Combinative evaluation of primary tumor and lymph nodes to predict pelvic lymphatic metastasis in cervical cancer: an integrated PET-IVIM MRI study
Year: 2020
Type: article
Study design: Observational Study
Abstract: Background The aim of this study was to evaluate the value of combining pelvic lymph node and tumor characteristics on positron emission tomography-intravoxel incoherent motion magnetic resonance (PET-IVIM MR) imaging for predicting lymph node metastasis in patients with cervical cancer, especially in those with negative lymph nodes on PET. Methods The medical records of 95 patients with cervical cancer who underwent surgical resection with pelvic lymph node dissection were evaluated. The patients were divided into negative and positive groups according to postoperative pathologic lymph node diagnosis, and comparisons of the PET and IVIM-derived parameters between the two groups were performed. Univariate and multivariate analyses were performed to construct a predictive model of lymph node metastasis. Results For all patients, tumor SUVmax, TLG, Dmin, PET and MRI for lymph node diagnosis showed significant differences between patients with and without confirmed lymph node metastasis. Univariate and multivariate logistic analysis showed that the combination of tumor TLG, Dmin and PET for lymph node diagnosis had the strongest predictive value (AUC 0.913, p < 0.001). For patients with PET-negative lymph nodes, SUVmax, SUVmean, MTV, TLG, and Dmin showed significant between-group differences, and univariate and multivariate logistic analysis showed that TLG had the strongest predictive value. Conclusions The combination of tumorTLG, Dmin and PET for lymph node diagnosis is a powerful prognostic factor for all patients. TLG has the best predictive performance in patients with PET negative lymph nodes.
Source: Cancer Imaging
Authors: Chen Xu, Xiaoran Li, Yanchi Shi, Bo Wang, Hongzan Sun
Institutions: China Medical University, Sheng Jing Hospital
Language: English
Topic: MRI in cancer diagnosis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,145
Award: G938449949
OpenAlex ID: W3010962286