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Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study

  • Gynecologic Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Purpose

This study investigated predictors of cervical cancer (CC) recurrence from native T1 mapping, conventional imaging, and clinicopathologic metrics.

Patients and Methods

In total, 144 patients with histopathologically confirmed CC (90 with and 54 without surgical treatment) were enrolled in this prospective study. Native T1 relaxation time, conventional imaging, and clinicopathologic characteristics were acquired. The association of quantitative and qualitative parameters with post-treatment tumor recurrence was assessed using univariate and multivariate Cox proportional hazard regression analyses. Independent risk factors were combined into a model and individual prognostic index equation for predicting recurrence risk. The receiver operating characteristic (ROC) curve determined the optimal cutoff point.

Results

In total, 12 of 90 (13.3%) surgically treated patients experienced tumor recurrence. Native T1 values (X1) [hazard ratio (HR) 1.008; 95% confidence interval (CI) 1.001–1.016], maximum tumor diameter (X2) (HR 1.065; 95% CI 1.020–1.113), and parametrial invasion (X3) (HR 3.930; 95% CI 1.013–15.251) were independent tumor recurrence risk factors. The individual prognostic index (PI) of the established recurrence risk model was PI = 0.008X1 + 0.063X2 + 1.369X3. The area under the ROC curve (AUC) of the Cox regression model was 0.923. A total of 20 of 54 (37.0%) non-surgical patients experienced tumor recurrence. Native T1 values (X1) (HR 1.012; 95% CI 1.007–1.016) and lymph node metastasis (X2) (HR 4.064; 95% CI 1.378–11.990) were independent tumor recurrence risk factors. The corresponding PI was calculated as follows: PI = 0.011X1 + 1.402X2; the Cox regression model AUC was 0.921.

Conclusions

Native T1 values combined with conventional imaging and clinicopathologic variables could facilitate the pretreatment prediction of CC recurrence.

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Acknowledgment

The authors would like to thank Siemens Healthcare China in Beijing for their support, as well as Mingyang Ding and Fangfang Li for their assistance in data collection.

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Author notes

  1. Jie Liu and Shujian Li have equally contributed to this work.

Authors and Affiliations

  1. Department of MRI, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

    Jie Liu MD, Shujian Li MD, Yong Zhang MD & Jingliang Cheng MD

  2. Department of Radiotreatment, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province, China

    Qinchen Cao MD

  3. MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany

    Marcel Dominik Nickel PhD

  4. MR Collaboration, Siemens Healthineers Ltd., Xicheng District, Beijing, China

    Jinxia Zhu PhD

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JL, SL, QC, YZ, MDN, JZ, and JC declare they have no potential conflict of interest.

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Liu, J., Li, S., Cao, Q. et al. Prediction of Recurrent Cervical Cancer in 2-Year Follow-Up After Treatment Based on Quantitative and Qualitative Magnetic Resonance Imaging Parameters: A Preliminary Study. Ann Surg Oncol 30, 5577–5585 (2023). https://doi.org/10.1245/s10434-023-13756-1

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