American journal of otolaryngology · 2024
Predicting sinonasal inverted papilloma attachment using machine learning: Current lessons and future directions.
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McKee Sean P, Liang Xiaomin, Yao William C, Anderson Brady, Ahmad Jumah G, Allen David Z, Hasan Salman, Chua Andy J, Mokashi Chinmay, Islam Samia, Luong Amber U, Citardi Martin J, Giancardo Luca
Аннотация
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.
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.