Korean J Radiol.  2021 May;22(5):751-758. 10.3348/kjr.2020.0576.

MRI Predictors of Malignant Transformation in Patients with Inverted Papilloma: A Decision Tree Analysis Using Conventional Imaging Features and Histogram Analysis of Apparent Diffusion Coefficients

Affiliations
  • 1Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
  • 2Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea.
  • 3Department of Radiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
  • 4Clinical Research Center, Asan Medical Center, Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Korea.

Abstract


Objective
Preoperative differentiation between inverted papilloma (IP) and its malignant transformation to squamous cell carcinoma (IP-SCC) is critical for patient management. We aimed to determine the diagnostic accuracy of conventional imaging features and histogram parameters obtained from whole tumor apparent diffusion coefficient (ADC) values to predict IP-SCC in patients with IP, using decision tree analysis.
Materials and Methods
In this retrospective study, we analyzed data generated from the records of 180 consecutive patients with histopathologically diagnosed IP or IP-SCC who underwent head and neck magnetic resonance imaging, including diffusion-weighted imaging and 62 patients were included in the study. To obtain whole tumor ADC values, the region of interest was placed to cover the entire volume of the tumor. Classification and regression tree analyses were performed to determine the most significant predictors of IP-SCC among multiple covariates. The final tree was selected by cross-validation pruning based on minimal error.
Results
Of 62 patients with IP, 21 (34%) had IP-SCC. The decision tree analysis revealed that the loss of convoluted cerebriform pattern and the 20th percentile cutoff of ADC were the most significant predictors of IP-SCC. With these decision trees, the sensitivity, specificity, accuracy, and C-statistics were 86% (18 out of 21; 95% confidence interval [CI], 65–95%), 100% (41 out of 41; 95% CI, 91–100%), 95% (59 out of 61; 95% CI, 87–98%), and 0.966 (95% CI, 0.912–1.000), respectively.
Conclusion
Decision tree analysis using conventional imaging features and histogram analysis of whole volume ADC could predict IP-SCC in patients with IP with high diagnostic accuracy.

Keyword

Inverted papilloma; Magnetic resonance imaging; Diffusion
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