Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis.
Multiparametric MRI Features Predict the SYP Gene Expression in Low-Grade Glioma Patients: A Machine Learning-Based Radiomics Analysis.
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多参数 MRI 特征预测低级别胶质瘤患者的 SYP 基因表达：基于机器学习的放射组学分析
DOI：
10.3389/fonc.2021.663451
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发表时间：
2021
影响因子：
4.7
通讯作者：
Chen WL
中科院分区：
文献类型：
--
作者：
Xiao Z;Yao S;Wang ZM;Zhu DM;Bie YN;Zhang SZ;Chen WL
突触素（SYP）基因表达水平与神经胶质瘤患者的生存率相关。本研究旨在探讨应用由卷积神经网络组成的多参数磁共振成像（MRI）放射组学模型来预测胶质瘤患者SYP基因表达的可行性。我们使用 TCGA 数据库检查了 614 名被诊断患有神经胶质瘤的患者。首先，使用偏相关分析研究了 SYP 基因表达水平与生存率结果之间的关系。然后，从拥有可用多参数 MRI 扫描的 108 名低级别胶质瘤患者中每人提取 7266 个斑块，其中包括 TCIA 数据库中的术前 T1 加权图像 (T1WI)、T2 加权图像 (T2WI) 和对比增强 T1WI 图像。最后，使用卷积神经网络（ConvNet）构建了基于放射组学特征的模型，该模型可以使用ROC曲线、准确率、召回率、敏感性和特异性作为评估指标进行自主学习分类。 SYP的表达水平随着肿瘤分级的增加而降低。对于II级、III级和一般患者，SYP表达水平较高的患者生存率较高。然而，SYP 表达水平与 IV 级患者的结果没有显示出任何显着相关性。我们使用 ConvNet 构建的多参数 MRI 放射组学模型在预测低级别胶质瘤患者的 SYP 基因表达水平和预后方面表现出良好的性能。
Synaptophysin (SYP) gene expression levels correlate with the survival rate of glioma patients. This study aimed to explore the feasibility of applying a multiparametric magnetic resonance imaging (MRI) radiomics model composed of a convolutional neural network to predict the SYP gene expression in patients with glioma. Using the TCGA database, we examined 614 patients diagnosed with glioma. First, the relationship between the SYP gene expression level and outcome of survival rate was investigated using partial correlation analysis. Then, 7266 patches were extracted from each of the 108 low-grade glioma patients who had available multiparametric MRI scans, which included preoperative T1-weighted images (T1WI), T2-weighted images (T2WI), and contrast-enhanced T1WI images in the TCIA database. Finally, a radiomics features-based model was built using a convolutional neural network (ConvNet), which can perform autonomous learning classification using a ROC curve, accuracy, recall rate, sensitivity, and specificity as evaluation indicators. The expression level of SYP decreased with the increase in the tumor grade. With regard to grade II, grade III, and general patients, those with higher SYP expression levels had better survival rates. However, the SYP expression level did not show any significant association with the outcome in Level IV patients. Our multiparametric MRI radiomics model constructed using ConvNet showed good performance in predicting the SYP gene expression level and prognosis in low-grade glioma patients.
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影响因子：
15.9
作者：
Verburg, Niels;Koopman, Thomas;Hamer, Philip C. de Witt
通讯作者：
Hamer, Philip C. de Witt
DOI：
10.1016/s1470-2045(17)30252-8
发表时间：
2017-05
期刊：
The Lancet. Oncology
影响因子：
--
作者：
Caudell JJ;Torres-Roca JF;Gillies RJ;Enderling H;Kim S;Rishi A;Moros EG;Harrison LB
通讯作者：
Harrison LB
影响因子：
14.8
作者：
Maros, Mate E.;Capper, David;Sill, Martin
通讯作者：
Sill, Martin
DOI：
10.1056/nejmoa1402121
发表时间：
2015-06-25
期刊：
The New England journal of medicine
影响因子：
--
作者：
