Artificial intelligence and MRI in sinonasal tumors discrimination: where do we stand?

BACKGROUND

Artificial intelligence (AI) demonstrates high potential when applied to radiomic analysis of magnetic resonance imaging (MRI) to discriminate sinonasal tumors. This can enhance diagnostic suspicion beyond visual assessment alone and prior to biopsy, leading to expedite the diagnostic timeline and the treatment planning. The aim of the present work is to evaluate the current advancements and accuracy of this technology in this domain.

METHODS

A systematic literature review was conducted following PRISMA guidelines. Inclusion criteria comprised studies utilizing any machine learning approach applied to MRI of patients with sinonasal tumors. For each study, comprehensive data were gathered on the MRI protocols, feature extraction techniques, and classifiers employed to develop the AI model. The performance was assessed based on accuracy and area under the curve (AUC).

RESULTS

Fourteen studies, published between May 2017 and August 2024, were included. These studies were categorized into three groups: those examining both benign and malignant tumors, those investigating malignant tumor subpopulations, and those focusing on benign pathologies. All studies reported an AUC greater than 0.800, achieving AUC > 0.89 and accuracy > 0.81 when incorporating clinical-radiological variables. Notably, the best discrimination performance was observed in studies utilizing combined conventional MRI sequences, including T1-weighted, contrasted T1-weighted, and T2-weighted images.

CONCLUSION

The application of AI and radiomics in analyzing MRI scans presents significant promise for improving the discrimination of sinonasal tumors. Integrating clinical and radiological indicators enhances model performance, suggesting that future research should focus on larger patient cohorts and diverse AI methodologies to refine diagnostic accuracy and clinical utility.