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Purpose <p>This 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).</p> Materials and methods <p>A 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.</p> Results <p>For 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.</p> Conclusions <p>The 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.</p>

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Preoperative prediction of parametrial invasion in early-stage cervical cancer: a radiomics nomogram fusing multi-parametric MRI and clinical biomarkers

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Bin Wang,
  • Jianxin Xiao,
  • Yuanyuan Ding,
  • Jinjie Yu,
  • Lizhi Xie,
  • Aiguo Zhou,
  • Canyu Wang,
  • Xiaochun Wang,
  • Yongfang Wang

摘要

Purpose

This 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 methods

A 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.

Results

For 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.

Conclusions

The 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.