Yin Gang, Li Churong, Chen Heng, Luo Yangkun, Orlandini Lucia Clara, Wang Pei, Lang Jinyi
Department of Radiation Oncology, Sichuan Cancer Hospital & Institute, No.55, the 4th Section, Renmin South Road, Chengdu, 610041, Sichuan, China.
Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China.
Clin Exp Metastasis. 2017 Feb;34(2):115-124. doi: 10.1007/s10585-016-9833-7. Epub 2017 Jan 18.
In this study the relationship between brain structure and brain metastases (BM) occurrence was analyzed. A model for predicting the time of BM onset in patients with non-small cell lung cancer (NSCLC) was proposed. Twenty patients were used to develop the model, whereas the remaining 69 were used for independent validation and verification of the model. Magnetic resonance images were segmented into cerebrospinal fluid, gray matter (GM), and white matter using voxel-based morphometry. Automatic anatomic labeling template was used to extract 116 brain regions from the GM volume. The elapsed time between the MRI acquisitions and BM diagnosed was analyzed using the least absolute shrinkage and selection operator method. The model was validated using the leave-one-out cross validation (LOOCV) and permutation test. The GM volume of the extracted 11 regions of interest increased with the progression of BM from NSCLC. LOOCV test on the model indicated that the measured and predicted BM onset were highly correlated (r = 0.834, P = 0.0000). For the 69 independent validating patients, accuracy, sensitivity, and specificity of the model for predicting BM occurrence were 70, 75, and 66%, respectively, in 6 months and 74, 82, and 60%, respectively, in 1 year. The extracted brain GM volumes and interval times for BM occurrence were correlated. The established model based on MRI data may reliably predict BM in 6 months or 1 year. Further studies with larger sample size are needed to validate the findings in a clinical setting.
在本研究中，分析了脑结构与脑转移（BM）发生之间的关系。提出了一种预测非小细胞肺癌（NSCLC）患者BM发病时间的模型。20例患者用于建立模型，其余69例用于模型的独立验证和核实。使用基于体素的形态测量法将磁共振图像分割为脑脊液、灰质（GM）和白质。使用自动解剖标记模板从GM体积中提取116个脑区。使用最小绝对收缩和选择算子方法分析MRI采集与诊断出BM之间的时间间隔。使用留一法交叉验证（LOOCV）和置换检验对模型进行验证。随着NSCLC患者BM的进展，提取的11个感兴趣区域的GM体积增加。对模型的LOOCV检验表明，测量的和预测的BM发病高度相关（r = 0.834，P = 0.0000）。对于69例独立验证患者，模型预测BM发生的准确性、敏感性和特异性在6个月时分别为70%、75%和66%，在1年时分别为74%、82%和60%。提取的脑GM体积与BM发生的间隔时间相关。基于MRI数据建立的模型可以可靠地预测6个月或1年内的BM。需要进行更大样本量的进一步研究以在临床环境中验证这些发现。
