Prediction rules for aggressive prostate cancer in AS population
- Abbreviated Name
- PCA3 Sanda-PHI
- Lead Investigator
- — Emory University
- Coordinating Investigator
- Zheng, Yingye — Fred Hutchinson Cancer Center
- Involved Investigators
Abstract
No abstract availalbe.
Aims
In the development phase, we plan to formulate a number of rules: The first one is to linearly combine PCA3, phi, and clinical variables via logistic regression. The clinical variables include age, BMI, race, DRE result, initial biopsy indicator, number of previous negative biopsies after diagnosis, cores ratio (ratio of biopsy cores containing cancer to total cores), months since diagnosis, and prostate volume. Appropriate variable selection will be implemented in the logistic regression to screen out non-informative clinical variables. The second rule is to linearly combine PCA3, phi, and initial biopsy indicator only, without other clinical variables. Obviously, this rule is expected to have less predictive power than the first one, but can be more practical if the predictive power reduction is limited. The third rule is a “or” logic combination of PCA3, phi, and a composite score of clinical variables. The composite score is obtained via logistic regression. The fourth rule is a “or” logic combination of PCA3 and phi only.
Analytic Method
No analytic method available.
Publications
- No publications available at this time for this protocol.
Biomarkers
Data Collections
- No data collections available at this time for this protocol.
- Start Date
- May 1 2018
- Estimated Finish Date
- May 1 2019
- Protocol ID
- 453
- Protocol Type
- Reference Set
- Fields of Research
-
- Proteomics
- Collaborative Group
- Prostate and Urologic Cancers Research Group
- Cancer Types
-
- Malignant neoplasm of prostate
- Phased Status
- 2