Reliability study of MRI in detecting lymph node metastasis of cervical cancer: a multi-center retrospective study result analysis
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Magnetic resonance imaging (MRI) currently stands as a cornerstone in preoperative staging and detection of lymph node metastasis (LNM) in cervical cancer (CC). The objective of this study was to assess the efficacy of MRI in discerning LNM in CC. Methods: This retrospective study scrutinized patient data across three medical centers, focusing on those who underwent preoperative MRI assessment followed by radical surgery and pelvic lymph node (LN) dissection. We juxtaposed the correspondence between LN status as determined by MRI and pathological findings, conducting comprehensive data analysis through statistical software. In addition, the factors that may lead to misinterpretation of MRI were analyzed. Results: Out of a cohort comprising 1,027 patients, 10.9% (112/1,027) exhibited LN positivity on MRI, with 49.1% (55/112) confirmed as LNM upon pathological examination. Among patients with negative MRI findings, around 15.7% (144/915) were later diagnosed with LNM. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MRI for assessing LNM were determined to be 27.6%, 93.0%, 49.1%, and 84.3%, respectively. LNM in CC patients is associated with MRI findings, squamous cell carcinoma antigen (SCCA), carbohydrate antigen 125 (CA125), and carcinoembryonic antigen (CEA). The SCCA level of the misjudgment group was significantly higher than that of the accurate group. Conclusions: Preoperative MRI exhibits limitations in sensitivity and accuracy when discerning metastatic from non-metastatic LNs in CC. Therefore, categorizing patients as stage IIIC solely based on MRI-detected LN positivity warrants further scrutiny.
Authors: Lixiao Liu, Luyao Pan, Chengyang Zou, Fubin Zhang, Yongmin Du, Tianhong Zhu, Jiayi Shen, Shun Lu, Jingtian Shen, Ruru Bao, Yihan Shan, Yuning Pan, Lifeng Chen, Hongyou Zhou, Xiaojian Yan, Yutao Guan
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4414484192