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

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Radiomics-Driven Tumor Prognosis Prediction Across Imaging Modalities: Advances in Sampling, Feature Selection, and

Mohan Huang¹, Helen K W Law², Shing Yau Tam¹

¹School of Medical and Health Sciences, Tung Wah College, Hong Kong SAR, China.

|October 16, 2025

Summary

Radiomics analysis of medical images offers a powerful, noninvasive method for predicting cancer prognosis. Advances in segmentation and data integration enhance accuracy, but standardization and clinical validation are crucial for widespread adoption.

Keywords:

clinical translation feature selection imaging modality multi-omics prognosis prediction radiomics sampling methods tumor prognosis

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Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

Area of Science:

Oncology
Medical Imaging
Radiomics

Background:

Radiomics provides noninvasive, quantitative tumor analysis from medical images.
It holds significant potential for predicting cancer prognosis across diverse cancer types.

Purpose of the Study:

To review recent advancements in radiomics for cancer prognosis.
To highlight innovative methods and challenges in the field.

Main Methods:

Review of radiomics applications across various imaging modalities (CT, MRI, PET, ultrasound).
Analysis of advanced segmentation techniques (deep learning, multi-regional, adaptive ROI).
Examination of feature selection methods (LASSO, mRMR, ensemble) and multi-omics integration.

Main Results:

Innovative sampling and feature selection methods improve radiomics model performance and robustness.
Integration with multi-omics data enhances predictive accuracy and biological insights.
Despite progress, challenges in reproducibility, standardization, and clinical validation persist.

Conclusions:

Radiomics shows great promise for noninvasive cancer prognosis.
Future efforts must focus on multicenter collaboration, methodological standardization, and clinical translation to realize its full potential.