Ziwei Yang,1,2,* Xiao Liang,1,2,* Yuqi Ji,1,2 Wei Zeng,1,2 Yao Wang,1,2 Yong Zhang,3 Fuqing Zhou1,2 

1Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China; 2Neuroradiology Laboratory, Jiangxi Province Medical Imaging Research Institute, Nanchang, 330006, People’s Republic of China; 3Department of Pain Clinic, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, 330006, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Yong Zhang, Department of Pain Clinic, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, 17 Yongwaizheng Street, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86 791 8869 5036, Email zy830226@163.com Fuqing Zhou, Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, 17 Yongwaizheng Street, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86 791 8869 5132, Email ndyfy02301@ncu.edu.cn

Purpose: To investigate whether functional radiomic features in bilateral hippocampi can identify the cognitively impaired patients from low-back-related leg pain (LBLP).
Patients and Methods: For this retrospective study, a total of 95 clinically definite LBLP patients (40 cognitively impaired patients and 45 cognitively preserved patients) were included, and all patients underwent functional MRI and clinical assessments. After calculating the amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), voxel-mirrored homotopic connectivity (VMHC) and degree centrality (DC) imaging, the radiomic features (n = 819) of bilateral hippocampi were extracted from these images, respectively. After feature selection, machine learning models were trained. Finally, we further analyzed the relationship between the hippocampal functional radiomic features and clinical measures, to explore the clinical significance of these features.
Results: The combined radiomic features model logistic regression algorithm superior performance in distinguishing cognitively impaired patients from LBLP (AUC = 0.970, accuracy = 92.3%, sensitivity = 92.3%, specificity = 92.3%) compared to the other models. Additionally, radiomic wavelet features were correlated with Montreal Cognitive Assessment (MoCA) and Hamilton Anxiety Scale, present pain intensity scores in cognitively impaired LBLP patients (P < 0.05, with Bonferroni correction).
Conclusion: Hippocampal functional radiomic features are valuable for diagnosing cognitively impaired patients from LBLP.

Keywords: cognitive impairment, resting-state functional MRI, low-back-related leg pain, radiomic, logistic regression algorithm