LAUSR

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 automated anatomical labelling (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 and classifying ADHD subtypes (area under the 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.