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Cancer Imaging. 2019 Mar 25;19(1):19. doi: 10.1186/s40644-019-0203-y.

Application of a machine learning method to whole brain white matter injury after radiotherapy for nasopharyngeal carcinoma.

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Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China.
Department of Psychology, The Fourth Military Medical University, Xi'an, Shaanxi, 710032, People's Republic of China.
Department of Radiology, Zhujiang Hospital of Southern Medical University, No. 253, Gong Ye Da Dao Zhong, Guangzhou, Guangdong, 510280, People's Republic of China.
Medical Imaging Center, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, Guangdong, 510405, People's Republic of China.



The purpose/aim of this study was to 1) use magnetic resonance diffusion tensor imaging (DTI), fibre bundle/tract-based spatial statistics (TBSS) and machine learning methods to study changes in the white matter (WM) structure and whole brain WM network in different periods of the nasopharyngeal carcinoma (NPC) patients after radiotherapy (RT), 2) identify the most discriminating WM regions and WM connections as biomarkers of radiation brain injury (RBI), and 3) supplement the understanding of the pathogenesis of RBI, which is useful for early diagnosis in the clinic.


A DTI scan was performed in 77 patients and 67 normal controls. A fractional anisotropy map was generated by DTIFit. TBSS was used to find the region where the FA differed between the case and control groups. Each resulting FA value image is registered with each other to create an average FA value skeleton. Each resultant FA skeleton image was connected to feature vectors, and features with significant differences were extracted and classified using a support vector machine (SVM). Next, brain segmentation was performed on each subject's DTI image using automated anatomical labeling (AAL), and deterministic white matter fiber bundle tracking was performed to generate symmetrical brain matrix, select the upper triangular component as a classification feature. Two-sample t-test was used to extract the features with significant differences, then classified by SVM. Finally, we adopted a permutation test and ROC curves to evaluate the reliability of the classifier.


For FA, the accuracy of classification between the 0-6, 6-12 and > 12 months post-RT groups and the control group was 84.5, 83.9 and 74.5%, respectively. In the case groups, the FA with discriminative ability was reduced, mainly in the bilateral cerebellum and bilateral temporal lobe, with prolonged time, the damage was aggravated. For WM connections, the SVM classifier classification recognition rates of the 0-6, 6-12 and > 12 months post-RT groups reached 82.5, 78.4 and 76.3%, respectively. The WM connections with discriminative ability were reduced.


RBI is a disease involving whole brain WM network anomalies. These brain discriminating WM regions and WM connection modes can supplement the understanding of RBI and be used as biomarkers for the early clinical diagnosis of RBI.


Diffusion tensor imaging; Nasopharyngeal carcinoma; Radiation injuries

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