应用协调正电子发射断层扫描放射学特征对治愈性切除胰腺癌的预后分析。

IF 1.7 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Journal of Hybrid Imaging Pub Date : 2023-03-06 DOI:10.1186/s41824-023-00163-8
Masao Watanabe, Ryo Ashida, Chisato Miyakoshi, Shigeki Arizono, Tsuyoshi Suga, Shotaro Kanao, Koji Kitamura, Takahisa Ogawa, Reiichi Ishikura
{"title":"应用协调正电子发射断层扫描放射学特征对治愈性切除胰腺癌的预后分析。","authors":"Masao Watanabe,&nbsp;Ryo Ashida,&nbsp;Chisato Miyakoshi,&nbsp;Shigeki Arizono,&nbsp;Tsuyoshi Suga,&nbsp;Shotaro Kanao,&nbsp;Koji Kitamura,&nbsp;Takahisa Ogawa,&nbsp;Reiichi Ishikura","doi":"10.1186/s41824-023-00163-8","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Texture features reflecting tumour heterogeneity enable us to investigate prognostic factors. The R package ComBat can harmonize the quantitative texture features among several positron emission tomography (PET) scanners. We aimed to identify prognostic factors among harmonized PET radiomic features and clinical information from pancreatic cancer patients who underwent curative surgery.</p><p><strong>Methods: </strong>Fifty-eight patients underwent preoperative enhanced dynamic computed tomography (CT) scanning and fluorodeoxyglucose PET/CT using four PET scanners. Using LIFEx software, we measured PET radiomic parameters including texture features with higher order and harmonized these PET parameters. For progression-free survival (PFS) and overall survival (OS), we evaluated clinical information, including age, TNM stage, and neural invasion, and the harmonized PET radiomic features based on univariate Cox proportional hazard regression. Next, we analysed the prognostic indices by multivariate Cox proportional hazard regression (1) by using either significant (p < 0.05) or borderline significant (p = 0.05-0.10) indices in the univariate analysis (first multivariate analysis) or (2) by using the selected features with random forest algorithms (second multivariate analysis). Finally, we checked these multivariate results by log-rank test.</p><p><strong>Results: </strong>Regarding the first multivariate analysis for PFS after univariate analysis, age was the significant prognostic factor (p = 0.020), and MTV and GLCM contrast were borderline significant (p = 0.051 and 0.075, respectively). Regarding the first multivariate analysis of OS, neural invasion, Shape sphericity and GLZLM LZLGE were significant (p = 0.019, 0.042 and 0.0076). In the second multivariate analysis, only MTV was significant (p = 0.046) for PFS, whereas GLZLM LZLGE was significant (p = 0.047), and Shape sphericity was borderline significant (p = 0.088) for OS. In the log-rank test, age, MTV and GLCM contrast were borderline significant for PFS (p = 0.08, 0.06 and 0.07, respectively), whereas neural invasion and Shape sphericity were significant (p = 0.03 and 0.04, respectively), and GLZLM LZLGE was borderline significant for OS (p = 0.08).</p><p><strong>Conclusions: </strong>Other than the clinical factors, MTV and GLCM contrast for PFS and Shape sphericity and GLZLM LZLGE for OS may be prognostic PET parameters. A prospective multicentre study with a larger sample size may be warranted.</p>","PeriodicalId":36160,"journal":{"name":"European Journal of Hybrid Imaging","volume":"7 1","pages":"5"},"PeriodicalIF":1.7000,"publicationDate":"2023-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986192/pdf/","citationCount":"0","resultStr":"{\"title\":\"Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features.\",\"authors\":\"Masao Watanabe,&nbsp;Ryo Ashida,&nbsp;Chisato Miyakoshi,&nbsp;Shigeki Arizono,&nbsp;Tsuyoshi Suga,&nbsp;Shotaro Kanao,&nbsp;Koji Kitamura,&nbsp;Takahisa Ogawa,&nbsp;Reiichi Ishikura\",\"doi\":\"10.1186/s41824-023-00163-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Texture features reflecting tumour heterogeneity enable us to investigate prognostic factors. The R package ComBat can harmonize the quantitative texture features among several positron emission tomography (PET) scanners. We aimed to identify prognostic factors among harmonized PET radiomic features and clinical information from pancreatic cancer patients who underwent curative surgery.</p><p><strong>Methods: </strong>Fifty-eight patients underwent preoperative enhanced dynamic computed tomography (CT) scanning and fluorodeoxyglucose PET/CT using four PET scanners. Using LIFEx software, we measured PET radiomic parameters including texture features with higher order and harmonized these PET parameters. For progression-free survival (PFS) and overall survival (OS), we evaluated clinical information, including age, TNM stage, and neural invasion, and the harmonized PET radiomic features based on univariate Cox proportional hazard regression. Next, we analysed the prognostic indices by multivariate Cox proportional hazard regression (1) by using either significant (p < 0.05) or borderline significant (p = 0.05-0.10) indices in the univariate analysis (first multivariate analysis) or (2) by using the selected features with random forest algorithms (second multivariate analysis). Finally, we checked these multivariate results by log-rank test.</p><p><strong>Results: </strong>Regarding the first multivariate analysis for PFS after univariate analysis, age was the significant prognostic factor (p = 0.020), and MTV and GLCM contrast were borderline significant (p = 0.051 and 0.075, respectively). Regarding the first multivariate analysis of OS, neural invasion, Shape sphericity and GLZLM LZLGE were significant (p = 0.019, 0.042 and 0.0076). In the second multivariate analysis, only MTV was significant (p = 0.046) for PFS, whereas GLZLM LZLGE was significant (p = 0.047), and Shape sphericity was borderline significant (p = 0.088) for OS. In the log-rank test, age, MTV and GLCM contrast were borderline significant for PFS (p = 0.08, 0.06 and 0.07, respectively), whereas neural invasion and Shape sphericity were significant (p = 0.03 and 0.04, respectively), and GLZLM LZLGE was borderline significant for OS (p = 0.08).</p><p><strong>Conclusions: </strong>Other than the clinical factors, MTV and GLCM contrast for PFS and Shape sphericity and GLZLM LZLGE for OS may be prognostic PET parameters. A prospective multicentre study with a larger sample size may be warranted.</p>\",\"PeriodicalId\":36160,\"journal\":{\"name\":\"European Journal of Hybrid Imaging\",\"volume\":\"7 1\",\"pages\":\"5\"},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2023-03-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986192/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Journal of Hybrid Imaging\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s41824-023-00163-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Journal of Hybrid Imaging","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41824-023-00163-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
引用次数: 0

