Mixed-effects framework quantifies radiomic AI sensitivity to CT parameters and identifies optimal settings (>=200 mA tube current, <=1.5 pitch, <=1.25 mm slice thickness) for better cross-dataset performance in lung cancer diagnosis.
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How Sensitive Are Radiomic AI Models to Acquisition Parameters?
Mixed-effects framework quantifies radiomic AI sensitivity to CT parameters and identifies optimal settings (>=200 mA tube current, <=1.5 pitch, <=1.25 mm slice thickness) for better cross-dataset performance in lung cancer diagnosis.