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Predicting sinonasal inverted papilloma attachment using machine learning: Current lessons and future directions.

Sean P McKee1, Xiaomin Liang2, William C Yao3

  • 1Department of Otolaryngology, Massachusetts Eye & Ear Infimary, Boston, MA, USA.

American Journal of Otolaryngology
|December 31, 2024
PubMed
Summary
This summary is machine-generated.

A machine learning model was developed to identify inverted papilloma (IP) attachment sites on CT scans. While successful in some cases, the model requires more data for reliable clinical use.

Keywords:
Artificial intelligenceInverted papillomaMachine learningRadiomicsTumor

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Inverted papilloma (IP) is often associated with hyperostosis on CT scans.
  • Identifying IP tumor origin and attachment sites is crucial for surgical planning.
  • Computed tomography (CT) is a key imaging modality for evaluating IP.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) model for identifying IP attachment sites on CT images.
  • To assess the performance of a deep learning segmentation algorithm (nnU-Net) in this task.
  • To determine factors influencing the model's accuracy.

Main Methods:

  • Retrospective review of 58 patients with IP treated at the institution.
  • Manual segmentation of tumor attachment sites on CT scans by the operating surgeon.
  • Application of a nnU-Net model for automated identification and segmentation of IP attachment sites.
  • Evaluation using 5-fold cross-validation and Sørensen-Dice coefficient.

Main Results:

  • The ML algorithm identified the IP attachment site in 55.2% of patients.
  • Average Dice score was 0.34 (+/- 0.24), indicating moderate segmentation performance.
  • The model performed better for maxillary sinus attachment sites (OR 4.6) and worse in revision surgeries (OR 0.11).

Conclusions:

  • A state-of-the-art ML model demonstrated capability in identifying IP attachment sites.
  • The model showed high fidelity in select cases, particularly for maxillary sinus origins.
  • Larger and more diverse datasets are necessary for reliable clinical integration of this ML tool.