LOW-DOSE COMPUTED TOMOGRAPHY IMAGES AND CORRESPONDING DATA

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The National Lung Screening Trial (NLST) demonstrated a substantial reduction in lung cancer mortality in subjects screened with low-dose computerized tomography (LDCT) as compared to chest radiographs; however, there was also a very high false positive rate (FPR) with the LDCT screens. The FPR was around 25% for the first two screening rounds, and 16% in the final round. In addition to the high FPR, there is a need for improvement in predicting risk among those with positive LDCT screens. In the NLST, of those with positive LDCT screens who went on to lung biopsy, about 40% did not have cancer. Conversely, there is also the problem of diagnostic uncertainty leading to delay in proceeding to biopsy among those who do have lung cancer. Among 21% of the NLST subjects who were retrospectively determined to have had lung cancer present at the baseline LDCT scan, it took over 18 months to diagnose the cancer. Therefore, the assessment of whom among those with positive screens needs to proceed to biopsy, and when, has room for major improvement. The high FPR of LDCT lung cancer screening, along with the limited ability in predicting risk levels, has three major detrimental effects as follows: 1) it constitutes a significant harm to patients undergoing screening in terms of short-term anxiety, increased radiation from follow-up CTs, and the potential for complications from invasive diagnostic procedures, 2) it contributes to increased health care costs and increased utilization of scarce health-care resources, and 3) it serves to lower the uptake of LDCT screening due to the perceived, and real, burden of false positives on patients and health care providers. Therefore, decreasing the FPR should serve to ameliorate these detrimental effects. Artificial intelligence (AI) is poised to transform medical imaging. In the past decade, significant progress has been made in computer aided detection (CAD) to assist with cancer detection and diagnosis, leading to a number of FDA-approved software tools. More recently, efforts are focused on deep learning to develop more accurate and integrated tools that can replicate or out-perform medical professionals. It is anticipated that AI can substantially reduce the FPR of LDCT screening while minimally affecting test sensitivity, thereby reducing diagnostic uncertainty.