CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer.

IF 16.4 1区 化学 Q1 CHEMISTRY, MULTIDISCIPLINARY
Yan Kong, Muchen Xu, Xianding Wei, Danqi Qian, Yuan Yin, Zhaohui Huang, Wenchao Gu, Leyuan Zhou
{"title":"CT imaging-based radiomics signatures improve prognosis prediction in postoperative colorectal cancer.","authors":"Yan Kong, Muchen Xu, Xianding Wei, Danqi Qian, Yuan Yin, Zhaohui Huang, Wenchao Gu, Leyuan Zhou","doi":"10.3233/XST-230090","DOIUrl":null,"url":null,"abstract":"<p><strong>Objective: </strong>To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients.</p><p><strong>Methods: </strong>A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS.</p><p><strong>Results: </strong>In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively.</p><p><strong>Conclusion: </strong>NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.</p>","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3233/XST-230090","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Objective: To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients.

Methods: A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS.

Results: In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively.

Conclusion: NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.

基于CT成像的放射组学特征提高结直肠癌术后预后预测。
目的:探讨非对比增强(NCE)和对比增强(CE) CT放射组学特征(Rad-scores)作为预测结直肠癌(CRC)术后患者总生存期(OS)的预后因素。方法:回顾性分析在我院行手术切除的65例结直肠癌患者作为训练集,从癌症影像档案(the Cancer Imaging Archive, TCIA)检索的19例患者图像作为外部验证集。在训练中,从术前NCE/CE-CT中提取放射组学特征,然后通过5倍交叉验证LASSO Cox法选择并用于构建rad评分。根据rad评分和临床因素建立模型并进行比较。Kaplan-Meier分析也用于比较高风险和低风险拉德评分组之间的生存概率。最后,开发了一个nomogram来预测OS。结果:在训练中,临床模型的C-index为0.796 (95% CI: 0.722-0.870),临床和两个rad评分联合模型的C-index表现最好,为0.821 (95% CI: 0.743-0.899)。此外,具有CE-CT rad评分的模型在训练中的表现略优于NCE-CT模型。对于与CE-CT rad评分相结合的模型,训练集和验证集的c指数分别为0.818 (95% CI: 0.742-0.894)和0.774 (95% CI: 0.556-0.992)。Kaplan-Meier分析显示高危组和低危组的生存率有显著差异。最后,该模型1年、3年和5年生存率的受试者工作特征(ROC)曲线下面积分别为0.904、0.777和0.843。结论:NCE-CT或CE-CT放射组学及临床联合模型可预测结直肠癌患者的OS,建议在有条件时纳入两种rad评分。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Accounts of Chemical Research
Accounts of Chemical Research 化学-化学综合
CiteScore
31.40
自引率
1.10%
发文量
312
审稿时长
2 months
期刊介绍: Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信