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Related Experiment Video

Updated: Mar 18, 2026

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies

Published on: February 6, 2019

Big Data Analytics for Prostate Radiotherapy.

James Coates¹, Luis Souhami², Issam El Naqa³

¹Department of Oncology, University of Oxford , Oxford , UK.

Frontiers in Oncology

|July 6, 2016

Summary

This summary is machine-generated.

Personalized risk profiles using big data can improve radiation therapy for prostate cancer. This approach balances tumor control with minimizing normal tissue damage, enhancing patient outcomes.

Keywords:

big data data mining machine learning radiotherapy systems radiobiology

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Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

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Published on: June 10, 2025

Area of Science:

Radiation oncology
Medical physics
Data science

Background:

Radiation therapy is a primary treatment for localized prostate cancer.
Radiation-induced normal tissue damage limits treatment efficacy.
Balancing tumor control and normal tissue sparing is crucial.

Purpose of the Study:

To review outcome modeling and big data-mining techniques for radiotherapy.
To develop personalized, data-driven risk profiles for treatment outcomes.
To guide clinicians and patients in treatment decisions.

Main Methods:

Harvesting and integrating heterogeneous data (clinical, dose-volume, biological).
Utilizing a multi-dimensional space ('RadoncSpace') for predictor identification.
Applying modeling approaches to hypofractionated prostate cancer patient cohort.
Employing cross-validation for framework refinement and model performance assessment.

Main Results:

Demonstrated the application of various modeling techniques on a prostate cancer cohort.
Incorporated diverse data types and parameters for comprehensive analysis.
Reviewed cross-validation techniques for model robustness.

Conclusions:

Big data analytics, including machine learning and AI, offer advanced modeling techniques.
Systems radiobiology approaches hold future potential for optimizing radiotherapy.
Personalized risk profiling enhances treatment planning and patient care.