ArticleBMC medical imaging2025
Machine learning-based clinical-radiomics model for predicting recurrence risk after radical surgery in sinonasal squamous cell carcinoma: a preliminary 2-year follow-up study.
Article in BMC medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Artificial Intelligence in Venous Thromboembolism Prevention: A Narrative Review of Machine Learning, Deep Learning, and Natural Language Processing.Journal of cardiovascular development and disease · 2026Review
- Interpretable clinical-radiomics fusion model for noninvasive prediction of INI1 deficiency and prognosis in sinonasal undifferentiated carcinoma (SNUC): a two-center study.BMC medical imaging · 2026Article
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6 authors.
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Abstract
backgroundTo construct and validate an optimal machine learning (ML)-based clinical-radiomics model integrating clinical and radiomics features for predicting recurrence risk within 2 years after radical surgery in patients with sinonasal squamous cell carcinoma (SNSCC).
methodsThis retrospective study included a total of 207 patients with pathologically confirmed SNSCC who underwent preoperative MRI. Patients were divided into a training cohort (n = 151) from the headquarter hospital and an independent testing cohort (n = 56) from the branch hospital. Radiomics features were acquired from diffusion-weighted imaging (DWI) sequences. Clinical, radiomics, and combined clinical-radiomics models were constructed and validated using various ML algorithms, with the areas under the receiver operating characteristic curves (AUCs) computed to evaluate their classification performance. The optimal clinical-radiomics model that integrates multimodal features was subsequently identified.
resultsDuring the two-year period after radical surgery, 93 patients (44.9%) experienced recurrence at follow-up. The clinical-radiomics model, developed with seven predictive features (Range, surgical margin status, Kurtosis, T4 stage, Idn, Robust Mean Absolute Deviation, and Ki-67 levels) using ANOVA feature selection and LDA classifier, achieved optimal discriminative performance. In the training cohort, the model achieved an AUC of 0.85, in contrast to 0.76 for the clinical model and 0.79 for the radiomics model. This superiority was maintained in the testing cohort, where it achieved an AUC of 0.83, compared to 0.71 for the clinical model and 0.77 for the radiomics model. The combined model consistently outperformed the standalone models across both cohorts.
conclusionsThe present study developed an ML-based clinical-radiomics model that exhibited promising performance for predicting recurrence risk after radical surgery in SNSCC. Given its preliminary nature, this model has the potential to serve as an intelligent decision support tool, advancing precision medicine in SNSCC management.
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