Prediction of IDH and TERT promoter mutations in low-grade glioma from magnetic resonance images using a convolutional neural network

被引：48

作者：

Fukuma, Ryohei ^{[1
,2
]}

Yanagisawa, Takufumi ^{[1
,2
,3
]}

Kinoshita, Manabu ^{[1
]}

Shinozaki, Takashi ^{[4
,22
]}

Arita, Hideyuki ^{[1
,5
,6
]}

Kawaguchi, Atsushi ^{[7
]}

Takahashi, Masamichi ^{[8
]}

Narita, Yoshitaka ^{[8
]}

Terakawa, Yuzo ^{[5
,9
]}

Tsuyuguchi, Naohiro ^{[5
,9
,10
]}

Okita, Yoshiko ^{[21
]}

Nonaka, Masahiro ^{[5
,11
,12
]}

Moriuchi, Shusuke ^{[5
,11
,13
]}

Takagaki, Masatoshi ^{[1
,5
]}

Fujimoto, Yasunori ^{[1
,5
]}

Fukai, Junya ^{[5
,14
]}

Izumoto, Shuichi ^{[5
,10
]}

Ishibashi, Kenichi ^{[5
,9
]}

Nakajima, Yoshikazu ^{[5
,15
]}

Shofuda, Tomoko ^{[5
,16
]}

Kanematsu, Daisuke ^{[5
,17
]}

Yoshioka, Ema ^{[5
,16
]}

Kodama, Yoshinori ^{[18
]}

Mano, Masayuki ^{[5
,19
]}

Mori, Kanji ^{[5
,20
]}

Ichimura, Koichi ^{[6
]}

Kanemura, Yonehiro ^{[5
,17
]}

Kishima, Haruhiko ^{[1
]}

机构：

[1] Osaka Univ, Grad Sch Med, Dept Neurosurg, 2-2Yamadaoka, Suita, Osaka 5650871, Japan

[2] ATR Computat Neurosci Labs, Dept Neuroinformat, 2-2-2 Hikaridai,Seika Cho, Kyoto 6190288, Japan

[3] Osaka Univ, Inst Adv Cocreat Studies, 2-2Yamadaoka, Suita, Osaka 5650871, Japan

[4] Natl Inst Informat & Commun Technol, Ctr Informat & Neural Networks, 1-4Yamadaoka, Suita, Osaka 5650871, Japan

[5] Kansai Mol Diag Network CNS Tumors, Osaka 5400006, Japan

[6] Natl Canc Ctr, Div Brain Tumor Translat Res, Res Inst, Tokyo 1040045, Japan

[7] Saga Univ, Fac Med, Educ & Res Ctr Community Med, Saga 8498501, Japan

[8] Natl Canc Ctr, Dept Neurosurg & Neurooncol, Tokyo 1040045, Japan

[9] Osaka City Gen Hosp, Dept Neurosurg, Osaka 5340021, Japan

[10] Kindai Univ, Dept Neurosurg, Fac Med, Sayama, Osaka 5898511, Japan

[11] Natl Hosp Org, Dept Neurosurg, Osaka Natl Hosp, Osaka 5400006, Japan

[12] Kansai Med Univ, Dept Neurosurg, Hirakata, Osaka 5731191, Japan

[13] Rinku Gen Med Ctr, Dept Neurosurg, Izumisano 5988577, Japan

[14] Wakayama Med Univ, Dept Neurol Surg, Sch Med, Wakayama 6410012, Japan

[15] Sakai City Med Ctr, Dept Neurosurg, Sakai, Osaka 5938304, Japan

[16] Natl Hosp Org, Inst Clin Res, Dept Biomed Res & Innovat, Div Stem Cell Res,Osaka Natl Hosp, Osaka 5400006, Japan

[17] Natl Hosp Org, Inst Clin Res, Dept Biomed Res & Innovat, Div Regenerat Med,Osaka Natl Hosp, Osaka 5400006, Japan

[18] Kobe Univ, Dept Diagnost Pathol, Grad Sch Med, Chuo Ku, 7-5-1 Kusunoki Cho, Kobe, Hyogo 6500017, Japan

[19] Natl Hosp Org, Dept Cent Lab & Surg Pathol, Osaka Natl Hosp, Osaka 5400006, Japan

[20] Kansai Rosai Hosp, Dept Neurosurg, Amagasaki, Hyogo 6608511, Japan

[21] Osaka Prefectural Hosp Org, Osaka Int Canc Inst, Dept Neurosurg, Osaka 5418567, Japan

[22] Osaka Univ, Grad Sch Informat Sci & Technol, 1-5 Yamadaoka, Suita, Osaka 5650871, Japan

来源：

SCIENTIFIC REPORTS | 2019年 / 9卷 / 1期

关键词：

CELL LUNG-CANCER; SURVIVAL; ROBUST; HETEROGENEITY; OPTIMIZATION; REGISTRATION; SIGNATURES; ACCURATE; RISK;

D O I：

10.1038/s41598-019-56767-3

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Identification of genotypes is crucial for treatment of glioma. Here, we developed a method to predict tumor genotypes using a pretrained convolutional neural network (CNN) from magnetic resonance (MR) images and compared the accuracy to that of a diagnosis based on conventional radiomic features and patient age. Multisite preoperative MR images of 164 patients with grade II/III glioma were grouped by IDH and TERT promoter (pTERT) mutations as follows: (1) IDH wild type, (2) IDH and pTERT co-mutations, (3) IDH mutant and pTERT wild type. We applied a CNN (AlexNet) to four types of MR sequence and obtained the CNN texture features to classify the groups with a linear support vector machine. The classification was also performed using conventional radiomic features and/or patient

引用

页数：8

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