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LungCancer

Pierre-Louis Benveniste $^{1}$, Julie Alberge $^{2}$

  • 1 - Ecole Normale Supérieure de Paris Saclay
  • 2 - École nationale des ponts et chaussées

This repository contains the files of the web app to predict lung cancer risk in the next 5 years.

Related Paper

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Abstract

Introduction: Lung cancer is a significant cause of mortality worldwide, emphasizing the importance of early detection for improved survival rates. In this study, we propose a machine learning (ML) tool trained on data from the PLCO Cancer Screening Trial and validated on the NLST to estimate the likelihood of lung cancer occurrence within five years.

Methods: The study utilized two datasets, the PLCO (n=55,161) and NLST (n=48,595), consisting of comprehensive information on risk factors, clinical measurements, and outcomes related to lung cancer. Data pre-processing involved removing patients who were not current or former smokers and those who had died of causes unrelated to lung cancer. Additionally, a focus was placed on mitigating bias caused by censored data. Feature selection, hyperparameter optimization, and model calibration were performed using XGBoost, an ensemble learning algorithm that combines gradient boosting and decision trees.

Results: The final ML model was trained on the pre-processed PLCO dataset and tested on the NLST dataset. The model incorporated features such as age, gender, smoking history, medical diagnoses, and family history of lung cancer. The model was well-calibrated (Brier score=0.044). ROC-AUC was 82% on the PLCO dataset and 70% on the NLST dataset. PR-AUC was 29% and 11% respectively. When compared to the USPSTF guidelines for lung cancer screening, our model provided the same recall with a precision of 13.1% vs 9.3% on the PLCO dataset and 3.2% vs 3.1% on the NLST dataset.

Conclusion: The developed ML tool provides a freely-available web application for estimating the likelihood of developing lung cancer within five years. By utilizing risk factors and clinical data, individuals can assess their risk and make informed decisions regarding lung cancer screening. This research contributes to the efforts in early detection and prevention strategies, aiming to reduce lung cancer-related mortality rates.

Repository files

The repository includes the following files :

assets

  • custom css
  • the logo of the web page

models

  • model_lungs_final.pkl - the final XGBoost model for lung cancer risk in pickle format

tabs

  • intro.py - The code for the 'About' tab
  • LungCancerRisk.py - The tab to predict lung cancer risk

main app

  • app.py - Initiates the Dash app
  • index.py - The main Dash code with the layout and callback
  • Procfile - The Procfile for Heroku
  • requirements.txt - The requirements.txt file for Heroku
  • runtime.txt - The python required version for Heroku

To run locally

In order to run this code on your machine :

  • run the requirements.txt file : pip install -r requirements.txt
  • launch the app : python index.py