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Radiation: Applications

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The average temperature of Earth is the subject of much current discussion. Earth is in radiative contact with both the Sun and dark space; it receives almost all its energy from the radiation of the Sun and reflects some of it into outer space. Dark space is very cold, about 3 K, so Earth radiates energy into it. For instance, heat transfer occurs from soil and grasses, the rate of which can be so rapid that frost can occur on clear summer evenings, even in warm latitudes.
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Diffuse Optical Spectroscopy for the Quantitative Assessment of Acute Ionizing Radiation Induced Skin Toxicity Using a Mouse Model

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Research progress of dosiomics in precision radiotherapy.

Yifan Lei^1,2, Han Bai², Jinhui Yu^1,2

¹Clinical Oncology College of Kunming Medical University, Yunnan Tumor Hospital, Kunming, Yunnan, China.

Journal of Cancer Research and Therapeutics

|September 4, 2025

Summary

Machine learning models analyze radiotherapy dose data (dosiomics) to predict treatment side effects and patient outcomes. This approach enhances personalized radiotherapy by revealing dose-response relationships for improved tumor treatment.

Keywords:

Dosimetry dosiomics machine learning prediction model radiotherapy

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Area of Science:

Oncology
Medical Physics
Artificial Intelligence in Medicine

Background:

Radiotherapy is a cornerstone of cancer treatment but causes side effects impacting prognosis.
Predicting and mitigating these side effects is crucial for patient outcomes.
Artificial intelligence (AI) and machine learning (ML) offer new tools for analyzing complex medical data.

Purpose of the Study:

To review the progress of dosiomics in predicting radiotherapy toxicity and prognosis.
To highlight recent advancements in clinical radiotherapy applications.
To discuss the future potential of dosiomics in personalized radiotherapy.

Main Methods:

Utilizing machine learning models to analyze three-dimensional dose distribution maps from radiotherapy plans.
Extracting quantitative features (dosiomics) to understand dose-response relationships.
Correlating dosiomic features with clinical outcomes like toxicity and prognosis.

Main Results:

Dosiomics, combined with ML, shows promise in accurately predicting radiotherapy toxicity.
These models can reveal underlying dose-response relationships for organs and tumors.
The findings support the foundation for developing personalized radiotherapy strategies.

Conclusions:

Dosiomics is a valuable tool for predicting radiotherapy outcomes.
Machine learning enhances the predictive power of dosiomic features.
Further research in dosiomics will drive personalized radiotherapy advancements.