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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review

Yuan Sun^1,2, Bo Li³, Chuanlan Ju⁴

¹Centre for Evidence-based Chinese Medicine, Beijing University of Chinese Medicine, No.11 East Beisanhuan Road, Heping Street, Chaoyang District, Beijing, 100029, China, 8613552999260.

JMIR Medical Informatics

|December 2, 2025

Summary

This summary is machine-generated.

Radiomics-based machine learning models show promise in predicting colorectal cancer (CRC) recurrence risk. Combining radiomics and clinical features offers the best predictive performance, supporting personalized treatment strategies.

Keywords:

PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses clinical prediction model colorectal cancer meta-analysis radiomics

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Area of Science:

Oncology
Medical Imaging
Machine Learning

Background:

Predicting colorectal cancer (CRC) recurrence is challenging.
Radiomics models show potential for forecasting CRC recurrence.
Lack of systematic evidence hinders clinical adoption of radiomics models.

Purpose of the Study:

To explore the value of radiomics in predicting CRC recurrence.
To provide evidence for developing targeted interventions.
To systematically evaluate radiomics-based models for CRC recurrence prediction.

Main Methods:

Meta-analysis of 17 studies (4600 patients) from 4 databases.
Included studies developed/validated radiomics models using CT/MRI for CRC recurrence.
Assessed study quality using Radiomics Quality Score; pooled c-index values using a bivariate mixed-effects model.

Main Results:

Studies had low quality (mean RQS 13.23/36).
Combined radiomics and clinical features achieved the highest c-index values (0.83 in validation, 0.83 in internal validation, 0.83 in external validation).
Radiomics features alone showed improved prediction over clinical features alone (0.80 vs. 0.73 in validation).

Conclusions:

Radiomics models, particularly integrated ones, show significant potential for predicting CRC recurrence.
Limitations include moderate study quality and heterogeneity, necessitating further high-quality prospective research.
Integrated models may enhance risk stratification and personalized treatment for CRC patients.