利用 ComBat 方法协调多站点数据,通过 DAT-SPECT 提高帕金森病诊断水平

Noritaka Wakasugi, Harumasa Takano, Mitsunari Abe, N. Sawamoto, Toshiya Murai, Toshiki Mizuno, Teruyuki Matsuoka, Ryo Yamakuni, Hirooki Yabe, Hiroshi Matsuda, Takashi Hanakawa
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引用次数: 0

摘要

多巴胺转运体单光子发射计算机断层扫描(DAT-SPECT)是评估帕金森病(PD)患者的重要工具。然而,在大型多点临床试验中,它的作用受到了试验点间变异性的限制。为了克服这一局限性,传统的前瞻性校正方法采用了线性回归和模型扫描,这种方法虽然有效,但只能以前瞻性的方式使用。我们分析了在四个临床试验机构登记的 72 名健康老年人和 81 名帕金森病患者的 DAT-SPECT 特异性结合率(SBR)。我们对原始 SBR 进行了前瞻性校正和回顾性 ComBat 校正。接下来,我们比较了原始 SBR 和两种校正 SBR 在区分帕金森病患者和健康对照组方面的性能。对照组和帕金森病组的原始 SBR 分别为 6.13 ± 1.54(平均值 ± 标准差)和 2.03 ± 1.41。经过前瞻性校正后,对照组和腹膜透析组的平均 SBR 分别为 6.52 ± 1.06 和 2.40 ± 0.99。经过 ComBat 追溯校正后,对照组和帕金森病组的 SBR 分别为 5.25 ± 0.89 和 2.01 ± 0.73,平均值发生了很大变化,但方差较小。原始 SBR 在区分帕金森病和对照组方面表现尚可(Hedges's g = 2.76;AUC-ROC = 0.936)。两种校正方法都提高了区分性能。ComBat校正后的SBR与前瞻性校正后的SBR(g = 4.32,AUC-ROC = 0.992)具有相当的区分性能(g = 3.99,AUC-ROC = 0.987)。我们的研究结果支持在多地点研究中采用与ComBat统一的方法来巩固基于SBR的PD诊断或分层。尽管如此,鉴于ComBat校正后的SBR平均值变化很大,在对其进行解释时仍需谨慎。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Harmonizing multisite data with the ComBat method for enhanced Parkinson’s disease diagnosis via DAT-SPECT
Dopamine transporter single-photon emission computed tomography (DAT-SPECT) is a crucial tool for evaluating patients with Parkinson’s disease (PD). However, its implication is limited by inter-site variability in large multisite clinical trials. To overcome the limitation, a conventional prospective correction method employs linear regression with phantom scanning, which is effective yet available only in a prospective manner. An alternative, although relatively underexplored, involves retrospective modeling using a statistical method known as “combatting batch effects when combining batches of gene expression microarray data” (ComBat).We analyzed DAT-SPECT-specific binding ratios (SBRs) derived from 72 healthy older adults and 81 patients with PD registered in four clinical sites. We applied both the prospective correction and the retrospective ComBat correction to the original SBRs. Next, we compared the performance of the original and two corrected SBRs to differentiate the PD patients from the healthy controls. Diagnostic accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC).The original SBRs were 6.13 ± 1.54 (mean ± standard deviation) and 2.03 ± 1.41 in the control and PD groups, respectively. After the prospective correction, the mean SBRs were 6.52 ± 1.06 and 2.40 ± 0.99 in the control and PD groups, respectively. After the retrospective ComBat correction, the SBRs were 5.25 ± 0.89 and 2.01 ± 0.73 in the control and PD groups, respectively, resulting in substantial changes in mean values with fewer variances. The original SBRs demonstrated fair performance in differentiating PD from controls (Hedges’s g = 2.76; AUC-ROC = 0.936). Both correction methods improved discrimination performance. The ComBat-corrected SBR demonstrated comparable performance (g = 3.99 and AUC-ROC = 0.987) to the prospectively corrected SBR (g = 4.32 and AUC-ROC = 0.992) for discrimination.Although we confirmed that SBRs fairly discriminated PD from healthy older adults without any correction, the correction methods improved their discrimination performance in a multisite setting. Our results support the utility of harmonization methods with ComBat for consolidating SBR-based diagnosis or stratification of PD in multisite studies. Nonetheless, given the substantial changes in the mean values of ComBat-corrected SBRs, caution is advised when interpreting them.
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