Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
The primary objective of the AIIB 2023 competition is to evaluate the
predictive significance of airway-related imaging biomarkers in determining the
survival outcomes of patients with lung fibrosis.This study introduces a
comprehensive three-stage approach. Initially, a segmentation network, namely
nn-Unet, is employed to delineate the airway's structural boundaries.
Subsequently, key features are extracted from the radiomic images centered
around the trachea and an enclosing bounding box around the airway. This step
is motivated by the potential presence of critical survival-related insights
within the tracheal region as well as pertinent information encoded in the
structure and dimensions of the airway. Lastly, radiomic features obtained from
the segmented areas are integrated into an SVM classifier. We could obtain an
overall-score of 0.8601 for the segmentation in Task 1 while 0.7346 for the
classification in Task 2.
