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Title:           Forecasting Survival Rates in Metastatic Colorectal Cancer Patients Undergoing Bevacizumab-Based Chemotherapy: A Machine Learning Approach
Subject:         Background: Antibiotics can play a pivotal role in the treatment of colorectal cancer (CRC) at various stages of the disease, both directly and indirectly. Identifying novel patterns of antibiotic effects or responses in CRC within extensive medical data poses a significant challenge that can be addressed through algorithmic approaches. Machine Learning (ML) emerges as a promising solution for predicting clinical outcomes using clinical and heterogeneous cancer data. In the pursuit of our objective, we employed ML techniques for predicting CRC mortality and antibiotic influence. Methods: We utilized a dataset to examine the accuracy of death prediction in metastatic colorectal cancer. In addition, we analyzed the association between antibiotic exposure and mortality in metastatic colorectal cancer. The dataset comprised 147 patients, nineteen independent variables, and one dependent variable. Our analysis involved testing different classification-supervised ML, including an oversampling pool for classification models, Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machine, Random Forest, XGBboost Classifier, a consensus of all models, and a consensus of top models (meta models). Results: The consensus of the top models' classifier exhibited the highest accuracy among the algorithms tested (93%). This model met the standards for good accuracy, surpassing the 90% threshold considered useful in ML applications. Consistent with the accuracy results, other metrics are also good, including precision (0.96), recall (0.93), F-Beta (0.94), and AUC (0.93). Hazard ratio analysis suggests that there is no discernible difference between patients who received antibiotics and those who did not. Conclusions: Our modelling approach provides an alternative for analyzing and predicting the relationship between antibiotics and mortality in metastatic colorectal cancer patients treated with bevacizumab, complementing classic statistical methods. This methodology lays the groundwork for future use of datasets in cancer treatment research and highlights the advantages of meta models.
Keywords:        machine learning; meta models; mortality prediction; colorectal cancer; antibiotics
Author:          Sergio Sánchez-Herrero, Abtin Tondar, Elena Perez-Bernabeu, Laura Calvet and Angel A. Juan
Creator:         LaTeX with hyperref
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      Title: Introduction
      Destination: section.1
     Item: 
      Title: Materials and Methods
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       Item: 
        Title: Sources of Data
        Destination: subsection.2.1
       Item: 
        Title: Data Processing
        Destination: subsection.2.2
       Item: 
        Title: Software
        Destination: subsection.2.3
       Item: 
        Title: Model Development
        Destination: subsection.2.4
       Item: 
        Title: Model Evaluation
        Destination: subsection.2.5
       Item: 
        Title: Feature Importance and Partial Dependence Plots
        Destination: subsection.2.6
       Item: 
        Title: Risk Stratification Using ML
        Destination: subsection.2.7
     Item: 
      Title: Results
      Destination: section.3
      Children: 
       Item: 
        Title: Descriptive Analysis
        Destination: subsection.3.1
       Item: 
        Title: Model Performance
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       Item: 
        Title: Feature Analysis
        Destination: subsection.3.3
     Item: 
      Title: Discussion
      Destination: section.4
     Item: 
      Title: Conclusions
      Destination: section.5
     Item: 
      Title: Appendix A
      Destination: appendix.A.
     Item: 
      Title: References
      Destination: appendix.B.
   Info: 
    Title: Forecasting Survival Rates in Metastatic Colorectal Cancer Patients Undergoing Bevacizumab-Based Chemotherapy: A Machine Learning Approach
    Author: Sergio Sánchez-Herrero, Abtin Tondar, Elena Perez-Bernabeu, Laura Calvet and Angel A. Juan
    Subject: Background: Antibiotics can play a pivotal role in the treatment of colorectal cancer (CRC) at various stages of the disease, both directly and indirectly. Identifying novel patterns of antibiotic effects or responses in CRC within extensive medical data poses a significant challenge that can be addressed through algorithmic approaches. Machine Learning (ML) emerges as a promising solution for predicting clinical outcomes using clinical and heterogeneous cancer data. In the pursuit of our objective, we employed ML techniques for predicting CRC mortality and antibiotic influence. Methods: We utilized a dataset to examine the accuracy of death prediction in metastatic colorectal cancer. In addition, we analyzed the association between antibiotic exposure and mortality in metastatic colorectal cancer. The dataset comprised 147 patients, nineteen independent variables, and one dependent variable. Our analysis involved testing different classification-supervised ML, including an oversampling pool for classification models, Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machine, Random Forest, XGBboost Classifier, a consensus of all models, and a consensus of top models (meta models). Results: The consensus of the top models' classifier exhibited the highest accuracy among the algorithms tested (93%). This model met the standards for good accuracy, surpassing the 90% threshold considered useful in ML applications. Consistent with the accuracy results, other metrics are also good, including precision (0.96), recall (0.93), F-Beta (0.94), and AUC (0.93). Hazard ratio analysis suggests that there is no discernible difference between patients who received antibiotics and those who did not. Conclusions: Our modelling approach provides an alternative for analyzing and predicting the relationship between antibiotics and mortality in metastatic colorectal cancer patients treated with bevacizumab, complementing classic statistical methods. This methodology lays the groundwork for future use of datasets in cancer treatment research and highlights the advantages of meta models.
    Keywords: machine learning; meta models; mortality prediction; colorectal cancer; antibiotics
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  Checksum: 96879329
   Type: CRC32
  Checksum: d3a551f1dfd4d8d03fd6c0e73716e1e4
   Type: MD5
  Checksum: 36e78981a75617e98c0ef5a0aa5032f7ad424ee8
   Type: SHA-1
  Checksum: f3400a490a92e489335aa28dc586c54debf9b9b40c21968503dd06a6a09086f1
   Type: SHA-256