Background: Hyperostosis is a common radiographic feature of inverted papilloma (IP) tumor origin on computed tomography (CT). Herein, we developed a machine learning (ML) model capable of analyzing CT images and identifying IP attachment sites.
Methods: A retrospective review of patients treated for IP at our institution was performed. The tumor attachment site was manually segmented on CT scans by the operating surgeon. We used a nnU-Net model, a state-of-the-art deep learning-based segmentation algorithm that automatically configures image preprocessing, network architecture, training, and post-processing to identify the IP attachment site. The model was trained and evaluated using a 5-fold cross validation, where each iteration split the data into train/validation/test to avoid chances of overfitting. The attachment site was classified as either 'identified or 'not identified' using the nnU-Net model output and the Sørensen-Dice coefficient (Dice) was used to further evaluate the segmentation performance of each subject.
Results: A total of 58 subjects met enrollment criteria. The algorithm identified the attachment site in 55.2 % (n = 32) of patients with an average dice score (+/-SD) of 0.34 (+/- 0.24). In the univariate analysis, the algorithm performed better for attachment sites within the maxillary sinus (OR 4.0; p < 0.05) and performed worse during revision surgery (OR 0.13; p < 0.05). Multivariate logistic regression analysis confirmed these associations for maxillary attachment site (OR 4.6; p < 0.05) and revision surgery (OR 0.11; p < 0.05).
Conclusion: A state-of-the-art ML model successfully identified the attachment site of IP with a high degree of fidelity in select cases, but requires larger sample sizes and more diverse datasets to become reliably integrated into clinical practice.
Keywords: Artificial intelligence; Inverted papilloma; Machine learning; Radiomics; Tumor.
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