Curiale Ariel Hernán, San José Estépar Raúl
Applied Chest Imaging Laboratory, Department of Radiology and Medicine, Brigham and Women's Hospital, 399 Revolution Drive, Somerville, 02145, MA, USA; Harvard Medical School, 25 Shattuck Street, Boston, 02115 MA, USA.
Comput Biol Med. 2025 Feb;185:109500. doi: 10.1016/j.compbiomed.2024.109500. Epub 2024 Dec 6.
Emphysema, marked by irreversible lung tissue destruction, poses challenges in progression prediction due to its heterogeneity. Early detection is particularly critical for patients with Alpha-1 Antitrypsin Deficiency (AATD), a genetic disorder reducing ATT protein levels. Heterozygous carriers (PiMS and PiMZ) have variable AAT levels thus complicating their prognosis. This study introduces a novel prognostic model, the Lobe-based Transformer encoder (LobTe), designed to predict the annual change in lung density (ΔALD [g/L-yr]) using CT scans. Utilizing a global self-attention mechanism, LobTe specifically analyzes lobar tissue destruction to forecast disease progression. In parallel, we developed and compared a second model utilizing an LSTM architecture that implements a local subject-specific attention mechanism. Our methodology was validated on a cohort of 2,019 participants from the COPDGene study. The LobTe model demonstrated a small root mean squared error (RMSE=1.73 g/L-yr) and a notable correlation coefficient (ρ=0.61), explaining over 35% of the variability in ΔALD (R= 0.36). Notably, it achieved a higher correlation coefficient of 0.68 for PiMZ heterozygous carriers, indicating its effectiveness in detecting early emphysema progression among smokers with mild to moderate AAT deficiency. The presented models could serve as a tool for monitoring disease progression and informing treatment strategies in carriers and subjects with AATD. Our code is available at github.com/acil-bwh/LobTe.
肺气肿以不可逆的肺组织破坏为特征，因其异质性给病情进展预测带来挑战。早期检测对于患有α-1抗胰蛋白酶缺乏症（AATD）的患者尤为关键，这是一种会降低ATT蛋白水平的遗传性疾病。杂合子携带者（PiMS和PiMZ）的AAT水平各不相同，因此其预后情况较为复杂。本研究引入了一种新型预后模型，即基于肺叶的Transformer编码器（LobTe），旨在利用CT扫描预测肺密度的年度变化（ΔALD [g/L-yr]）。LobTe利用全局自注意力机制，专门分析肺叶组织破坏情况以预测疾病进展。同时，我们开发并比较了另一种利用LSTM架构的模型，该模型采用局部受试者特异性注意力机制。我们的方法在来自慢性阻塞性肺疾病基因（COPDGene）研究的2019名参与者队列中得到了验证。LobTe模型显示出较小的均方根误差（RMSE = 1.73 g/L-yr）和显著的相关系数（ρ = 0.61），解释了ΔALD中超过35%的变异性（R = 0.36）。值得注意的是，对于PiMZ杂合子携带者，它实现了更高的相关系数0.68，表明其在检测轻度至中度AAT缺乏吸烟者的早期肺气肿进展方面具有有效性。所提出的模型可作为监测疾病进展以及为AATD携带者和受试者的治疗策略提供参考的工具。我们的代码可在github.com/acil-bwh/LobTe获取。
