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首页> 外文期刊>Acta Radiologica >Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas
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Machine learning-based unenhanced CT texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas

机译：基于机器学习的未加入CT纹理分析，用于预测透明细胞肾细胞癌的BAP1突变状态

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Background BRCA1-associated protein 1 (BAP1) mutation is an unfavorable factor for overall survival in patients with clear cell renal cell carcinoma (ccRCC). Radiomics literature about BAP1 mutation lacks papers that consider the reliability of texture features in their workflow. Purpose Using texture features with a high inter-observer agreement, we aimed to develop and internally validate a machine learning-based radiomic model for predicting the BAP1 mutation status of ccRCCs. Material and Methods For this retrospective study, 65 ccRCCs were included from a public database. Texture features were extracted from unenhanced computed tomography (CT) images, using two-dimensional manual segmentation. Dimension reduction was done in three steps: (i) inter-observer agreement analysis; (ii) collinearity analysis; and (iii) feature selection. The machine learning classifier was random forest. The model was validated using 10-fold nested cross-validation. The reference standard was the BAP1 mutation status. Results Out of 744 features, 468 had an excellent inter-observer agreement. After the collinearity analysis, the number of features decreased to 17. Finally, the wrapper-based algorithm selected six features. Using selected features, the random forest correctly classified 84.6% of the labelled slices regarding BAP1 mutation status with an area under the receiver operating characteristic curve of 0.897. For predicting ccRCCs with BAP1 mutation, the sensitivity, specificity, and precision were 90.4%, 78.8%, and 81%, respectively. For predicting ccRCCs without BAP1 mutation, the sensitivity, specificity, and precision were 78.8%, 90.4%, and 89.1%, respectively. Conclusion Machine learning-based unenhanced CT texture analysis might be a potential method for predicting the BAP1 mutation status of ccRCCs.
机译：背景技术BRCA1相关蛋白1（BAP1）突变是透明细胞肾细胞癌（CCRCC）患者整体存活的不利因素。关于BAP1突变的辐射瘤文献缺乏考虑其工作流程中纹理特征的可靠性的论文。目的使用具有高观察员间协议的纹理特征，我们旨在开发和内部验证基于机器学习的辐射族模型，用于预测CCRCC的BAP1突变状态。本回顾性研究的材料和方法，来自公共数据库中包含65个CCRCC。使用二维手动分段，从未加入计算断层扫描（CT）图像中提取纹理特征。减少尺寸减少三个步骤：（i）观察员间协议分析; （ii）共同性分析;和（iii）功能选择。机器学习分类器是随机森林。使用10倍嵌套交叉验证验证该模型。参考标准是BAP1突变状态。结果为744个功能，468个具有出色的观察者间协议。在相机性分析之后，功能的数量减少到17.最后，基于包装器的算法选择了六个特征。使用所选特征，随机森林正确分类了有关BAP1突变状态的标记片的84.6％，接收器下的接收器的一个区域为0.897。为了预测具有BAP1突变的CCRCC，敏感性，特异性和精度分别为90.4％，78.8％和81％。为了预测没有BAP1突变的CCRCC，敏感性，特异性和精度分别为78.8％，90.4％和89.1％。结论基于机器学习的未加入CT纹理分析可能是预测CCRCCS的BAP1突变状态的潜在方法。

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