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Development of a model to predict breast cancer survival using data from the National Cancer Data Base. | LitMetric

Development of a model to predict breast cancer survival using data from the National Cancer Data Base.

Surgery

Cancer Programs, American College of Surgeons, Chicago, IL; Northwestern Institute for Comparative Effectiveness Research in Oncology (NICER-Onc) and Surgical Outcomes and Quality Improvement Center (SOQIC), Feinberg School of Medicine, Northwestern University, Chicago, IL; Division of Research and Optimal Patient Care, American College of Surgeons, Chicago, IL.

Published: February 2016

AI Article Synopsis

  • Researchers developed a survival prediction model for breast cancer using data from the National Cancer Data Base, aiming to support clinicians in discussing patient prognosis.
  • * The study analyzed a large group of 296,284 breast cancer patients, employing a multivariable Cox model to ensure accurate survival predictions.
  • * The resulting model showed strong predictive performance, indicating it could serve as a prototype for a new tool by the Commission on Cancer to provide personalized long-term survival estimates for patients.

Article Abstract

Background: With the large amounts of data on patient, tumor, and treatment factors available to clinicians, it has become critically important to harness this information to guide clinicians in discussing a patient's prognosis. However, no widely accepted survival calculator is available that uses national data and includes multiple prognostic factors. Our objective was to develop a model for predicting survival among patients diagnosed with breast cancer using the National Cancer Data Base (NCDB) to serve as a prototype for the Commission on Cancer's "Cancer Survival Prognostic Calculator."

Patients And Methods: A retrospective cohort of patients diagnosed with breast cancer (2003-2006) in the NCDB was included. A multivariable Cox proportional hazards regression model to predict overall survival was developed. Model discrimination by 10-fold internal cross-validation and calibration was assessed.

Results: There were 296,284 patients for model development and internal validation. The c-index for the 10-fold cross-validation ranged from 0.779 to 0.788 after inclusion of all available pertinent prognostic factors. A plot of the observed versus predicted 5 year overall survival showed minimal deviation from the reference line.

Conclusion: This breast cancer survival prognostic model to be used as a prototype for building the Commission on Cancer's "Cancer Survival Prognostic Calculator" will offer patients and clinicians an objective opportunity to estimate personalized long-term survival based on patient demographic characteristics, tumor factors, and treatment delivered.

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Source
http://dx.doi.org/10.1016/j.surg.2015.08.006DOI Listing

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