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Using Sequential Decision Making to Improve Lung Cancer Screening Performance.

Panayiotis Petousis¹, Audrey Winter², William Speier²

¹UCLA Bioengineering Department, Los Angeles, CA 90095, USA.

IEEE Access : Practical Innovations, Open Solutions

|August 6, 2020

Summary

This summary is machine-generated.

This study introduces a machine learning model to improve lung cancer screening accuracy. The new approach enhances early lung cancer detection while reducing false positives, making screening more reliable.

Keywords:

Dynamic Bayesian networks Early disease prediction Lung cancer screening Partially observable Markov decision processes QMDP algorithm

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Lung cancer is a leading cause of cancer death globally.
Low-dose computed tomography (LDCT) is effective for early lung cancer detection but has suboptimal specificity.
High false positive rates in screening can lead to unnecessary procedures and patient anxiety.

Purpose of the Study:

To develop a machine learning model to optimize lung cancer screening.
To improve the specificity of LDCT screening while maintaining high sensitivity.
To address the sequential decision-making nature of lung cancer screening.

Main Methods:

Utilized data from the National Lung Screening Trial (NLST).
Developed a partially-observable Markov decision process using machine learning.
Employed a dynamic Bayesian network for the observational model.
Used inverse reinforcement learning to define an expert-based rewards function.

Main Results:

The predictive model significantly decreased the false positive rate.
High true positive rates were maintained, comparable to human expert performance.
The model demonstrated the capability for earlier lung cancer detection.
Improved specificity enhances the overall reliability of lung cancer screening.

Conclusions:

Machine learning, specifically partially-observable Markov decision processes, can enhance lung cancer screening accuracy.
The developed model offers a promising approach to optimize the balance between detection and specificity in LDCT screening.
This AI-driven strategy has the potential to improve patient outcomes by enabling earlier diagnosis and reducing unnecessary follow-ups.