摘要

背景:反映肿瘤异质性的质地特征使我们能够研究预后因素。R包ComBat可以协调多个正电子发射断层扫描(PET)扫描仪之间的定量纹理特征。我们的目的是在接受治疗性手术的胰腺癌患者的PET放射学特征和临床信息中确定预后因素。方法:58例患者术前使用4台PET扫描仪进行增强动态计算机断层扫描(CT)和氟脱氧葡萄糖PET/CT。利用LIFEx软件测量了包括高阶纹理特征在内的PET放射学参数,并对这些PET参数进行了协调。对于无进展生存期(PFS)和总生存期(OS),我们评估了临床信息,包括年龄、TNM分期和神经侵犯,以及基于单变量Cox比例风险回归的统一PET放射学特征。结果:在单因素分析后的第一次多因素分析中,年龄是PFS的显著预后因素(p = 0.020), MTV和GLCM对比具有临界显著性(p分别= 0.051和0.075)。第一次多变量分析中,神经侵犯、形状球度和GLZLM、LZLGE差异有统计学意义(p = 0.019、0.042和0.0076)。在第二个多变量分析中,只有MTV对PFS有显著性意义(p = 0.046),而GLZLM和LZLGE对OS有显著性意义(p = 0.047), Shape sphericity有边缘性意义(p = 0.088)。在log-rank检验中,年龄、MTV和GLCM对比在PFS中有显著性差异(p分别为0.08、0.06和0.07),而神经侵袭和形状球形度在OS中有显著性差异(p分别为0.03和0.04),GLZLM和LZLGE在OS中有显著性差异(p = 0.08)。结论:除临床因素外,PFS的MTV和GLCM对比,OS的GLZLM和LZLGE可能是预后的PET参数。可能需要更大样本量的前瞻性多中心研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

