ORIGINAL RESEARCH article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1662354
This article is part of the Research TopicPrecision Medical Imaging for Cancer Diagnosis and Treatment Volume IIIView all articles
Predicting the recurrence risk of liver metastasis from colorectal cancer: a study based on preoperative CT intratumoral and peritumoral radiomics features Author Names
Provisionally accepted
- The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Objective: This study aims to explore the value of predicting the recurrence risk of colorectal cancer liver metastasis (CRLM) based on preoperative CT intratumoral and peritumoral radiomics features.Methods: This study utilized retrospectively collected preoperative CT data of 201 CRLM patients, comprising 145 cases from the hospital one and 56 cases from an external hospital two. Liver metastases were precisely segmented via manual annotation. Subsequently, the intratumoral region of interest (ROIIntra) was isotropically dilated to radii of 2 mm, 4 mm, and 6 mm, resulting in peri-tumoral ROIs (ROIPeri2mm, ROIPeri4mm and ROIPeri6mm). We established the prediction models based on support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms. The area under the subject operating characteristic curve (AUC) was used to evaluate the predictive performance.: Compared with SVM and RF, MLP demonstrated superior predictive performance for estimating the recurrence risk of CRLM patients. The best radiomics signatures for predicting the recurrence risk of CRLM were ROIIntra+Peri4mm model, and the AUCs of the ROIIntra model, ROIIntra+Peri2mm model, ROIIntra+Peri4mm model, and ROIIntra+Peri6mm model constructed by MLP are 0.758 (95% confidence interval (CI), 0.621 -0.865), 0.815 (95% CI, 0.684 -0.908), 0.855 (95% CI, 0.731 -0.936), and 0.825 (95% CI, 0.696 -0.915), respectively, in the external test set. Conclusion: Preoperative CT-based radiomics features extracted from intra-tumoral (ROIIntra) and peritumoral (ROIIntra+Peri2mm, ROIIntra+Peri4mm, and ROIIntra+Peri6mm) regions can effectively predict recurrence risk in CRLM patients.
Keywords: computed tomography, Radiomics, Colorectal Neoplasms, Liver, Neoplasm Metastasis
Received: 09 Jul 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Zhang, Li, Wei, Li, Xue and Guo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dongying Zhang, The First Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.