Advances in molecular and genetic technologies have enabled the study of mutation and molecular changes in gliomas. The 1p/19q coding state of gliomas is important in predicting pathogenesis-based pharmacological treatments and determining innovative immunotherapeutic strategies. In this study, T1-weighted and T2-weighted fluid-attenuated inversion recovery magnetic resonance imaging (MRI) images of 121 low-grade glioma patients with biopsy-proven 1p/19q coding status and no deletion (n = 40) or co-deletion (n = 81) were used. First, regions of interests were segmented with the grow-cut algorithm. Later, 851 radiomic features including three-dimensional wavelet preprocessed and non-preprocessed ones were extracted from six different matrices such as first order, shape and texture. The extracted features were preprocessed with the synthetic minority over-sampling technique algorithm. Next, the 1p/19q decoding states of gliomas were classified using machine-learning algorithms. The best classification in the classification of glioma grades (grade II and grade III) according to 1p/19q coding status was obtained by using the logistic regression algorithm, with 93.94% accuracy and 94.74% area under the curve values. In conclusion, it was determined that non-invasive estimation of 1p/19q status from MRI images enables the selection of effective treatment strategies with early diagnosis using machine-learning algorithms without the need for surgical biopsy.

中文翻译：

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使用机器学习预测脑肿瘤的 1p/19q 染色体缺失

分子和遗传技术的进步使研究胶质瘤的突变和分子变化成为可能。胶质瘤的 1p/19q 编码状态对于预测基于发病机制的药物治疗和确定创新的免疫治疗策略很重要。在这项研究中，121 名低级别胶质瘤患者的 T1 加权和 T2 加权流体衰减反转恢复磁共振成像 (MRI) 图像经活检证实为 1p/19q 编码状态且无缺失 ( n = 40) 或共删除 ( n= 81) 被使用。首先，用grow-cut算法分割感兴趣的区域。随后，从一阶、形状和纹理等六个不同的矩阵中提取了包括三维小波预处理和未预处理的851个放射组学特征。提取的特征采用合成少数过采样技术算法进行预处理。接下来，使用机器学习算法对神经胶质瘤的 1p/19q 解码状态进行分类。根据1p/19q编码状态对胶质瘤分级（II级和III级）的最佳分类是使用逻辑回归算法得到的，准确率为93.94%，曲线下面积为94.74%。综上所述，