Background and purpose: The purpose of this study was to compare the diagnostic performance between apparent diffusion coefficient (ADC) analysis of one-point measurement and whole-tumor measurement, including radiomics for differentiating pleomorphic adenoma (PA) from carcinoma ex pleomorphic adenoma (CXPA), and to evaluate the impact of inter-operator segmentation variability. Materials and methods: One hundred and fifteen patients with PA and 22 with CXPA were included. Four radiologists with different experience independently placed one-point and whole-tumor ROIs and a radiomics-predictive model was constructed from the extracted imaging features. We calculated the area under the receiver-operator characteristic curve (AUC) for the diagnostic performance of imaging features and the radiomics-predictive model. Results: AUCs of the imaging features from whole-tumor varied between readers (0.50–0.89). The most experienced radiologist (Reader 1) produced significantly high AUCs than less experienced radiologists (Reader 3 and 4; P = 0.01 and 0.009). AUCs were higher for the radiomics-predictive model (0.82–0.87) than for one-point (0.66–0.79) in all readers. Conclusion: Some imaging features of whole-tumor and radiomics-predictive model had higher diagnostic performance than one-point. The diagnostic performance of imaging features from whole-tumor alone varied depending on operator experience. Operator experience appears less likely to affect diagnostic performance in the radiomics-predictive model.
- Carcinoma ex pleomorphic adenoma
- Diagnostic performance
- Machine learning
- Pleomorphic adenoma
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging