利用患者特征、放射学特征和磁共振成像预测弥漫性胶质瘤成人患者的异柠檬酸脱氢酶状态:通过可变视觉转换器进行多模态分析。

IF 2.1 4区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Magnetic resonance imaging Pub Date : 2024-05-29 DOI:10.1016/j.mri.2024.05.012
Takuma Usuzaki , Ryusei Inamori , Takashi Shizukuishi , Yohei Morishita , Hidenobu Takagi , Mami Ishikuro , Taku Obara , Kei Takase
{"title":"利用患者特征、放射学特征和磁共振成像预测弥漫性胶质瘤成人患者的异柠檬酸脱氢酶状态:通过可变视觉转换器进行多模态分析。","authors":"Takuma Usuzaki ,&nbsp;Ryusei Inamori ,&nbsp;Takashi Shizukuishi ,&nbsp;Yohei Morishita ,&nbsp;Hidenobu Takagi ,&nbsp;Mami Ishikuro ,&nbsp;Taku Obara ,&nbsp;Kei Takase","doi":"10.1016/j.mri.2024.05.012","DOIUrl":null,"url":null,"abstract":"<div><h3>Objectives</h3><p>To evaluate the performance of the multimodal model, termed variable Vision Transformer (vViT), in the task of predicting isocitrate dehydrogenase (IDH) status among adult patients with diffuse glioma.</p></div><div><h3>Materials and methods</h3><p>vViT was designed to predict IDH status using patient characteristics (sex and age), radiomic features, and contrast-enhanced T1-weighted images (CE-T1WI). Radiomic features were extracted from each enhancing tumor (ET), necrotic tumor core (NCR), and peritumoral edematous/infiltrated tissue (ED). CE-T1WI were split into four images and input to vViT. In the training, internal test, and external test, 271 patients with 1070 images (535 IDH wildtype, 535 IDH mutant), 35 patients with 194 images (97 IDH wildtype, 97 IDH mutant), and 291 patients with 872 images (436 IDH wildtype, 436 IDH mutant) were analyzed, respectively. Metrics including accuracy and AUC-ROC were calculated for the internal and external test datasets. Permutation importance analysis combined with the Mann–Whitney <em>U</em> test was performed to compare inputs.</p></div><div><h3>Results</h3><p>For the internal test dataset, vViT correctly predicted IDH status for all patients. For the external test dataset, an accuracy of 0.935 (95% confidence interval; 0.913–0.945) and AUC-ROC of 0.887 (0.798–0.956) were obtained. For both internal and external test datasets, CE-T1WI ET radiomic features and patient characteristics had higher importance than other inputs (<em>p</em> &lt; 0.05).</p></div><div><h3>Conclusions</h3><p>The vViT has the potential to be a competent model in predicting IDH status among adult patients with diffuse glioma. Our results indicate that age, sex, and CE-T1WI ET radiomic features have key information in estimating IDH status.</p></div>","PeriodicalId":18165,"journal":{"name":"Magnetic resonance imaging","volume":"111 ","pages":"Pages 266-276"},"PeriodicalIF":2.1000,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0730725X24001620/pdfft?md5=6cb64e915f2d33c0b818d4b1e6d9d7d2&pid=1-s2.0-S0730725X24001620-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Predicting isocitrate dehydrogenase status among adult patients with diffuse glioma using patient characteristics, radiomic features, and magnetic resonance imaging: Multi-modal analysis by variable vision transformer\",\"authors\":\"Takuma Usuzaki ,&nbsp;Ryusei Inamori ,&nbsp;Takashi Shizukuishi ,&nbsp;Yohei Morishita ,&nbsp;Hidenobu Takagi ,&nbsp;Mami Ishikuro ,&nbsp;Taku Obara ,&nbsp;Kei Takase\",\"doi\":\"10.1016/j.mri.2024.05.012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objectives</h3><p>To evaluate the performance of the multimodal model, termed variable Vision Transformer (vViT), in the task of predicting isocitrate dehydrogenase (IDH) status among adult patients with diffuse glioma.</p></div><div><h3>Materials and methods</h3><p>vViT was designed to predict IDH status using patient characteristics (sex and age), radiomic features, and contrast-enhanced T1-weighted images (CE-T1WI). Radiomic features were extracted from each enhancing tumor (ET), necrotic tumor core (NCR), and peritumoral edematous/infiltrated tissue (ED). CE-T1WI were split into four images and input to vViT. In the training, internal test, and external test, 271 patients with 1070 images (535 IDH wildtype, 535 IDH mutant), 35 patients with 194 images (97 IDH wildtype, 97 IDH mutant), and 291 patients with 872 images (436 IDH wildtype, 436 IDH mutant) were analyzed, respectively. Metrics including accuracy and AUC-ROC were calculated for the internal and external test datasets. Permutation importance analysis combined with the Mann–Whitney <em>U</em> test was performed to compare inputs.</p></div><div><h3>Results</h3><p>For the internal test dataset, vViT correctly predicted IDH status for all patients. For the external test dataset, an accuracy of 0.935 (95% confidence interval; 0.913–0.945) and AUC-ROC of 0.887 (0.798–0.956) were obtained. For both internal and external test datasets, CE-T1WI ET radiomic features and patient characteristics had higher importance than other inputs (<em>p</em> &lt; 0.05).</p></div><div><h3>Conclusions</h3><p>The vViT has the potential to be a competent model in predicting IDH status among adult patients with diffuse glioma. Our results indicate that age, sex, and CE-T1WI ET radiomic features have key information in estimating IDH status.</p></div>\",\"PeriodicalId\":18165,\"journal\":{\"name\":\"Magnetic resonance imaging\",\"volume\":\"111 \",\"pages\":\"Pages 266-276\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-05-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0730725X24001620/pdfft?md5=6cb64e915f2d33c0b818d4b1e6d9d7d2&pid=1-s2.0-S0730725X24001620-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Magnetic resonance imaging\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0730725X24001620\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Magnetic resonance imaging","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0730725X24001620","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

研究目的材料与方法:设计 vViT 的目的是利用患者特征(性别和年龄)、放射学特征和对比增强 T1 加权图像(CE-T1WI)预测 IDH 状态。从每个增强肿瘤(ET)、坏死瘤核(NCR)和瘤周水肿/浸润组织(ED)中提取放射学特征。CE-T1WI 被分成四幅图像并输入 vViT。在训练、内部测试和外部测试中,分别分析了271例患者的1070张图像(535张IDH野生型,535张IDH突变型)、35例患者的194张图像(97张IDH野生型,97张IDH突变型)和291例患者的872张图像(436张IDH野生型,436张IDH突变型)。计算了内部和外部测试数据集的准确度和 AUC-ROC 等指标。在比较输入结果时,进行了置换重要性分析和曼-惠特尼U检验:对于内部测试数据集,vViT 能正确预测所有患者的 IDH 状态。外部测试数据集的准确率为 0.935(95% 置信区间;0.913-0.945),AUC-ROC 为 0.887(0.798-0.956)。在内部和外部测试数据集中,CE-T1WI ET 放射特征和患者特征的重要性均高于其他输入数据(p 结论:vViT 有潜力成为一种新的诊断方法:vViT 有可能成为预测弥漫性胶质瘤成人患者 IDH 状态的有效模型。我们的研究结果表明,年龄、性别和CE-T1WI ET放射学特征是估计IDH状态的关键信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Predicting isocitrate dehydrogenase status among adult patients with diffuse glioma using patient characteristics, radiomic features, and magnetic resonance imaging: Multi-modal analysis by variable vision transformer

Objectives

To evaluate the performance of the multimodal model, termed variable Vision Transformer (vViT), in the task of predicting isocitrate dehydrogenase (IDH) status among adult patients with diffuse glioma.

Materials and methods

vViT was designed to predict IDH status using patient characteristics (sex and age), radiomic features, and contrast-enhanced T1-weighted images (CE-T1WI). Radiomic features were extracted from each enhancing tumor (ET), necrotic tumor core (NCR), and peritumoral edematous/infiltrated tissue (ED). CE-T1WI were split into four images and input to vViT. In the training, internal test, and external test, 271 patients with 1070 images (535 IDH wildtype, 535 IDH mutant), 35 patients with 194 images (97 IDH wildtype, 97 IDH mutant), and 291 patients with 872 images (436 IDH wildtype, 436 IDH mutant) were analyzed, respectively. Metrics including accuracy and AUC-ROC were calculated for the internal and external test datasets. Permutation importance analysis combined with the Mann–Whitney U test was performed to compare inputs.

Results

For the internal test dataset, vViT correctly predicted IDH status for all patients. For the external test dataset, an accuracy of 0.935 (95% confidence interval; 0.913–0.945) and AUC-ROC of 0.887 (0.798–0.956) were obtained. For both internal and external test datasets, CE-T1WI ET radiomic features and patient characteristics had higher importance than other inputs (p < 0.05).

Conclusions

The vViT has the potential to be a competent model in predicting IDH status among adult patients with diffuse glioma. Our results indicate that age, sex, and CE-T1WI ET radiomic features have key information in estimating IDH status.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Magnetic resonance imaging
Magnetic resonance imaging 医学-核医学
CiteScore
4.70
自引率
4.00%
发文量
194
审稿时长
83 days
期刊介绍: Magnetic Resonance Imaging (MRI) is the first international multidisciplinary journal encompassing physical, life, and clinical science investigations as they relate to the development and use of magnetic resonance imaging. MRI is dedicated to both basic research, technological innovation and applications, providing a single forum for communication among radiologists, physicists, chemists, biochemists, biologists, engineers, internists, pathologists, physiologists, computer scientists, and mathematicians.
期刊最新文献
Preclinical validation of a metasurface-inspired conformal elliptical-cylinder resonator for wrist MRI at 1.5 T. P53 status combined with MRI findings for prognosis prediction of single hepatocellular carcinoma. Predicting progression in triple-negative breast cancer patients undergoing neoadjuvant chemotherapy: Insights from peritumoral radiomics. Deep learning radiomics nomograms predict Isocitrate dehydrogenase (IDH) genotypes in brain glioma: A multicenter study. Reliability of post-contrast deep learning-based highly accelerated cardiac cine MRI for the assessment of ventricular function.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1