摘要图片

摘要图片

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
Prognostic analysis of curatively resected pancreatic cancer using harmonized positron emission tomography radiomic features.

Background: Texture features reflecting tumour heterogeneity enable us to investigate prognostic factors. The R package ComBat can harmonize the quantitative texture features among several positron emission tomography (PET) scanners. We aimed to identify prognostic factors among harmonized PET radiomic features and clinical information from pancreatic cancer patients who underwent curative surgery.

Methods: Fifty-eight patients underwent preoperative enhanced dynamic computed tomography (CT) scanning and fluorodeoxyglucose PET/CT using four PET scanners. Using LIFEx software, we measured PET radiomic parameters including texture features with higher order and harmonized these PET parameters. For progression-free survival (PFS) and overall survival (OS), we evaluated clinical information, including age, TNM stage, and neural invasion, and the harmonized PET radiomic features based on univariate Cox proportional hazard regression. Next, we analysed the prognostic indices by multivariate Cox proportional hazard regression (1) by using either significant (p < 0.05) or borderline significant (p = 0.05-0.10) indices in the univariate analysis (first multivariate analysis) or (2) by using the selected features with random forest algorithms (second multivariate analysis). Finally, we checked these multivariate results by log-rank test.

Results: Regarding the first multivariate analysis for PFS after univariate analysis, age was the significant prognostic factor (p = 0.020), and MTV and GLCM contrast were borderline significant (p = 0.051 and 0.075, respectively). Regarding the first multivariate analysis of OS, neural invasion, Shape sphericity and GLZLM LZLGE were significant (p = 0.019, 0.042 and 0.0076). In the second multivariate analysis, only MTV was significant (p = 0.046) for PFS, whereas GLZLM LZLGE was significant (p = 0.047), and Shape sphericity was borderline significant (p = 0.088) for OS. In the log-rank test, age, MTV and GLCM contrast were borderline significant for PFS (p = 0.08, 0.06 and 0.07, respectively), whereas neural invasion and Shape sphericity were significant (p = 0.03 and 0.04, respectively), and GLZLM LZLGE was borderline significant for OS (p = 0.08).

Conclusions: Other than the clinical factors, MTV and GLCM contrast for PFS and Shape sphericity and GLZLM LZLGE for OS may be prognostic PET parameters. A prospective multicentre study with a larger sample size may be warranted.

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
European Journal of Hybrid Imaging
European Journal of Hybrid Imaging Computer Science-Computer Science (miscellaneous)
CiteScore
3.40
自引率
0.00%
发文量
29
审稿时长
17 weeks
期刊最新文献
Phosphaturic mesenchymal tumor demonstrated by 68Ga-DOTATATE PET/CT in a patient: a case report Carcinoid crisis in Lutetium-177-Dotatate therapy of neuroendocrine tumors: an overview of pathophysiology, risk factors, recognition, and treatment Four-dimensional computed tomography as first-line imaging in primary hyperparathyroidism, a retrospective comparison to conventional imaging in a predominantly single adenoma population Clinical value of semi-quantitative parameters in 68Ga-DOTANOC PET/CT in treatment and diagnostics of cranial meningioma in a single-center retrospective analysis Cardiac transplant rejection assessment with 18F-FDG PET-CT: initial single-centre experience for diagnosis and management
×
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