18F-FDG PET/CT 在诊断和分级偶发结直肠腺瘤中的价值。

Z Qi, K Tang, X Lu, Y Zhu, N Xu
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引用次数: 0

摘要

目的:结直肠腺瘤(CRA)的组织学分级越高,发展为结直肠癌(CRC)的风险越高。本研究探讨了 18F- 氟脱氧葡萄糖正电子发射计算机断层扫描(18F-FDG PET/CT)的最大标准化摄取值(SUVmax)与 CRA 组织学分级之间的关系,并构建了区分不同组织学分级的最佳回归模型:本研究回顾性分析了153例在PET/CT中偶然发现结直肠18F-FDG摄取的CRA患者的数据。根据组织学分级将患者分为低级别上皮内瘤变(LGIN)和高级别上皮内瘤变(HGIN)两组。在分析了术前 18F-FDG PET/CT 扫描测得的 SUVmax 与组织学分级之间的关系后,分析了接收器操作特征(ROC)曲线,以确定区分两组的最佳临界值。研究人员纳入了常见的临床和病理因素,并对其进行了单变量和多变量逻辑回归分析,以确定独立的风险因素。通过多变量逻辑回归分析,建立了一个综合 SUVmax 和几个风险因素的诊断模型:两组间的 SUVmax 有明显差异(P涉及 18F-FDG PET/CT SUVmax 的回归模型可区分 CRA 的组织学分级,因此可作为准确诊断 CRA 的无创工具,并有助于在手术前制定针对患者的治疗策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Value of 18F-FDG PET/CT in the diagnosis and grading of incidental colorectal adenomas.

Purpose: Colorectal adenomas (CRAs) are at a higher risk of progressing to colorectal cancer (CRC) as their histological grade increases. Herein, this study investigated the relationship between the maximum standardized uptake value (SUVmax) on 18F-fluorodeoxyglucose positron emission tomography-computed tomography (18F-FDG PET/CT) and the histological grades of CRAs and constructed the optimal regression model for distinguishing between different histological grades.

Methods: This study retrospectively analyzed the data of 153 patients with CRAs who had colorectal 18F-FDG uptake incidentally found on PET/CT. The patients were categorized into low-grade intraepithelial neoplasia (LGIN) and high-grade intraepithelial neoplasia (HGIN) groups based on their histological grade. After the analysis of the relationship between SUVmax measured on preoperative 18F-FDG PET/CT scans and histological grades, receiver-operating characteristic (ROC) curves were analyzed to determine the optimal cut-off values for distinguishing between the two groups. Common clinical and pathological factors were included and subjected to univariate and multivariate logistic regression analyses to identify independent risk factors. A diagnostic model integrating SUVmax and several risk factors was developed with the multivariate logistic regression analysis.

Results: SUVmax was significantly different between the two groups (P < 0.001) and increased with an elevation in the malignancy degree. The area under the ROC curve (AUC) for identifying LGIN and HGIN was 0.796, and the AUC of the combination model was 0.822. Furthermore, SUVmax was an independent risk factor for distinguishing between different histological grades in pairwise comparisons.

Conclusion: The regression model involving SUVmax on 18F-FDG PET/CT can distinguish between histological grades of CRAs, which therefore can be used as a noninvasive tool for the accurate diagnosis of CRAs and assist in developing patient-specific treatment strategies before surgery.

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