Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost

• URL: http://arxiv.org/abs/2501.14066v2
• Date: Thu, 01 May 2025 01:53:31 GMT
• Title: Segment-and-Classify: ROI-Guided Generalizable Contrast Phase Classification in CT Using XGBoost
• Authors: Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Xinya Wang, Ronald M. Summers, Zhiyong Lu,
• Abstract summary: This study utilized three public CT datasets from separate institutions.<n>The phase prediction model was trained on the WAW-TACE dataset and validated on the VinDr-Multiphase and C4KC-KiTS datasets.
• Score: 7.689389068258514
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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