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Lung Cancer

Background

This documentation summarizes the Medial EarlySign research regarding the Lung Cancer algorithm. All data points, performance metrics, and deployment history listed below are sourced exclusively from publicly available literature and peer-reviewed manuscripts. This serves as a technical overview of the original MES methodology.

Overview

The Lung cancer model was developed at Kaiser Permanente Southern California (KPSC) to identify all types of lung cancer, including Squamous, Adenocarcinoma, Small Cell, and Non-Small Cell variants. It has been validated at approximately 10 different sites.

Deployments

Publicly available deployments were in:

  • Geisinger Health System (since 2022)

List of Publications

  • Partial list of publications.
Manuscript Population Year
Machine Learning for Early Lung Cancer Identification Using Routine Clinical and Laboratory Data KPSC - US 2021
Flagging High-Risk Individuals with a ML Model Improves NSCLC Early Detection in a USPSTF-Eligible Population KPSC - US 2023
Improved Efficiency with Lungflag vs Opportunistic Selection in a Theoretical East Asian Lung Cancer Screening Program Budget impact 2023
Use of the predictive risk model LungFlagTM for lung cancer screening in a Spanish reference center: A cost-effectiveness analysis Spain - Budget Impact 2023
LungFlag, a machine-learning (ML) personalized tool for assessing lung cancer risk in a community setting, to evaluate performance in flagging non-small cell lung cancer (NSCLC) regardless of sex or race KPSC - US 2023
1561P Targeted screening methodologies to select high risk individuals: LungFlag performance in Estonia Lung Cancer Screening Pilot Estonia 2024
Validation of LungFlag™ Prediction Model Using Electronic Medical Records (EMR) On Taiwan Data National Taiwan University Hospital - Taiwan 2024
Artificial intelligence–aided lung cancer screening in routine clinical practice: A pilot of LungFlag at Geisinger Geisinger - US 2024
Budget impact model of LungFlag, a predictive risk model for lung cancer screening US - Budget Impact 2024
Validation of LungFlag Lean machine-learning model to identify individuals with lung cancer using multinational data KPSC - US, Geisinger - US, THIN - UK, additional site in US 2025
Cost-effectiveness of a machine learning risk prediction model (LungFlag) in the selection of high-risk individuals for non-small cell lung cancer screening in Spain Spain - Budget Impact 2025
Maximizing Lung Cancer Screening in High-Risk Population Leveraging ML-Developed Risk-Prediction Algorithms: Danish Retrospective Validation of LungFlag Southern Denmark 2025