Cancer Genome Atlas Research Network;Brat DJ;Verhaak RG;Aldape KD;Yung WK;Salama SR;Cooper LA;Rheinbay E;Miller CR;Vitucci M;Morozova O;Robertson AG;Noushmehr H;Laird PW;Cherniack AD;Akbani R;Huse JT;Ciriello G;Poisson LM;Barnholtz-Sloan JS;Berger MS;Brennan C;Colen RR;Colman H;Flanders AE;Giannini C;Grifford M;Iavarone A;Jain R;Joseph I;Kim J;Kasaian K;Mikkelsen T;Murray BA;O'Neill BP;Pachter L;Parsons DW;Sougnez C;Sulman EP;Vandenberg SR;Van Meir EG;von Deimling A;Zhang H;Crain D;Lau K;Mallery D;Morris S;Paulauskis J;Penny R;Shelton T;Sherman M;Yena P;Black A;Bowen J;Dicostanzo K;Gastier-Foster J;Leraas KM;Lichtenberg TM;Pierson CR;Ramirez NC;Taylor C;Weaver S;Wise L;Zmuda E;Davidsen T;Demchok JA;Eley G;Ferguson ML;Hutter CM;Mills Shaw KR;Ozenberger BA;Sheth M;Sofia HJ;Tarnuzzer R;Wang Z;Yang L;Zenklusen JC;Ayala B;Baboud J;Chudamani S;Jensen MA;Liu J;Pihl T;Raman R;Wan Y;Wu Y;Ally A;Auman JT;Balasundaram M;Balu S;Baylin SB;Beroukhim R;Bootwalla MS;Bowlby R;Bristow CA;Brooks D;Butterfield Y;Carlsen R;Carter S;Chin L;Chu A;Chuah E;Cibulskis K;Clarke A;Coetzee SG;Dhalla N;Fennell T;Fisher S;Gabriel S;Getz G;Gibbs R;Guin R;Hadjipanayis A;Hayes DN;Hinoue T;Hoadley K;Holt RA;Hoyle AP;Jefferys SR;Jones S;Jones CD;Kucherlapati R;Lai PH;Lander E;Lee S;Lichtenstein L;Ma Y;Maglinte DT;Mahadeshwar HS;Marra MA;Mayo M;Meng S;Meyerson ML;Mieczkowski PA;Moore RA;Mose LE;Mungall AJ;Pantazi A;Parfenov M;Park PJ;Parker JS;Perou CM;Protopopov A;Ren X;Roach J;Sabedot TS;Schein J;Schumacher SE;Seidman JG;Seth S;Shen H;Simons JV;Sipahimalani P;Soloway MG;Song X;Sun H;Tabak B;Tam A;Tan D;Tang J;Thiessen N;Triche T Jr;Van Den Berg DJ;Veluvolu U;Waring S;Weisenberger DJ;Wilkerson MD;Wong T;Wu J;Xi L;Xu AW;Yang L;Zack TI;Zhang J;Aksoy BA;Arachchi H;Benz C;Bernard B;Carlin D;Cho J;DiCara D;Frazer S;Fuller GN;Gao J;Gehlenborg N;Haussler D;Heiman DI;Iype L;Jacobsen A;Ju Z;Katzman S;Kim H;Knijnenburg T;Kreisberg RB;Lawrence MS;Lee W;Leinonen K;Lin P;Ling S;Liu W;Liu Y;Liu Y;Lu Y;Mills G;Ng S;Noble MS;Paull E;Rao A;Reynolds S;Saksena G;Sanborn Z;Sander C;Schultz N;Senbabaoglu Y;Shen R;Shmulevich I;Sinha R;Stuart J;Sumer SO;Sun Y;Tasman N;Taylor BS;Voet D;Weinhold N;Weinstein JN;Yang D;Yoshihara K;Zheng S;Zhang W;Zou L;Abel T;Sadeghi S;Cohen ML;Eschbacher J;Hattab EM;Raghunathan A;Schniederjan MJ;Aziz D;Barnett G;Barrett W;Bigner DD;Boice L;Brewer C;Calatozzolo C;Campos B;Carlotti CG Jr;Chan TA;Cuppini L;Curley E;Cuzzubbo S;Devine K;DiMeco F;Duell R;Elder JB;Fehrenbach A;Finocchiaro G;Friedman W;Fulop J;Gardner J;Hermes B;Herold-Mende C;Jungk C;Kendler A;Lehman NL;Lipp E;Liu O;Mandt R;McGraw M;Mclendon R;McPherson C;Neder L;Nguyen P;Noss A;Nunziata R;Ostrom QT;Palmer C;Perin A;Pollo B;Potapov A;Potapova O;Rathmell WK;Rotin D;Scarpace L;Schilero C;Senecal K;Shimmel K;Shurkhay V;Sifri S;Singh R;Sloan AE;Smolenski K;Staugaitis SM;Steele R;Thorne L;Tirapelli DP;Unterberg A;Vallurupalli M;Wang Y;Warnick R;Williams F;Wolinsky Y;Bell S;Rosenberg M;Stewart C;Huang F;Grimsby JL;Radenbaugh AJ;Zhang J
通讯作者：
Zhang J
影响因子：
64.8
作者：
Barthel, Floris P.;Johnson, Kevin C.;Verhaak, Roel G. W.
通讯作者：
Verhaak, Roel G. W.