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Updated: Aug 12, 2026
07:41
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Do we really need prognostic factors for breast cancer?
1Division of Medical Oncology, University of Texas Health Science Center at San Antonio 78284-7884.
Breast Cancer Research and Treatment
|January 1, 1994
Summary
Prognostic factors help optimize breast cancer treatment by identifying patients who may not benefit from adjuvant therapy or require more aggressive approaches. These factors also predict response to specific treatments, improving patient subset discrimination.
Area of Science:
- Oncology
- Medical Statistics
- Clinical Decision Making
Background:
- The widespread use of adjuvant therapy in breast cancer has shifted the role of prognostic factors.
- Accurate prognostic assessment remains crucial for personalized treatment strategies.
- Identifying patient subsets with distinct prognoses is essential for optimizing care.
Purpose of the Study:
- To discuss the utility of prognostic factors in three key clinical scenarios for breast cancer patients.
- To explore cutpoint analyses and validation methods for individual prognostic factors and indexes.
- To enhance the reliable discrimination of patient subsets for tailored therapeutic decisions.
Main Methods:
- Review and discussion of clinical scenarios where prognostic factors are beneficial.
- Analysis of cutpoint determination for prognostic factor significance.
- Validation strategies for individual factors and combined prognostic indexes.
Main Results:
- Prognostic factors can identify patients with excellent prognoses, potentially avoiding unnecessary adjuvant therapy.
- They can also identify patients with poor prognoses, warranting more aggressive adjuvant treatment.
- Factors predict response or resistance to specific therapies, guiding treatment selection.
Conclusions:
- Prognostic factors remain vital for optimizing breast cancer treatment, even with routine adjuvant therapy.
- Their application in identifying patient subsets improves cost-effectiveness and treatment intensity.
- Validated prognostic indexes enhance the reliability of treatment decisions for diverse patient groups.
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