Keywords: Cancer, Cancer
Motivation: Enhance the accuracy of distinguishing misdiagnosed or ambiguous cases of pleomorphic adenoma (PA) and Warthin tumor (WT).
Goal(s): This study aims to construct various MRI-based radiomics models employing different machine learning classifiers to determine the optimal models for identifying misdiagnosed or ambiguous PA and WT cases.
Approach: we evaluate the effectiveness of various MRI-based radiomics models.
Results: A nomogram demonstrates exceptional and consistent diagnostic performance. In routine practice, combining clinical parameters is essential for distinguishing between PA and WT.
Impact: MRI-based radiomics models can effectively differentiate misdiagnosed or ambiguous cases of PA and WT. The nomogram is a valuable tool for preoperatively and non-invasively distinguishing between PA and WT, a task often challenging for clinicians and radiologists before surgery.
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