Preoperative prediction of cervical cancer recurrence using explainable MRI radiomics: a SHAP-Guided machine learning study.

Journal: Abdominal radiology (New York)
Published Date: Nov 10, 2025

Abstract

PURPOSE: To develop and validate a machine learning model that integrated MRI radiomics features and clinical factors for preoperative prediction of postoperative recurrence in cervical cancer. METHODS: This retrospective study included 268 patients with pathologically confirmed cervical cancer (training cohort: n = 185; validation cohort: n = 83). A total of 124 radiomics features were extracted from T2-weighted images, with 12 optimal features were selected through reproducibility analysis (ICC > 0.75), minimum redundancy maximum relevance (mRMR), and least absolute shrinkage and selection operator (LASSO) regression. Four machine learning (ML) models (logistic regression [LR], naïve Bayes [NB], gradient boosting machine [GBM], random forest [RF]) were trained and evaluated for their discrimination performance (area under the curve [AUC], sensitivity, specificity), calibration accuracy, and clinical utility (via decision curve analysis [DCA]). SHapley Additive exPlanations (SHAP) were employed to elucidate the importance of each feature. RESULTS: The LR model demonstrated the highest performance in the validation cohort (AUC = 0.818, sensitivity = 0.629, specificity = 0.806), which significantly outperformed clinical factors alone (AUC = 0.681, P = 0.005). SHAP analysis identified tumor heterogeneity features such as original_firstorder_Minimum and GLCM-correlation, as the top predictors. The combined radiomics-clinical model further improved AUC to 0.844. DCA confirmed a net benefit across clinically relevant risk thresholds. CONCLUSION: An interpretable ML radiomics model that leveraged preoperative MRI and clinical data showed potential to stratify the risk of recurrence in cervical cancer, thereby offering potential to guide personalized decisions regarding surgical and adjuvant therapies.

Authors

  • Weixia Lin
    Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
  • Xiaoyu Lan
    Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
  • Weixi Huang
    Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
  • Shaoliang Tang
    School of Medical Imaging, Fujian Medical University, Fuzhou, China.
  • Sang Li
    Key Laboratory for NeuroInformation of Ministry of Education, University of Electronic Science and Technology of China, Chengdu, China; Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China; Department of Biomedical Engineering, University of Electronic Science and Technology of China, Chengdu, China.
  • Wang Ren
    Department of Radiology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
  • Xiang Zheng
    Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Tuborg Havnevej 19 2900 Hellerup, Denmark.

Keywords

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