Predicting Patient Survival with Airway Biomarkers using nn-Unet/Radiomics

TL;DR

This paper presents a three-stage AI approach combining nn-Unet segmentation, radiomic feature extraction, and SVM classification to predict lung fibrosis patient survival based on airway biomarkers.

Contribution

It introduces a novel pipeline integrating segmentation and radiomics for survival prediction in lung fibrosis, achieving high segmentation and classification scores.

Findings

Segmentation score of 0.8601 achieved

Classification score of 0.7346 achieved

Effective integration of airway biomarkers for survival prediction

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.