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Proton Therapy Delivery and Its Clinical Application in Select Solid Tumor Malignancies
Published on: February 6, 2019
Predictive models in external beam radiotherapy for clinically localized prostate cancer
Mack Roach1, Fred Waldman, Alan Pollack
1Department of Radiation Oncology, University of California at San Francisco, San Francisco, California, USA. mroach@radonc.ucsf.edu
Cancer
|June 23, 2009
Summary
Predictive models for prostate cancer are improving by incorporating biomarkers alongside traditional data like PSA levels. Future models combining these elements will enhance outcome prediction and treatment selection for better patient care.
Area of Science:
- Oncology
- Radiotherapy
- Biomarker Research
Background:
- Predictive models for prostate cancer commonly use Gleason score, tumor classification, and PSA levels.
- Advanced models include treatment variables like radiation dose and androgen-deprivation therapy.
- Traditionally, models focused on PSA recurrence, but clinical endpoints are increasingly used.
Purpose of the Study:
- To review the development and incorporation of biomarkers into predictive models for prostate cancer.
- To assess the potential of novel biomarkers to improve treatment outcome prediction.
Main Methods:
- Analysis of data from Radiation Therapy Oncology Group (RTOG) phase 3 trials (RTOG 8610 and 9202).
- Preliminary assessment of various biomarkers including p53, DNA ploidy, p16, Ki-67, Bcl-2, and others.
- Evaluation of pretreatment and treatment-related variables.
Main Results:
- Several biomarkers (p53, DNA ploidy, p16, Ki-67, Bcl-2, etc.) have undergone preliminary assessment.
- Current models are not yet ready for routine clinical use.
- Biomarkers show promise for strengthening predictive models.
Conclusions:
- Future predictive models will likely integrate biomarkers with traditional variables.
- This integration is expected to improve the accuracy of outcome prediction.
- Enhanced prediction will aid in selecting optimal treatments for prostate cancer patients.
