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Updated: May 22, 2026
06:46
Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Prediction tools in surgical oncology.
Brandon K Isariyawongse1, Michael W Kattan
1Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue/Q-10, Cleveland, OH 44195, USA.
Surgical Oncology Clinics of North America
|May 16, 2012
Summary
Clinical prediction tools like artificial neural networks and nomograms can accurately forecast cancer outcomes. However, more models are needed to predict quality of life, enabling informed patient treatment decisions.
Area of Science:
- Oncology
- Biostatistics
- Health Informatics
Background:
- Artificial neural networks, prediction tables, and clinical nomograms offer concise prognostic information.
- Existing models effectively predict oncologic outcomes such as pathologic stage and survival rates.
- There is a notable lack of predictive models addressing quality of life outcomes in cancer care.
Purpose of the Study:
- To highlight the need for validated prediction tools in oncology.
- To emphasize the importance of integrating quality of life predictions with oncologic outcome predictions.
- To advocate for the development of comparative effectiveness tables for patient decision-making.
Main Methods:
- Review of current literature on prognostic models in oncology.
- Analysis of the capabilities of artificial neural networks, prediction tables, and nomograms.
- Identification of gaps in predicting quality of life outcomes.
Main Results:
- Established models accurately predict various oncologic outcomes.
- A significant gap exists in models predicting quality of life.
- Independent validation of existing tools is crucial.
Conclusions:
- Integrating oncologic and quality of life predictive tools is essential.
- Comparative effectiveness tables can empower patients with cancer.
- Informed, individualized treatment decisions require comprehensive prognostic information.
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