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A gene-based predictive model for lymph node metastasis in cervical cancer: superior performance over imaging
Dongdong Xu1,2, Xibo Zhao1,2, Dongdong Ye1,2
1Department of Gynecological Oncology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China.
Journal of Translational Medicine
|April 4, 2025
Summary
A new gene expression model accurately predicts lymph node metastasis (LNM) in cervical cancer, outperforming CT and MRI. This tool improves diagnostic accuracy and sensitivity for better treatment planning.
Area of Science:
- Oncology
- Genomics
- Medical Imaging
Background:
- Lymph node metastasis (LNM) is a critical factor in cervical cancer prognosis and treatment.
- Current imaging methods like CT and MRI have limitations in accurately assessing lymph node status.
Purpose of the Study:
- To develop a more accurate and efficient method for predicting LNM in cervical cancer.
- To compare the diagnostic performance of the new predictive model against traditional imaging techniques.
Main Methods:
- Merged three independent cohorts into training, internal validation, and external validation groups.
- Utilized LASSO regression and multivariate logistic regression to identify predictive genes and construct a predictive model.
- Compared the model's diagnostic accuracy, sensitivity, and specificity against CT and MRI.
Main Results:
- Identified four predictive genes (MAPT, EPB41L1, ACSL5, PRPF4B) and developed an LNM risk score model.
- The gene expression model achieved higher accuracy (0.840) and sensitivity (0.804) compared to CT/MRI (0.713 and 0.587, respectively).
- The model corrected 81% of CT/MRI misdiagnoses, demonstrating significant improvements in diagnostic efficiency.
Conclusions:
- A novel gene expression-based predictive model significantly enhances preoperative assessment of LNM in cervical cancer.
- This model offers superior sensitivity and accuracy over conventional imaging, providing a foundation for precise diagnostic tools.
- The findings support the development of individualized treatment planning for cervical cancer patients.
