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Related Experiment Video

Updated: May 6, 2026

Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones

Advanced Animal Model of Colorectal Metastasis in Liver: Imaging Techniques and Properties of Metastatic Clones

Published on: November 30, 2016

Comparison of Radiomic Features from Different MRI Sequences for Predicting Synchronous Liver Metastases after Rectal

Apekshya Singh¹, Sheng-Ming Shi¹, Han Liu¹

¹Department of Magnetic Resonance Imaging, The Second Affiliated Hospital, Harbin Medical University, Harbin 150086, Heilongjiang Province, China.

Current Medical Imaging

|December 2, 2025

Summary

Radiomics and Random Forest models show promise for detecting synchronous liver metastases in rectal cancer patients. This approach aids in early detection and personalized treatment planning.

Keywords:

Diffusion-weighted imaging ITK-SNAP.LASSO Machine learning Predictive model Radiomics Random forest Rectal cancer Synchronous liver metastasis T2-weighted imaging

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

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Published on: August 16, 2020

Area of Science:

Oncology
Radiology
Medical Imaging

Background:

Synchronous liver metastases (SLM) significantly impact rectal cancer prognosis.
Accurate preoperative detection of SLM is crucial for effective treatment planning.
This study evaluates radiomic features from MRI for SLM detection.

Purpose of the Study:

To compare the predictive performance of radiomic features from T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) MRI.
To develop and evaluate machine learning models for early SLM detection in rectal cancer.

Main Methods:

Retrospective analysis of 137 rectal cancer patients (71 with SLM, 66 without).
Extraction of 3,452 radiomic features from T2WI and DWI using Pyradiomics.
Development of predictive models using Logistic Regression (LR), Support Vector Machine (SVM), and Random Forest (RF) with LASSO feature selection.

Main Results:

Random Forest (RF) models outperformed LR and SVM.
The optimal RF model achieved an AUC of 0.82 and an accuracy of 0.71 for SLM detection.
Combined-Optimal RF model also showed strong performance (AUC = 0.76).

Conclusions:

Radiomics combined with RF models offer a promising non-invasive tool for early SLM detection and risk stratification.
This approach can improve clinical decision-making and guide individualized treatment for rectal cancer patients.
The optimal feature set-based predictive model using RF demonstrated superior accuracy and balanced diagnostic performance.