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Related Concept Videos

Dosage Regimen Designs: Nomograms and Tabulations01:23

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Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
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Using machine learning to construct nomograms for patients with metastatic colon cancer.

B Zhao1, R A Gabriel2, F Vaida3

  • 1Department of Surgery, University of California San Diego, La Jolla, California, USA.

Colorectal Disease : the Official Journal of the Association of Coloproctology of Great Britain and Ireland
|January 29, 2020
PubMed
Summary

Machine learning models accurately predict overall survival (OS) for patients with metastatic colon cancer. These models, presented as nomograms, aid clinicians and patients in shared decision-making for cancer care.

Keywords:
NCDBcolon cancermachine learningmetastasisnomogram

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Area of Science:

  • Oncology
  • Data Science
  • Biostatistics

Background:

  • Predicting overall survival (OS) for patients with synchronous colon cancer metastases is challenging due to high variability.
  • Accurate predictive models are needed to guide clinical decision-making and patient care.

Purpose of the Study:

  • To develop and validate machine learning-based nomograms for predicting 3-year OS in patients with right colon (RC) and left colon (LC) cancer and synchronous metastases.
  • To improve the accuracy of OS prediction in this patient population.

Main Methods:

  • Utilized the National Cancer Database (2010-2014) including 11,018 RC and 8,346 LC patients with synchronous metastases.
  • Developed nomograms using Cox proportional hazard regression with lasso regression, splitting data into training and testing sets.
  • Evaluated model calibration and validation using concordance index (c-index).

Main Results:

  • Nomograms demonstrated good predictive accuracy, with predicted OS within 95% confidence intervals of observed OS in four out of five risk groups for both RC and LC models.
  • Externally validated 3-year c-indexes were 0.794 for RC models and 0.761 for LC models.
  • Machine learning models showed improved accuracy in predicting OS compared to traditional methods.

Conclusions:

  • Machine learning-driven nomograms provide more accurate OS predictions for patients with metastatic colon cancer.
  • These nomograms can serve as valuable tools for shared decision-making between clinicians and patients regarding cancer treatment.
  • The study highlights the potential of leveraging big data and machine learning in precision oncology.