🤖 AI Summary
This study addresses the significant performance degradation of radiomics-based AI models caused by multicenter heterogeneity in CT acquisition protocols, which hinders their clinical translation. To tackle this issue, the authors introduce mixed-effects modeling into radiomics sensitivity analysis for the first time, establishing a performance-oriented analytical framework that simultaneously accounts for subject-specific random effects and fixed effects of CT acquisition parameters. This approach enables quantitative assessment of how key scanning parameters influence model robustness. Leveraging multicenter CT data, multiple deep learning architectures, and cross-dataset validation, the study identifies an optimal CT protocol—tube current ≥200 mA, pitch ≤1.5, and slice thickness ≤1.25 mm—that substantially improves diagnostic sensitivity from 0.79 to 0.90 and specificity from 0.47 to 0.79, thereby markedly enhancing cross-center generalizability.
📝 Abstract
A main barrier for the deployment of AI radiomic systems in clinical routine is their drop in performance under heterogeneous multicentre acquisition protocols. This work presents a performance-oriented framework for quantifying scan parameter sensitivity of radiomic AI models, while identifying clinically significant parameter regions associated with improved cross-dataset robustness. We formulate a mixed-effects framework for quantifying the influence that clinically relevant acquisition parameters have on models performance, while accounting for subject-level random effects. We have applied our framework to lung cancer diagnosis in CT scans using two independent multicentre datasets (a public database and own-collected data) and several SoA architectures. To evaluate across-database reproducibility, CT parameters have been adjusted using the data collected and tested on the public set. The optimal configuration selected is the current of the X-ray tube >= 200 mA, spiral pitch <= 1.5, slice thickness <= 1.25 mm, which balances diagnostic quality with low radiation dose. These configuration push metrics from 0.79+-0.04 sensitivity, 0.47+-0.10 specificity in low quality scans to 0.90+-0.10 sensitivity, 0.79 +- 0.13 specificity in high quality ones.