Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost
Journal:
arXiv
Published Date:
Jan 23, 2025
Abstract
Purpose: To automate contrast phase classification in CT using organ-specific
features extracted from a widely used segmentation tool with a lightweight
decision tree classifier.
Materials and Methods: This retrospective study utilized three public CT
datasets from separate institutions. The phase prediction model was trained on
the WAW-TACE (median age: 66 [60,73]; 185 males) dataset, and externally
validated on the VinDr-Multiphase (146 males; 63 females; 56 unk) and C4KC-KiTS
(median age: 61 [50.68; 123 males) datasets. Contrast phase classification was
performed using organ-specific features extracted by TotalSegmentator, followed
by prediction using a gradient-boosted decision tree classifier.
Results: On the VinDr-Multiphase dataset, the phase prediction model achieved
the highest or comparable AUCs across all phases (>0.937), with superior
F1-scores in the non-contrast (0.994), arterial (0.937), and delayed (0.718)
phases. Statistical testing indicated significant performance differences only
in the arterial and delayed phases (p<0.05). On the C4KC-KiTS dataset, the
phase prediction model achieved the highest AUCs across all phases (>0.991),
with superior F1-scores in arterial/venous (0.968) and delayed (0.935) phases.
Statistical testing confirmed significant improvements over all baseline models
in these two phases (p<0.05). Performance in the non-contrast class, however,
was comparable across all models, with no statistically significant differences
observed (p>0.05).
Conclusion: The lightweight model demonstrated strong performance relative to
all baseline models, and exhibited robust generalizability across datasets from
different institutions.
