自动量化肺部肿瘤负荷，助力肺癌动态监测
Longitudinal Assessment of Lung Lesion Burden in CT
- 用3D nnUNet模型无先验训练，提升病灶分割效果
- 对>1cm病灶达71.3%精确率、69.8%F1值
- 可精准追踪患者肿瘤负荷变化，适合临床随访
在美国，肺癌是第二大死亡原因。早期发现可疑肺结节对治疗规划和改善预后至关重要。尽管已有多种肺结节分割与体积分析方法，但针对总肺部肿瘤负荷的纵向变化研究仍较少。本文训练了两个3D nnUNet模型（带与不带解剖先验），实现肺部病灶自动分割并量化每位患者的总病灶负担。不含解剖先验的模型显著优于含先验模型（p < .001）。对临床显著性病灶（>1cm）检测达到71.3%精确率、68.4%敏感度、69.8%F1值。分割性能为Dice分数77.1±20.3，豪斯多夫距离误差11.7±24.1 mm。中位病灶负荷为6.4 cc（IQR: 2.1, 18.1），人工与自动测量中位差值为0.02 cc（IQR: -2.8, 1.2）。通过线性回归与Bland-Altman图评估一致性。该方法可提供个体化总肿瘤负荷评估，并支持随时间追踪变化。
原文摘要 · Abstract (English)
In the U.S., lung cancer is the second major cause of death. Early detection of suspicious lung nodules is crucial for patient treatment planning, management, and improving outcomes. Many approaches for lung nodule segmentation and volumetric analysis have been proposed, but few have looked at longitudinal changes in total lung tumor burden. In this work, we trained two 3D models (nnUNet) with and without anatomical priors to automatically segment lung lesions and quantified total lesion burden for each patient. The 3D model without priors significantly outperformed ($p < .001$) the model trained with anatomy priors. For detecting clinically significant lesions $>$ 1cm, a precision of 71.3\%, sensitivity of 68.4\%, and F1-score of 69.8\% was achieved. For segmentation, a Dice score of 77.1 $\pm$ 20.3 and Hausdorff distance error of 11.7 $\pm$ 24.1 mm was obtained. The median lesion burden was 6.4 cc (IQR: 2.1, 18.1) and the median volume difference between manual and automated measurements was 0.02 cc (IQR: -2.8, 1.2). Agreements were also evaluated with linear regression and Bland-Altman plots. The proposed approach can produce a personalized evaluation of the total tumor burden for a patient and facilitate interval change tracking over time.
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