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• Clinical Article •
Clinical-radiomic analysis of multi-parametric magnetic resonance imaging predicts lymphovascular space invasion and outcomes in cervical cancer
CUI Yaqiong  HUANG Gang  WANG Lili  REN Jialiang  ZHAO Lianping  ZHOU Xing  MA Ying 

Cite this article as: CUI Y Q, HUANG G, WANG L L, et al. Clinical-radiomic analysis of multi-parametric magnetic resonance imaging predicts lymphovascular space invasion and outcomes in cervical cancer[J]. Chin J Magn Reson Imaging, 2023, 14(2): 73-82. DOI:10.12015/issn.1674-8034.2023.02.013.


[Abstract] Objective The surgical outcomes for patients with cervical cancer (CC) are impaired by lymphovascular space invasion (LVSI). We analyzed the predictive efficacy of radiomic features extracted from pretreatment multi-parameter magnetic resonance imaging (mpMRI) to predict LVSI and clinical outcomes in CC patients due to the lack of a reliable indicator to predict LVSI before surgery.Materials and Methods A retrospective analysis of 125 individuals with CC was performed. We carried out a radiomic-based characterization on the pretreatment mpMRI to develop and validate a noninvasive imaging biomarker capable of distinguishing between LVSI + and LVSI-. The small field of view high-resolution T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC), T2WI, and contrast-enhanced T1-weighted were included in the image modalities. The volume of interest of six different sequence images contained 107 extracted features in total. These features were then chosen using univariate analysis, LASSO, and stepwise logistic regression analysis. A Rad-score and 14 clinical factors were integrated into the combined (COMB) model, a stepwise logistic regression-based prediction model. Twenty times 3-fold cross-validation was repeated. The progression-free survival (PFS) survival curve was divided based on the follow-up results and the predicted LVSI status, and a difference in the model for the PFS grouping was observed.Results Radiomics related to intratumoral heterogeneity served as the primary indicator for LVSI prediction. The corresponding Rad-score varied considerably depending on the LVSI status (P＜0.001). Multivariate logistics identified 3 LVSI risk variables. The Rad-score was more important than squamous cell carcinoma antigen and hemoglobin [odds ratio (OR): 2.626, 1.061, 0.982]. The radiomic model has an area under the curve (AUC) in the training cohort of 0.823. The COMB model predicted a substantial difference in PFS between the LVSI + and LVSI-groups (median PFS: 64.8 vs. 58.3 months).Conclusions The LVSI status and clinical outcome of CC patients could be predicted using radiomics features in combination with mpMRI radiomics and clinical variates. It may show utility for improving patient stratification strategies in neoadjuvant and surgical settings. The potential of radiomic features to predict tumor prognosis may be connected to their capacity to reflect the histology of LVSI.
[Keywords] uterine cervical neoplasms；lymphovascular space invasion；magnetic resonance imaging；radiomics；prognosis

CUI Yaqiong1   HUANG Gang1*   WANG Lili1   REN Jialiang2   ZHAO Lianping1   ZHOU Xing1   MA Ying1  

1 Department of Radiology, Gansu Provincial Hospital, Lanzhou 730000, China

2 GE Healthcare China, Shanghai 200203, China

*Correspondence to: Huang G, E-mail: keen0999@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS The Project of Gansu Provincial Health Commission (No. GSWSKY2020-15); the Grant from the Gansu Provincial Hospital (No. 20GSSY1-18).
Received  2022-08-08
Accepted  2022-12-21
DOI: 10.12015/issn.1674-8034.2023.02.013
Cite this article as: CUI Y Q, HUANG G, WANG L L, et al. Clinical-radiomic analysis of multi-parametric magnetic resonance imaging predicts lymphovascular space invasion and outcomes in cervical cancer[J]. Chin J Magn Reson Imaging, 2023, 14(2): 73-82. DOI:10.12015/issn.1674-8034.2023.02.013.

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