D'hulst Ludovic, Van Weehaeghe Donatienne, Chiò Adriano, Calvo Andrea, Moglia Cristina, Canosa Antonio, Cistaro Angelina, Willekens Stefanie Ma, De Vocht Joke, Van Damme Philip, Pagani Marco, Van Laere Koen
a Division of Nuclear Medicine and Department of Imaging and pathology , University Hospitals Leuven and KU Leuven , Leuven , Belgium.
b ALS Center, 'Rita Levi Montalcini' Department of Neuroscience , University of Torino , Torino , Italy.
Amyotroph Lateral Scler Frontotemporal Degener. 2018 Nov;19(7-8):570-577. doi: 10.1080/21678421.2018.1476548. Epub 2018 Jun 4.
F-Fluorodeoxyglucose (F-FDG) positron emission tomography (PET) single-center studies using support vector machine (SVM) approach to differentiate amyotrophic lateral sclerosis (ALS) from controls have shown high overall accuracy on an individual patient basis using local a priori defined classifiers. The aim of the study was to validate the SVM accuracy on a multicentric level.
A previously defined Belgian (BE) group of 175 ALS patients (61.9 ± 12.2 years, 120M/55F) and 20 screened healthy controls (62.4 ± 6.4 years, 12M/8F) was used to classify another large dataset from Italy (IT), consisting of 195 patients (63.2 ± 11.6 years, 117M/78F) and 40 controls (62 ± 14.4 years; 29M/11F) free of any neurological and psychiatric disorder who underwent whole-body F-FDG PET-CT for lung cancer without any evidence of paraneoplastic symptoms. F-FDG within-center group comparisons based on statistical parametric mapping (SPM) were performed and SVM classifiers based on the local training sets were applied to differentiate ALS from controls from the other centers.
SPM group analysis showed only minor differences between both ALS groups, indicating pattern consistency. SVM using BE data set as training, classified 183/193 ALS-IT correctly (accuracy of 94.8%). However, 35/40 CON-IT were misclassified as ALS (accuracy 12.5%). Furthermore, using IT data as training, ALS-BE could not be distinguished from CON-BE. Within-center SPM group analysis confirmed prefrontal hypometabolism in CON-IT versus CON-BE, indicating subclinical brain changes in patients undergoing oncological scanning.
This multicenter study confirms that the F-FDG ALS pattern is stable across centers. Furthermore, it highlights the importance of carefully selected controls, as subclinical frontal changes might be present in patients in an oncological setting.
采用支持向量机（SVM）方法区分肌萎缩侧索硬化症（ALS）与对照的F-氟脱氧葡萄糖（F-FDG）正电子发射断层扫描（PET）单中心研究表明，使用局部先验定义的分类器在个体患者层面具有较高的总体准确率。本研究的目的是在多中心层面验证SVM的准确性。
先前定义的比利时（BE）组包括175例ALS患者（61.9± 12.2岁，120名男性/55名女性）和20名经过筛选的健康对照（62.4± 6.4岁，12名男性/8名女性），用于对来自意大利（IT）的另一个大型数据集进行分类，该数据集包括195例患者（63.2± 11.6岁，117名男性/78名女性）和40名对照（62± 14.4岁；29名男性/11名女性），这些对照无任何神经和精神疾病，因肺癌接受了全身F-FDG PET-CT检查，且无任何副肿瘤综合征症状的证据。基于统计参数映射（SPM）进行中心内F-FDG组间比较，并应用基于局部训练集的SVM分类器区分来自其他中心的ALS与对照。
SPM组分析显示两个ALS组之间仅有微小差异，表明模式一致性。以BE数据集作为训练集的SVM正确分类了183/193例ALS-IT患者（准确率为94.8%）。然而，35/40例CON-IT被误分类为ALS（准确率12.5%）。此外，以IT数据作为训练集时，无法区分ALS-BE与CON-BE。中心内SPM组分析证实CON-IT与CON-BE相比存在前额叶代谢减低，表明接受肿瘤扫描的患者存在亚临床脑改变。
这项多中心研究证实F-FDG ALS模式在各中心之间是稳定的。此外，它强调了精心选择对照的重要性，因为肿瘤患者可能存在亚临床额叶改变。
