Multi-sequence magnetic resonance imaging radiomics integrated with clinical features predicts pelvic lymph node metastasis in cervical cancer: a retrospective-prospective cohort study
4 affiliations
- 1Department of Radiology, Yichang Central People’s Hospital, Yichang, Hubei, China
- 2Department of Nursing, Yichang Central People’s Hospital, Yichang, Hubei, China
- 3School of Automation and Intelligence, Beijing Jiaotong University, Beijing, China
- 4Department of Thoracic Surgery, Beijing Genertec Aerospace Hospital, Beijing, China
Abstract
Background: Pelvic lymph node metastasis is a major determinant of staging, recurrence risk, and treatment selection in cervical cancer, yet conventional magnetic resonance imaging criteria based on nodal size and morphology remain insufficient for occult nodal disease. We developed and temporally validated a multi-sequence magnetic resonance imaging radiomics-clinical fusion framework for pretreatment prediction of pelvic lymph node metastasis.
Methods: This single-center retrospective-prospective cohort study included 200 women with histologically confirmed cervical cancer who underwent pretreatment pelvic magnetic resonance imaging. A retrospective training cohort (n = 100) was accrued from January 2018 to December 2022, and a prospective temporal validation cohort (n = 100) was accrued from January 2023 to December 2024. Reference standards comprised surgical histopathology in 154 patients and composite clinical-imaging adjudication in 46 patients. Primary tumors were manually segmented on axial T2-weighted imaging and registered to T1-weighted imaging, diffusion-weighted imaging/apparent diffusion coefficient maps, and dynamic contrast-enhanced imaging. An Image Biomarker Standardisation Initiative-compliant radiomics pipeline was used, including resampling, intensity discretization, reproducibility filtering, correlation pruning, and nested feature selection. Clinical, radiomics, clinical-radiomics, and deep fusion models were evaluated in the prospective cohort using discrimination, calibration, decision-curve, and reclassification analyses.
Results: The deep fusion model achieved the best validation discrimination, with an area under the receiver operating characteristic curve of 0.910 (95% confidence interval, 0.845–0.959) and an area under the precision-recall curve of 0.849. At the training-derived threshold, accuracy was 0.84, sensitivity was 0.79, specificity was 0.86, positive predictive value was 0.75, negative predictive value was 0.89, and Brier score was 0.125. Deep fusion outperformed the clinical model (area under the curve, 0.730; mean delta area under the curve, 0.180; P < 0.001) and improved reclassification relative to the clinical model.
Conclusion: Multi-sequence magnetic resonance imaging radiomics-clinical fusion improved pretreatment risk stratification for pelvic lymph node metastasis in cervical cancer. External multicenter validation is warranted before clinical implementation. These preliminary single-center findings should be interpreted cautiously and require multicenter external validation before broad clinical implementation.
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BibTeX
@article{gong2026multisequence,
title = {{Multi-sequence magnetic resonance imaging radiomics integrated with clinical features predicts pelvic lymph node metastasis in cervical cancer: a retrospective-prospective cohort study}},
author = {Wanqing Gong and Xiaoyi Liu and Mengli Wang and Xiaowen Hu and Guangping Yan and Kun Liu and Yongxin Li and Tingting Zhang},
journal = {Frontiers in Medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1910248},
url = {https://doi.org/10.3389/fmed.2026.1910248},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Multi-sequence magnetic resonance imaging radiomics integrated with clinical features predicts pelvic lymph node metastasis in cervical cancer: a retrospective-prospective cohort study
AU - Wanqing Gong
AU - Xiaoyi Liu
AU - Mengli Wang
AU - Xiaowen Hu
AU - Guangping Yan
AU - Kun Liu
AU - Yongxin Li
AU - Tingting Zhang
PY - 2026
DA - 2026/10/12
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1910248
UR - https://doi.org/10.3389/fmed.2026.1910248
AB - Background: Pelvic lymph node metastasis is a major determinant of staging, recurrence risk, and treatment selection in cervical cancer, yet conventional magnetic resonance imaging criteria based on nodal size and morphology remain insufficient for occult nodal disease. We developed and temporally validated a multi-sequence magnetic resonance imaging radiomics-clinical fusion framework for pretreatment prediction of pelvic lymph node metastasis. Methods: This single-center retrospective-prospective cohort study included 200 women with histologically confirmed cervical cancer who underwent pretreatment pelvic magnetic resonance imaging. A retrospective training cohort (n = 100) was accrued from January 2018 to December 2022, and a prospective temporal validation cohort (n = 100) was accrued from January 2023 to December 2024. Reference standards comprised surgical histopathology in 154 patients and composite clinical-imaging adjudication in 46 patients. Primary tumors were manually segmented on axial T2-weighted imaging and registered to T1-weighted imaging, diffusion-weighted imaging/apparent diffusion coefficient maps, and dynamic contrast-enhanced imaging. An Image Biomarker Standardisation Initiative-compliant radiomics pipeline was used, including resampling, intensity discretization, reproducibility filtering, correlation pruning, and nested feature selection. Clinical, radiomics, clinical-radiomics, and deep fusion models were evaluated in the prospective cohort using discrimination, calibration, decision-curve, and reclassification analyses. Results: The deep fusion model achieved the best validation discrimination, with an area under the receiver operating characteristic curve of 0.910 (95% confidence interval, 0.845–0.959) and an area under the precision-recall curve of 0.849. At the training-derived threshold, accuracy was 0.84, sensitivity was 0.79, specificity was 0.86, positive predictive value was 0.75, negative predictive value was 0.89, and Brier score was 0.125. Deep fusion outperformed the clinical model (area under the curve, 0.730; mean delta area under the curve, 0.180; P < 0.001) and improved reclassification relative to the clinical model. Conclusion: Multi-sequence magnetic resonance imaging radiomics-clinical fusion improved pretreatment risk stratification for pelvic lymph node metastasis in cervical cancer. External multicenter validation is warranted before clinical implementation. These preliminary single-center findings should be interpreted cautiously and require multicenter external validation before broad clinical implementation.
SN - 2296-858X
ER -