ArticleAbdominal radiology (New York)2026
Preoperative prediction of parametrial invasion in early-stage cervical cancer: a radiomics nomogram fusing multi-parametric MRI and clinical biomarkers.
Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeThis study aimed to develop and validate a radiomics nomogram that integrates multi-parametric MRI and clinical factors for the preoperative prediction of parametrial invasion (PMI) in early-stage cervical cancer (ECC). MATERIALS AND
methodsA total of 363 patients with ECC (FIGO stages IB-IIA) were divided into training, internal validation, and external validation cohorts. All patients underwent T2WI, DWI, and T1c scans before radical hysterectomy. Radiomics features were extracted from T2WI, DWI, and T1c images, and selected using the max-relevance and min-redundancy (mRMR) method and the least absolute shrinkage and selection operator (LASSO). Radiomics signatures were then derived from these selected features. An MRI model was built using the radiomics signatures to evaluate their performance in distinguishing patients with PMI. A radiomics nomogram was constructed based on the optimal radiomics signature, pre-procedure hematocrit levels, and CA-125 levels. The discrimination performance of the nomogram was subsequently evaluated.
resultsFor the MRI model, the radiomics signatures yielded AUCs of 0.834 (95% CI: 0.7275-0.9399) and 0.800 (95% CI: 0.6902-0.9105) in the internal and external validation cohorts, respectively. The radiomics nomogram, which integrated the radiomics signatures from T2WI, DWI, and T1c, along with hematocrit and CA-125 levels, showed excellent discrimination between PMI and non-PMI groups. The nomogram achieved an AUC of 0.827 (95% CI: 0.7116-0.9430) in the internal validation cohort and 0.806 (95% CI: 0.6997-0.9114) in the external validation cohort. The specificity and sensitivity were 0.866 and 0.762, respectively, in the internal validation cohort, and 0.875 and 0.583 in the external validation cohort.
conclusionsThe developed radiomics nomogram provides a non-invasive and reliable tool for preoperative PMI prediction in ECC. By facilitating more accurate risk stratification, it has the potential to inform personalized therapeutic planning.
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