Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach.
Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach.
复制标题
DOI：
10.3389/fonc.2020.606741
复制
发表时间：
2020
影响因子：
4.7
通讯作者：
Wang L
中科院分区：
文献类型：
--
作者：
Fang S;Fan Z;Sun Z;Li Y;Liu X;Liang Y;Liu Y;Zhou C;Zhu Q;Zhang H;Li T;Li S;Jiang T;Wang Y;Wang L
端粒酶逆转录酶启动子（pTERT）突变的检测是重要的，因为术前诊断pTERT状态有助于评估预后和确定手术策略。在这里，我们的目的是建立一个基于放射组学的机器学习算法，并评估其在预测世界卫生组织（WHO）II级胶质瘤患者pTERT突变方面的性能。本回顾性研究共入组了164例WHO II级胶质瘤患者。我们从多参数磁共振成像扫描中提取了总共1，293个放射组学特征。弹性网络（用于特征选择）和线性核的支持向量机应用于嵌套的10折交叉验证循环。通过受试者操作特征和精确-召回分析对预测模型进行评估。我们进行了非配对t检验以比较具有不同pTERT状态的患者之间的后验预测概率。我们选择了12个有价值的放射组学特征，使用嵌套的10倍交叉验证循环。曲线下面积（AUC）为0.8446（95%置信区间[CI]，0.7735-0.9065），灵敏度的最佳总和值为0.9355（95% CI，0.8802-0.9788），特异性为0.6197（95% CI，0.5071-0.7371）。总体准确度为0.7988（95% CI，0.7378-0.8598）。F1评分为0.8406（95%CI，0.7684-0.902），最佳精确度为0.7632（95%CI，0.6818-0.8364），召回率为0.9355（95%CI，0.8802-0.9788）。pTERT突变的后验概率在野生型和突变型TERT启动子患者之间有显著差异。我们的研究结果表明，使用机器学习算法的放射组学分析可用于预测WHO II级胶质瘤患者的pTERT状态，并可能有助于胶质瘤管理。
The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735–0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802–0.9788) and specificity of 0.6197 (95% CI, 0.5071–0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378–0.8598). The F1-score was 0.8406 (95% CI, 0.7684–0.902) with an optimal precision of 0.7632 (95% CI, 0.6818–0.8364) and recall of 0.9355 (95% CI, 0.8802–0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.
登录
查看更多内容
影响因子：
4.6
作者：
Arita H;Kinoshita M;Kawaguchi A;Takahashi M;Narita Y;Terakawa Y;Tsuyuguchi N;Okita Y;Nonaka M;Moriuchi S;Takagaki M;Fujimoto Y;Fukai J;Izumoto S;Ishibashi K;Nakajima Y;Shofuda T;Kanematsu D;Yoshioka E;Kodama Y;Mano M;Mori K;Ichimura K;Kanemura Y
通讯作者：
Kanemura Y
DOI：
10.1056/nejmoa1407279
发表时间：
2015-06-25
期刊：
The New England journal of medicine
影响因子：
--
作者：
Eckel-Passow JE;Lachance DH;Molinaro AM;Walsh KM;Decker PA;Sicotte H;Pekmezci M;Rice T;Kosel ML;Smirnov IV;Sarkar G;Caron AA;Kollmeyer TM;Praska CE;Chada AR;Halder C;Hansen HM;McCoy LS;Bracci PM;Marshall R;Zheng S;Reis GF;Pico AR;O'Neill BP;Buckner JC;Giannini C;Huse JT;Perry A;Tihan T;Berger MS;Chang SM;Prados MD;Wiemels J;Wiencke JK;Wrensch MR;Jenkins RB
通讯作者：
Jenkins RB
影响因子：
2.8
作者：
Jiang, Chendan;Kong, Ziren;Ma, Wenbin
通讯作者：
Ma, Wenbin
影响因子：
5.9
作者：
Kocak, Burak;Durmaz, Emine Sebnem;Kilickesmez, Ozgur
通讯作者：
Kilickesmez, Ozgur
使用 MRI 放射组学特征预测低级别胶质瘤 ATRX 突变的基因型
影响因子：
5.9
作者：
Li, Yiming;Liu, Xing;Jiang, Tao
通讯作者：
Jiang, Tao