Pointers at Glance
- Sybil is an artificial intelligence tool for lung cancer risk assessment developed by researchers at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, Mass General Cancer Center (MGCC), and Chang Gung Memorial Hospital (CGMH).
- It analyzes low-dose computed tomography (LDCT) image data to predict the risk of a patient developing future lung cancer within six years.
Lung cancer is the No. 1 deadliest cancer in the world. It is resulting in 1.7 million deaths across the world in 2020. The development of Sybil, an AI tool, is a significant step forward in the early detection of lung cancer. Despite recent advances in treatment, many patients with lung cancer still succumb to the disease.
Hence, it is highly important to detect lung cancer early. If lung cancer is detected earlier, there is a better survival rate than at advanced stages.
Sybil Analyses LDCT Image Data
Low-dose computed tomography (LDCT) scans to screen for lung cancer are becoming increasingly common, but the process is typically done with the assistance of a radiologist.
Sybil takes this process further by analyzing the LDCT image data independently, without requiring a radiologist to interpret the results. It predicts a patient’s risk of developing future lung cancer within six years. This tool not only increases the speed and efficiency of the screening process, but it also has the potential to reduce the number of false positives and false negatives that can occur when relying on human interpretation.
Sybil Obtained High Scores In Predicting Lung Cancer Risk
In a paper published in the Journal of Clinical Oncology, the researchers demonstrated that Sybil obtained high scores in predicting lung cancer risk, with C-indices of 0.75, 0.81, and 0.80 over the course of six years. The 3D nature of lung CT scans made Sybil a challenging model to build, as the imaging data used to train Sybil was largely absent of any signs of cancer because early-stage lung cancer occupies small portions of the lung.