Yang Chongshuang, Hassan Hasyma Abu, Omar Nur Farhayu, Soo Tze Hui, Shuib Bin Yahaya Ahmad, Shi Tianliang, Luo Yinbin, Wu Min
Department of Radiology, Faculty of Medicine and Health Sciences, Universiti Putra Malaysia, 43400, UPM Serdang, Selangor, Malaysia.
Department of Radiology, Tongren People's Hospital, Tongren, Guizhou Province, 554300, China.
Heliyon. 2024 Nov 8;10(22):e40291. doi: 10.1016/j.heliyon.2024.e40291. eCollection 2024 Nov 30.
To assess the effectiveness of Amide Proton Transfer (APT) imaging in predicting the histopathological characteristics of cervical cancer.
A comprehensive literature search was conducted across multiple databases, covering studies until December 27, 2023. The meta-analysis was performed using Stata 15 and Review Manager 5.4 software. Key metrics analyzed included pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio (DOR), and summary receiver operating characteristic curves. The analysis focused on differentiating cervical cancer types, squamous carcinoma differentiation, and lymph node involvement. Meta-regression was employed to investigate heterogeneity.
Thirteen studies involving 868 patients were included in the meta-analysis. For differentiating adenocarcinoma from squamous carcinoma, the pooled sensitivity was 0.82 (95%CI: 0.71-0.90), specificity was 0.65 (95%CI: 0.48-0.79), and DOR was 9 (95%CI: 1.6-3.5). When distinguishing poorly differentiated from moderately/well-differentiated squamous carcinoma, the sensitivity was 0.74 (95%CI: 0.66-0.81), specificity was 0.83 (95%CI: 0.75-0.89), and DOR was 14 (95%CI: 8-23). For identifying lymph node involvement, the sensitivity was 0.87 (95%CI: 0.78-0.92), specificity was 0.66 (95%CI: 0.59-0.73), and DOR was 13 (95%CI: 7-26). No publication bias was detected.
APT imaging demonstrates high sensitivity and specificity in distinguishing between cervical cancer types, grading squamous carcinoma, and detecting lymph node involvement. It can be considered a reliable technique for predicting the pathological features of cervical cancer in clinical practice.
评估酰胺质子转移(APT)成像在预测宫颈癌组织病理学特征方面的有效性。
在多个数据库中进行了全面的文献检索,涵盖截至2023年12月27日的研究。使用Stata 15和Review Manager 5.4软件进行荟萃分析。分析的关键指标包括合并敏感度、特异度、阳性似然比、阴性似然比、诊断比值比(DOR)和汇总受试者工作特征曲线。分析重点在于区分宫颈癌类型、鳞状细胞癌分化程度和淋巴结受累情况。采用Meta回归研究异质性。
荟萃分析纳入了13项研究,共868例患者。在区分腺癌与鳞状细胞癌方面,合并敏感度为0.82(95%CI:0.71 - 0.90),特异度为0.65(95%CI:0.48 - 0.79),DOR为9(95%CI:1.6 - 3.5)。在区分低分化与中/高分化鳞状细胞癌时,敏感度为0.74(95%CI:0.66 - 0.81),特异度为0.83(95%CI:0.75 - 0.89),DOR为14(95%CI:8 - 23)。在识别淋巴结受累方面,敏感度为0.87(95%CI:0.78 - 0.92),特异度为0.66(95%CI:0.59 - 0.73),DOR为13(95%CI:7 - 26)。未检测到发表偏倚。
APT成像在区分宫颈癌类型、鳞状细胞癌分级和检测淋巴结受累方面表现出高敏感度和特异度。在临床实践中,它可被视为预测宫颈癌病理特征的可靠技术。