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Prognostic factors
Gianfranco Buccheri1, Domenico Ferrigno
1Cuneo Lung Cancer Study Group, Divisione di Pneumologia, Ospedale "A Carle," Azienda Ospedaliera "S. Croce e Carle," Cuneo I-12100, Italy. buccheri@culcasg.org
Hematology/Oncology Clinics of North America
|March 10, 2004
Summary
Current disease prediction models explain less than half of patient variability. Continued discovery of novel prognostic factors, including molecular markers and mental health, will improve outcome prediction accuracy.
Area of Science:
- Oncology
- Medical Prognostics
- Patient Outcomes
Background:
- Current predictive models for disease outcomes are limited, explaining only up to 50% of natural variability.
- Sophisticated analyses and numerous variables have not fully elucidated patient prognoses.
Purpose of the Study:
- To highlight the limitations of existing predictive models in disease outcome assessment.
- To emphasize the ongoing need for discovering new prognostic factors to enhance prediction accuracy.
Main Methods:
- Review of current literature on prognostic factors in disease prediction.
- Identification of established and emerging prognostic factor categories.
- Discussion of the impact of novel factors on predictive model reliability.
Main Results:
- Existing models account for less than 50% of disease variability.
- Established factors include tumor neoangiogenesis and quality of life.
- Emerging factors encompass molecular genetic markers, coagulation/fibrinolysis, and mental depression.
Conclusions:
- The discovery of novel prognostic factors is crucial for improving patient outcome prediction.
- The universe of prognostic factors is vast, with much yet to be explored.
- Future research will likely identify additional factors, such as mental depression, significantly impacting prognoses.