Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Aug 15, 2025; 17(8): 108362
Published online Aug 15, 2025. doi: 10.4251/wjgo.v17.i8.108362
Noninvasive prediction of microsatellite instability in stage II/III rectal cancer using dynamic contrast-enhanced magnetic resonance imaging radiomics
Chao-Yang Zheng, Jia-Min Zhang, Qian-Sen Lin, Tao Lian, Liang-Pan Shi, Jie-Yun Chen, Ya-Li Cai
Chao-Yang Zheng, Jia-Min Zhang, Qian-Sen Lin, Tao Lian, Jie-Yun Chen, Ya-Li Cai, Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, Fujian Province, China
Liang-Pan Shi, Department of Gastrointestinal Surgery, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou 362000, Fujian Province, China
Co-first authors: Chao-Yang Zheng and Jia-Min Zhang.
Co-corresponding authors: Jie-Yun Chen and Ya-Li Cai.
Author contributions: Zheng CY and Zhang JM contributed to study conception and design, data collection and analysis, radiomics feature extraction, statistical analysis, manuscript drafting; Chen JY and Cai YL contributed to study supervision, methodology guidance, manuscript revision, and final approval; Lin QS and Lian T contributed to magnetic resonance imaging image acquisition and quality control, radiomics analysis, data interpretation; Shi LP contributed to patient recruitment, clinical data collection, pathological correlation, and clinical interpretation; All authors contributed to manuscript review and approved the final version.
Supported by the Natural Science Foundation of Fujian Province, China, No. 2022J011460.
Institutional review board statement: This study was approved by the Ethics Committee of Quanzhou First Hospital (No. QYLL2022242).
Informed consent statement: Patient consent was waived due to the retrospective nature of the study and the use of anonymized clinical data.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ya-Li Cai, Deputy Director, Department of Radiology, Quanzhou First Hospital Affiliated to Fujian Medical University, No. 1028 Anji Road, Fengze District, Quanzhou 362000, Fujian Province, China. a13178082309@163.com
Received: May 9, 2025
Revised: June 16, 2025
Accepted: July 16, 2025
Published online: August 15, 2025
Processing time: 96 Days and 16.6 Hours
Abstract
BACKGROUND

Colorectal cancer stands among the most prevalent digestive system malignancies. The microsatellite instability (MSI) profile plays a crucial role in determining patient outcomes and therapy responsiveness. Traditional MSI evaluation methods require invasive tissue sampling, are lengthy, and can be compromised by intratumoral heterogeneity.

AIM

To establish a non-invasive technique utilizing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and machine learning algorithms to determine MSI status in patients with intermediate-stage rectal cancer.

METHODS

This retrospective analysis examined 120 individuals diagnosed with stage II/III rectal cancer [30 MSI-high (MSI-H) and 90 microsatellite stability (MSS)/MSI-low (MSI-L) cases]. We extracted comprehensive radiomics signatures from DCE-MRI scans, encompassing textural parameters that reflect tumor heterogeneity, shape-based metrics, and histogram-derived statistical values. Least absolute shrinkage and selection operator regression facilitated feature selection, while predictive frameworks were developed using various classification algorithms (logistic regression, support vector machine, and random forest). Performance assessment utilized separate training and validation cohorts.

RESULTS

Our investigation uncovered distinctive imaging characteristics between MSI-H and MSS/MSI-L neoplasms. MSI-H tumors exhibited significantly elevated entropy values (7.84 ± 0.92 vs 6.39 ± 0.83, P = 0.004), enhanced surface-to-volume proportions (0.72 ± 0.14 vs 0.58 ± 0.11, P = 0.008), and heightened signal intensity variation (3642 ± 782 vs 2815 ± 645, P = 0.007). The random forest model demonstrated superior classification capability with area under the curves (AUCs) of 0.891 and 0.896 across training and validation datasets, respectively. An integrated approach combining radiomics with clinical parameters further enhanced performance metrics (AUC 0.923 and 0.914), achieving 88.5% sensitivity alongside 87.2% specificity.

CONCLUSION

DCE-MRI radiomics features interpreted through machine learning frameworks offer an effective strategy for MSI status assessment in intermediate-stage rectal cancer.

Keywords: Dynamic contrast-enhanced magnetic resonance imaging; Radiomics; Machine learning; Rectal cancer; Microsatellite instability

Core Tip: This study proposes a novel non-invasive approach to assess microsatellite instability (MSI) status in stage II/III rectal cancer using dynamic contrast-enhanced magnetic resonance imaging-based radiomics combined with machine learning models. Distinct image omics features were identified between MSI-high and microsatellite stable/low tumors. The integrated clinic-radiomics model achieved superior diagnostic performance, providing a potential alternative to traditional invasive methods. This approach may improve early stratification and treatment decision-making for rectal cancer patients.