Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model
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View abstract on PubMed
Summary
This summary is machine-generated.A new deep-learning model, CXR-LC, identifies high-risk lung cancer smokers using chest X-rays and electronic health records. This approach improves upon current screening eligibility criteria for lung cancer detection.
Area Of Science
- Artificial Intelligence in Medical Imaging
- Oncology
- Radiology
Background
- Lung cancer screening with CT scans saves lives but current eligibility criteria miss many high-risk individuals.
- An automated deep-learning method using chest X-rays may identify more eligible smokers for CT screening.
Purpose Of The Study
- To develop and validate a convolutional neural network (CXR-LC) for predicting long-term lung cancer risk.
- To utilize readily available electronic medical record (EMR) data including chest radiographs, age, sex, and smoking status.
Main Methods
- Developed the CXR-LC model using data from the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.
- Validated the CXR-LC model in independent PLCO and National Lung Screening Trial (NLST) datasets, including heavy smokers.
Main Results
- The CXR-LC model demonstrated superior discrimination for incident lung cancer compared to Centers for Medicare & Medicaid Services (CMS) eligibility criteria.
- CXR-LC performance was comparable to the established PLCO<sub>M2012</sub> risk score, identifying more eligible patients and missing fewer cancers.
- Decision curve analysis indicated a higher net benefit for CXR-LC over CMS eligibility.
Conclusions
- The CXR-LC model effectively identifies smokers at high risk for lung cancer.
- This deep-learning approach surpasses current CMS eligibility criteria and leverages common EMR data for improved lung cancer screening identification.
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