Computed Tomography-Based Habitat Analysis for Prognostic Stratification in Colorectal Liver Metastases

Chaoqun Zhou, Hao Xin, Lihua Qian, Yong Zhang, Jing Wang, Junpeng Luo
{"title":"Computed Tomography-Based Habitat Analysis for Prognostic Stratification in Colorectal Liver Metastases","authors":"Chaoqun Zhou,&nbsp;Hao Xin,&nbsp;Lihua Qian,&nbsp;Yong Zhang,&nbsp;Jing Wang,&nbsp;Junpeng Luo","doi":"10.1002/cai2.70000","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Colorectal liver metastasis (CRLM) has a poor prognosis, and traditional prognostic models have certain limitations in clinical application. This study aims to evaluate the prognostic value of CT-based habitat analysis in CRLM patients and compare it with existing traditional prognostic models to provide more evidence for individualized treatment of CRLM patients.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This retrospective study included 197 patients with CRLM whose preoperative contrast-enhanced CT images and corresponding DICOM Segmentation Objects (DSOs) were obtained from The Cancer Imaging Archive (TCIA). Tumor regions were segmented, and habitat features representing distinct subregions were extracted. An unsupervised K-means clustering algorithm classified the tumors into two clusters based on their habitat characteristics. Kaplan–Meier analysis was used to evaluate overall survival (OS), disease-free survival (DFS), and liver-specific DFS. The habitat model's predictive performance was compared with the Clinical Risk Score (CRS) and Tumor Burden Score (TBS) using the concordance index (C-index), Integrated Brier Score (IBS), and time-dependent area under the curve (AUC).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The habitat model identified two distinct patient clusters with significant differences in OS, DFS, and liver-specific DFS (<i>p</i> &lt; 0.01). Compared with CRS and TBS, the habitat model demonstrated superior predictive accuracy, particularly for DFS and liver-specific DFS, with higher time-dependent AUC values and improved model calibration (lower IBS).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>CT-based habitat analysis captures spatial tumor heterogeneity and provides enhanced prognostic stratification in CRLM. The method outperforms conventional models and offers potential for more personalized treatment planning.</p>\n </section>\n </div>","PeriodicalId":100212,"journal":{"name":"Cancer Innovation","volume":"4 2","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cai2.70000","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Innovation","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cai2.70000","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background

Colorectal liver metastasis (CRLM) has a poor prognosis, and traditional prognostic models have certain limitations in clinical application. This study aims to evaluate the prognostic value of CT-based habitat analysis in CRLM patients and compare it with existing traditional prognostic models to provide more evidence for individualized treatment of CRLM patients.

Methods

This retrospective study included 197 patients with CRLM whose preoperative contrast-enhanced CT images and corresponding DICOM Segmentation Objects (DSOs) were obtained from The Cancer Imaging Archive (TCIA). Tumor regions were segmented, and habitat features representing distinct subregions were extracted. An unsupervised K-means clustering algorithm classified the tumors into two clusters based on their habitat characteristics. Kaplan–Meier analysis was used to evaluate overall survival (OS), disease-free survival (DFS), and liver-specific DFS. The habitat model's predictive performance was compared with the Clinical Risk Score (CRS) and Tumor Burden Score (TBS) using the concordance index (C-index), Integrated Brier Score (IBS), and time-dependent area under the curve (AUC).

Results

The habitat model identified two distinct patient clusters with significant differences in OS, DFS, and liver-specific DFS (p < 0.01). Compared with CRS and TBS, the habitat model demonstrated superior predictive accuracy, particularly for DFS and liver-specific DFS, with higher time-dependent AUC values and improved model calibration (lower IBS).

Conclusions

CT-based habitat analysis captures spatial tumor heterogeneity and provides enhanced prognostic stratification in CRLM. The method outperforms conventional models and offers potential for more personalized treatment planning.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
0.70
自引率
0.00%
发文量
0
×
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