Lung cancer screening with chest computed tomography reduces lung cancer death. Centers for Medicare & Medicaid Services eligibility criteria for lung cancer screening with CT require detailed smoking information and miss many incident lung cancers. An automated deep-learning approach based on chest radiograph images may identify more smokers at high risk for lung cancer who could benefit from screening with CT. The CXR-LC model had better discrimination for incident lung cancer than CMS eligibility.
The CXR-LC model had better discrimination (area under the receiver-operating characteristic curve [AUC]) for incident lung cancer than CMS eligibility (PLCO AUC, 0.755 vs. 0.634; P < 0.001). The CXR-LC model’s performance was similar to that of PLCOM2012, a state-of-the-art risk score with 11 inputs, in both the PLCO data set (CXR-LC AUC of 0.755 vs. PLCOM2012 AUC of 0.751) and the NLST data set (0.659 vs. 0.650). When compared in equal-sized screening populations, CXR-LC was more sensitive than CMS eligibility in the PLCO data set and missed 30.7% fewer incident lung cancers.
The CXR-LC model identified smokers at high risk for incident lung cancer, beyond CMS eligibility, and using information commonly available in the EMR.
Ref: https://www.acpjournals.org/doi/10.7326/M20-1868
