提出量化影像组学模型对扫描参数敏感性的统计框架，提升跨中心稳定性。
A Statistical Multi-Objective Framework for Assessing Sensitivity of Radiomic AI Models to Acquisition Parameters
- 基于混合效应模型与分层帕累托策略，分析扫描参数影响
- 低性能条件下准确率0.72，高性能下达0.88，显著提升鲁棒性
- 适合医学AI研发者和临床部署团队参考，尤其关注多中心数据
AI影像组学系统在临床应用的主要障碍是不同中心扫描协议导致的性能下降。本文提出一种面向性能的框架，量化影像组学AI模型对扫描参数的敏感性，并识别与跨数据集鲁棒性提升相关的关键参数区间。采用混合效应模型结合分层帕累托策略，评估参数影响并筛选导致模型性能下降的关键配置。我们在两个独立多中心数据集（公开数据集与自收集私有数据）上，针对肺癌CT诊断应用了多种先进架构。为验证结果可迁移性，使用私有数据选择参数并在公开数据集上验证。在选定配置下，低性能条件下的准确率为0.72（95% CI [0.59, 0.86]），F1得分0.71（[0.58, 0.84]）；高性能条件下，准确率达0.88（[0.7, 1.0]），F1得分为0.87（[0.69, 1.0]）。
原文摘要 · Abstract (English)
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. Mixed-effects modelling in combination with a hierarchical Pareto-based strategy is used to quantify the influence of acquisition parameters and select critical values associated to low performance of predictive models. We apply our framework to lung cancer diagnosis in CT scans using two independent multicentre datasets (a public dataset and own-collected private data) and several state-of-the-art architectures. To evaluate transferability of results, CT parameters were selected using the private data and validated on the public set. With the selected configurations, validation in low performance conditions has 0.72 ([0.59,0.86], 95% CI) accuracy and 0.71 ([0.58, 0. 84], 95% CI) F1Score, while in high performance ones, we obtain 0.88 ([0.7, 1.0], 95% CI) accuracy with 0.87 ([0.69, 1.0], 95% CI) F1Score.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。