast Cancer Susceptibility Gene 1 (BRCA1) was most highly expressed in Cluster 4 (75%), showing significant differences between the four subtypes with a P-value of 0.035. The expression of programmed death-1 (PD-1) was highest in Cluster 1 (51%), with a P-value of 0.022. Vascular invasion occurred most frequently in Cluster 2 (28.9%), with a P-value of 0.022. The majority of patients with stage T3-4 were in Cluster 2 (67%), with a P-value of 0.003. Kaplan-Meier survival analysis revealed significant differences in PFS between the four groups (P = 0.013). Among them, patients in Cluster 1 had the best prognosis, while those in Cluster 2 had the worst.
CONCLUSIONS: This study highlights the effectiveness of
consensus clustering analysis based on enhanced CT radiomics features in identifying associations between radiomics features, histopathological characteristics, and prognosis in different clusters. These findings provide valuable insights for clinicians in accurately and effectively evaluating the prognosis of esophageal cancer.