Surface values, volumetric measurements and radiomics of structural MRI for the diagnosis and subtyping of attention-deficit/hyperactivity disorder.
Surface values, volumetric measurements and radiomics of structural MRI for the diagnosis and subtyping of attention-deficit/hyperactivity disorder.
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DOI：
10.1111/ejn.15485
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发表时间：
2021-11
影响因子：
3.4
通讯作者：
Fan, Xiuqin
中科院分区：
文献类型：
--
作者：
Shi, Liting;Liu, Xuechun;Wu, Keqing;Sun, Kui;Lin, Chunsen;Li, Zhengmei;Zhao, Shuying;Fan, Xiuqin
关键词：
Attention-deficit/hyperactivity disorder (ADHD) is diagnosed subjectively based on an individual’s behaviour and performance. The clinical community has no objective biomarker to inform the diagnosis and subtyping of ADHD. This study aimed to explore the potential diagnostic biomarkers of ADHD among surface values, volumetric metrics, and radiomic features that were extracted from structural MRI images. Public data of New York University and Peking University were downloaded from the ADHD-200 consortium. MRI T1-weighted images were pre-processed using CAT12. We calculated surface values based on the Desikan-Killiany atlas. The volumetric metrics (mean grey matter volume and mean white matter volume) and radiomic features within each AAL brain area were calculated using DPABI and IBEX, respectively. The differences among three groups of participants were tested using ANOVA or Kruskal-Wallis test depending on the normality of the data. We selected discriminative features and classified typically developing controls (TDCs) and ADHD patients as well as two ADHD subtypes using least absolute shrinkage and selection operator and support vector machine algorithms. Our results showed that the radiomics-based model outperformed the others in discriminating ADHD from TDC as well as classifying ADHD subtypes (area under curve [AUC]: 0.78 and 0.94 in training test; 0.79 and 0.85 in testing set). Combining grey matter volumes, surface values, and clinical factors with radiomic features can improve the performance for classifying ADHD patients and TDCs with training and testing AUCs of 0.82 and 0.83, respectively. This study demonstrates that MRI T1-weighted features, especially radiomic features, are potential diagnostic biomarkers of ADHD. Structural MRI T1-weighted image-extracted features can distinguish patients with attention-deficit/hyperactivity disorder (ADHD) from typically developing controls as well as between the inattentive and combined subtypes. Radiomic features showed better performance than surface values, grey matter volume, white matter volume, and clinical factors. Combining other categories of features with radiomic features to build a hybrid model can improve the performance for the diagnosis of ADHD.
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影响因子：
16.2
作者：
Giedd, Jay N.;Rapoport, Judith L.
通讯作者：
Rapoport, Judith L.
DOI：
10.1073/pnas.1308091110
发表时间：
2013-10-15
影响因子：
11.1
作者：
Chen, Chi-Hua;Fiecas, Mark;Kremen, William S.
通讯作者：
Kremen, William S.
影响因子：
4.2
作者：
Madeira, Nuno;Duarte, Joao Valente;Castelo-Branco, Miguel
通讯作者：
Castelo-Branco, Miguel
DOI：
10.1097/chi.0b013e3181b395c0
发表时间：
2009-10
影响因子：
13.3
作者：
Narr KL;Woods RP;Lin J;Kim J;Phillips OR;Del'Homme M;Caplan R;Toga AW;McCracken JT;Levitt JG
通讯作者：
Levitt JG
影响因子：
3
作者：
HD-200 Consortium
通讯作者：
HD-200 Consortium