November 28, 2025BMC Medical ImagingOpen Access

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

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Predictive model enhances the understanding of recurrence risk after surgery, improving patient management with intelligent tools.
The model demonstrated promising performance in identifying recurrence risk within a two-year follow-up period for SNSCC.
Assessment using a machine learning-based approach integrates clinical and radiomics data for improved decision-making.
Highlights the potential of advanced models in precision medicine for optimizing treatment choices in sinonasal carcinoma.

Implication

Observational analysis predicts recurrence risk after radical surgery in SNSCC, suggesting advancements in precision medicine.

Abstract

The 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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Cite This Study

Geng et al. (2025) studied this question.

synapsesocial.com/papers/6928f106a65b730b9ea79d34 https://doi.org/10.1186/s12880-025-02022-3

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Implication

Observational analysis predicts recurrence risk after radical surgery in SNSCC, suggesting advancements in precision medicine.

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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

Key Points

Implication

Abstract

Discussion

Cite This Study

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