Zhang Longjia, Diao Boyu, Fan Zhiyao, Zhan Hanxiang
Division of Pancreatic Surgery, Department of General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong Province, China (L.Z., B.D., Z.F., H.Z.).
Division of Pancreatic Surgery, Department of General Surgery, Qilu Hospital, Shandong University, Jinan, Shandong Province, China (L.Z., B.D., Z.F., H.Z.).
Acad Radiol. 2025 May;32(5):2679-2688. doi: 10.1016/j.acra.2024.11.047. Epub 2024 Dec 7.
As pancreatic cystic neoplasms (PCN) differ in current standard of care, and these treatments can affect quality of life to varying degrees, a definitive preoperative diagnosis must be reliable. Current diagnostic approaches, specifically traditional cross-sectional imaging techniques, face certain limitations. But radiomics has been shown to have high diagnostic accuracy across a range of diseases. Objective to conduct a comprehensive review of the literature on the use of radiomics to differentiate Mucinous Cystic Neoplasm (MCN) from Serous Cystic Neoplasm (SCN).
This study was comprehensively searched in Pubmed, Scopus and Web of Science databases for meta-analysis of studies that used radiomics to distinguish MCN from SCN. Risk of bias was assessed using the diagnostic accuracy study quality assessment method and combined with sensitivity, specificity, diagnostic odds ratio, and summary receiver operating characteristic (SROC)curve analysis.
A total of 884 patients from 8 studies were included in this analysis, including 365 MCN and 519 SCN. The Meta-analysis found that radiomics identified MCN and SCN with high sensitivity and specificity, with combined sensitivity and specificity of 0.84(0.82-0.87) and 0.82(0.79-0.84). The positive likelihood ratio (PLR) and the negative likelihood ratio (NLR) are 5.61(3.72, 8.47) and 0.14(0.09-0.26). In addition, the area under the SROC curve (AUC) was drawn at 0.93. No significant risk of publication bias was detected through the funnel plot analysis. The performances of feature extraction from the volume of interest (VOI) or Using AI classifier in the radiomics models were superior to those of protocols employing region of interest (ROI) or absence of AI classifier.
This meta-analysis demonstrates that radiomics exhibits high sensitivity and specificity in distinguishing between MCN and SCN, and has the potential to become a reliable diagnostic tool for their identification.
由于胰腺囊性肿瘤（PCN）的当前治疗标准不同，且这些治疗会对生活质量产生不同程度的影响，因此术前明确诊断必须可靠。目前的诊断方法，特别是传统的横断面成像技术，存在一定局限性。但已证明放射组学在一系列疾病中具有较高的诊断准确性。目的是对利用放射组学区分黏液性囊性肿瘤（MCN）和浆液性囊性肿瘤（SCN）的文献进行全面综述。
本研究在PubMed、Scopus和Web of Science数据库中进行全面检索，以对使用放射组学区分MCN和SCN的研究进行荟萃分析。使用诊断准确性研究质量评估方法评估偏倚风险，并结合敏感性、特异性、诊断比值比和汇总受试者工作特征（SROC）曲线分析。
本分析共纳入8项研究中的884例患者，其中MCN 365例，SCN 519例。荟萃分析发现，放射组学识别MCN和SCN具有较高的敏感性和特异性，联合敏感性和特异性分别为0.84（0.82 - 0.87）和0.82（0.79 - 0.84）。阳性似然比（PLR）和阴性似然比（NLR）分别为5.61（3.72，8.47）和0.14（0.09 - 0.26）。此外，SROC曲线下面积（AUC）为0.93。通过漏斗图分析未检测到明显的发表偏倚风险。放射组学模型中从感兴趣体积（VOI）提取特征或使用人工智能分类器的性能优于采用感兴趣区域（ROI）或无人工智能分类器的方案。
本荟萃分析表明，放射组学在区分MCN和SCN方面具有较高的敏感性和特异性，有潜力成为识别它们的可靠诊断工具。
