放射性"压力测试":在高风险的潜在肺结节队列中探索深度学习放射性模型
David Xiao1, Yency Forero2, Michael N Kammer2,3
1Department of General Surgery, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
BMJ open respiratory research
|June 27, 2025
概括
结合肺癌预测 (LCP) 评分和临床因素的综合模型改善了不确定性肺结节 (IPN) 恶性瘤风险预测. 这种方法可能有助于减少IPNs患者不必要的侵入性活检.
科学领域:
- 肺部病理学 肺部病理学
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
背景情况:
- 不确定的肺结节 (IPN) 经常经过活检,尽管许多是良性的.
- 肺癌预测 (LCP) 评分显示,它有望用于诊断偶然发现的IPN.
- 它在减少已被推用于侵入性手术的结节活检中的有用性尚未得到证实.
研究的目的:
- 评估梅奥诊所模型的诊断性能,LCP放射性模型,以及不确定性肺结节 (IPN) 的综合模型.
- 评估一个综合模型的潜力,以改善恶性瘤风险预测,减少不必要的侵入性活检.
主要方法:
- 对连续活检的IPN进行前性采集,后性盲目评估.
- 使用了梅奥诊所预测模型和LCP放射性模型.
- 通过逐步后勤回归开发了一种综合模型,将LCP与临床变量 (年龄,螺旋,上叶位置) 结合起来.
主要成果:
- 与梅奥诊所 (0.69) 和LCP (0.67) 模型相比,综合模型实现了最高的AUC (0.75).
- 综合模型显示了更好的分类 (F1评分0.645) 和显著的AUC改善 (p=0.046).
- 再分类分析显示,临床净再分类指数为0.36,正确降级良性结节,避免恶性结节的错误分类.
结论:
- 将LCP得分与精选的临床变量结合起来,可以提高不确定的肺结节 (IPN) 的恶性瘤风险预测.
- 这种改进的预测和分类可能会导致IPN不必要的侵入性活检减少.
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