METHODS: Twenty-eight PSP patients, 46 PD patients and 60 control subjects (HC) were consecutively enrolled in the study. Serum concentration of neurofilament light chain protein (Nf-L) was assessed by single molecule array (SIMOA), while an automatic segmentation algorithm was employed for T1-weighted measurements of third ventricle width/intracranial diameter ratio (3rdV/ID). Machine learning (ML) models with Logistic Regression (LR), Random Forest (RF), and XGBoost algorithms based on 3rdV/ID and serum Nf-L levels were tested in distinguishing among PSP, PD and HC.
RESULTS: PSP patients showed higher serum Nf-L levels and larger 3rdV/ID ratio in comparison with both PD and HC groups (p < 0.005). All ML algorithms (LR, RF and XGBoost) showed that the combination of MRI and blood biomarkers had excellent classification performances in differentiating PSP from PD (AUC ≥0.92), outperforming each biomarker used alone (AUC: 0.85-0.90). Among the different algorithms, XGBoost was slightly more powerful than LR and RF in distinguishing PSP from PD patients, reaching AUC of 0.94 ± 0.04.
CONCLUSIONS: Our findings highlight the usefulness of combining blood and simple linear MRI biomarkers to accurately distinguish between PSP and PD patients. This multimodal approach may play a pivotal role in patient management and clinical decision-making, paving the way for more effective and timely interventions in these neurodegenerative diseases.
方法:28例PSP患者,46名PD患者和60名对照受试者(HC)连续纳入研究。通过单分子阵列(SIMOA)评估神经丝轻链蛋白(Nf-L)的血清浓度,而第三脑室宽度/颅内直径比(3rdV/ID)的T1加权测量采用自动分割算法。具有Logistic回归(LR)的机器学习(ML)模型,随机森林(RF),基于3rdV/ID和血清NF-L水平的XGBoost算法在区分PSP中进行了测试,PD和HC。
结果:与PD和HC组相比,PSP患者的血清Nf-L水平更高,3rdV/ID比率更高(p<0.005)。所有ML算法(LR,RF和XGBoost)表明,MRI和血液生物标志物的组合在区分PSP与PD(AUC≥0.92)方面具有出色的分类性能,优于单独使用的每种生物标志物(AUC:0.85-0.90)。在不同的算法中,在区分PSP和PD患者方面,XGBoost比LR和RF功能稍强。AUC达到0.94±0.04。
结论:我们的研究结果强调了将血液和简单的线性MRI生物标志物结合在一起以准确区分PSP和PD患者的有用性。这种多模式方法可能在患者管理和临床决策中发挥关键作用。为更有效和及时地干预这些神经退行性疾病铺平道路。