Low-dose computed tomography (CT) lung cancer screening is recommended by the US Preventive Services Task Force for high lung cancer–risk populations. In this study, we investigated an important factor affecting the CT dose—the scan length, for this CT exam. A neural network model based on the “UNET” framework was established to segment the lung region in the CT scout images. It was trained initially with 247 chest X-ray images and then with 40 CT scout images. The mean Intersection over Union (IOU) and Dice coefficient were reported to be 0.954 and 0.976, respectively. Lung scan boundaries were determined from this segmentation and compared with the boundaries marked by an expert for 150 validation images, resulting an average 4.7% difference. Seven hundred seventy CT low-dose lung screening exams were retrospectively analyzed with the validated model. The average “desired” scan length was 252 mm with a standard deviation of 28 mm. The average “over-range” was 58.5 mm or 24%. The upper boundary (superior) on average had an “over-range” of 17 mm, and the lower boundary (inferior) on average had an “over-range” of 41 mm. Further analysis of this data showed that the extent of “over-range” was independent of acquisition date, acquisition time, acquisition station, and patient age, but dependent on technologist and patient weight. We concluded that this machine learning method could effectively support quality control on the scan length for CT low-dose screening scans, enabling the eliminations of unnecessary patient dose.
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This work is supported by the Radiology Pilot Grant from Department of Radiology, School of Medicine in University of Colorado. We would like to thank the PACS and clinical analysis team from University of Colorado Health for providing technology support.
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Huo, D., Kiehn, M. & Scherzinger, A. Investigation of Low-Dose CT Lung Cancer Screening Scan “Over-Range” Issue Using Machine Learning Methods. J Digit Imaging 32, 931–938 (2019). https://doi.org/10.1007/s10278-019-00233-z
- CT lung cancer screening
- CT dose
- Machine learning
- Convolutional neural network
- Artificial neural network