Multi-sequence magnetic resonance imaging radiomics integrated with clinical features predicts pelvic lymph node metastasis in cervical cancer: a retrospective-prospective cohort study.

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Abstract

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. 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 (<i>n</i> = 100) was accrued from January 2018 to December 2022, and a prospective temporal validation cohort (<i>n</i> = 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. 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; <i>P</i> < 0.001) and improved reclassification relative to the clinical model. 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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