With an estimated 160,000 deaths in 2018, lung cancer is the most common cause of cancer death in the United States.Lung cancer screening using low-dose computed tomography has been shown to reduce mortality by 20–43% and is now included in US screening guidelines.In this model a deep learning algorithm is developed that uses a patient’s current and prior computed tomography volumes to predict the risk of lung cancer.The clinical state of the art for diagnosing lung cancer is using the ACR Lung-RADS standard which tries to help a radiologist report in a consistent way and help them decide what is the malignancy risk. Despite improved consistency, persistent inter-grader variability and incomplete characterization of comprehensive imaging findings remain as limitations of Lung-RADS.In this approach we leverage a deep convolutional neural network (CNN) to automate this complex image analysis task.The two main components are the preprocessing module, which segments and centers the lungs in the CT volumes, and the full-volume model, a three-dimensional CNN trained to take the entire lung volume and produce a cancer risk prediction score for that patient.
Input variables : Lung CT Volume images
Output Variables : Lung cancer segmentation, risk score
| Business | : | Population at High Risk of Disease | Risk by Geography | Risk by Demographics | Risk by Clinical Parameters | Optimized Hospital Resource Utilization | Decreased Cost of Care | Decreased Patient Visits |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : arxiv.org
| Model Category | : | Public |
| Date Published | : | July, 2020 |
| Healthcare Domain | : |
Medical Technology
Provider |
| Code | : | github.com |
| Medical Imaging |
| Health Risk Management |
| Health Risk Prediction |