Loading...
A CT radiomics signature enables risk stratification and survival prediction in colorectal liver metastases
Kha, Quang-Hien Nguyen, Phat Ky Le, Minh Huu Nhat Huynh, Han Hong Huynh, Phat Kim Nguyen, Dang Le Nguyen, Hoang Le, Huan Viet Le, Trang Diep Thanh Le, Hoai Huu
Kha, Quang-Hien
Nguyen, Phat Ky
Le, Minh Huu Nhat
Huynh, Han Hong
Huynh, Phat Kim
Nguyen, Dang
Le Nguyen, Hoang
Le, Huan Viet
Le, Trang Diep Thanh
Le, Hoai Huu
Files
Supervisor
Department
Computer Vision
Embargo End Date
Type
Journal article
Date
License
http://creativecommons.org/licenses/by/4.0/
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Objective. Colorectal liver metastases (CRLM) represent a major clinical challenge because outcomes after hepatic resection vary widely between patients. Preoperative risk stratification remains limited, and radiomics may provide non invasive imaging biomarkers to support prognosis. This study aimed to develop a CT based radiomics signature capable of generating risk scores for predicting overall survival (OS) in patients undergoing CRLM resection.<i>Approach.</i>Preoperative CT scans from 197 CRLM patients were retrospectively obtained from The Cancer Imaging Archive. A total of 851 radiomics features were extracted using 3D Slicer, along with 256 deep features from a 3D convolutional neural network. Data were randomly divided into training (70%) and testing (30%) sets. Feature selection included correlation-based filtering, univariate Cox regression, and multivariate Cox regression with least absolute shrinkage and selection operator (LASSO) regularization. A radiomics-based risk score (Rad score) was calculated to stratify patients into high- and low- risk groups using the median value. Model performance was compared with clinical variables using time-dependent receiver operating characteristic analysis.<i>Main results.</i>The eight feature signature was prognostic in both internal splits. In multivariable Cox models, the Rad score remained an independent predictor of OS in the training cohort (HR = 2.671, 95% CI 2.023-3.527,<i>P</i>-value < 0.001; Harrell C index = 0.738, 95% CI 0.678-0.793) and the testing cohort (HR = 3.036, 95% CI 1.421-6.486,<i>P</i>-value = 0.004; Harrell C index = 0.663, 95% CI 0.558-0.752). Kaplan Meier analysis showed shorter survival in the high risk group than the low risk group in training (median OS 56.5 versus 76.00 months; log rank<i>P</i>-value = 0.0000) and testing (61.45 versus 74.25 months; log rank<i>P</i>-value = 0.0103). In an external cohort of 105 patients, the Rad score also separated risk (Harrell C index = 0.614, 95% CI 0.534-0.697; median OS 15.7 versus 29.87 months; log rank<i>P</i>-value = 0.0015).<i>Significance.</i>A compact CT radiomics signature derived from preoperative imaging provided independent prognostic information for OS and enabled risk stratification in internal testing and external validation. Further validation in independent CRLM cohorts is required before clinical deployment.
Citation
Q.-H. Kha, P.K. Nguyen, M.H.N. Le, H.H. Huynh, P.K. Huynh, D. Nguyen , et al., "A CT radiomics signature enables risk stratification and survival prediction in colorectal liver metastases," Physics in Medicine and Biology, vol. 71, no. 5, pp. 055012-055012, 2026, https://doi.org/10.1088/1361-6560/ae4c14.
Source
Physics in Medicine and Biology
Conference
Keywords
51 Physical Sciences, 5105 Medical and Biological Physics, Aged, Colorectal Neoplasms, Female, Humans, Image Processing, Computer-Assisted, Liver Neoplasms, Male, Middle Aged, Prognosis, Radiomics, Retrospective Studies, Risk Assessment, Survival Analysis, Tomography, X-Ray Computed, 3 Good Health and Well Being
Subjects
Source
Publisher
IOP Publishing