Deep Learning Radiomics Based on MRI for Differentiating Benign and Malignant Parapharyngeal Space Tumors.

Abstract

Objective: The study aims to establish a pre-academic diagnostic tool based on deep learning and conventional radiomics features to guide the clinical decision-making of parapharyngeal space (PPS) tumors.

Methods: This retrospective study included 217 patients with PPS tumors, from two medical centers in China from March 1, 2011, to October 1, 2023. The study cohort was divided into a training set (n = 145) and a test set (n = 72). A deep learning (DL) model and conventional radiomics (Rad) model based on neck MRI were constructed to distinguish malignant tumors (MTs) and benign tumors (BTs) of PPS tumors. The deep learning radiomics (DLR) model which integrates deep learning and radiomics features was further developed. The area under the receiver operating characteristic curve (AUC), specificity, and sensitivity were used to evaluate model performance. Decision curve analysis (DCA) was applied to assess the clinical utility.

Results: Compared with the Rad and DL models, the DLR model showed excellent performance in this study, with the highest AUC of 0.899 and 0.821 in the training set and test set, respectively. The DCA curve confirmed the clinical utility of the DLR model in distinguishing the pathological types of PPS tumors.

Conclusion: The DLR model demonstrated a high predictive ability in diagnosing MTs and BTs of PPS and could serve as a powerful tool to aid clinical decision-making in the preoperative diagnosis of PPS tumors.

Level of evidence: III Laryngoscope, 2025.