Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.

Article by Alex Ouyang | Abdul Latif Jameel Clinic for Machine Learning in Health

The name Sybil has its origins in the oracles of Ancient Greece, also known as sibyls: feminine figures who were relied upon to relay divine knowledge of the unseen and the omnipotent past, present, and future. Now, the name has been excavated from antiquity and bestowed on an artificial intelligence tool for lung cancer risk assessment being 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).

Lung cancer is the No. 1 deadliest cancer in the world, resulting in 1.7 million deaths worldwide in 2020, killing more people than the next three deadliest cancers combined.

“It’s the biggest cancer killer because it’s relatively common and relatively hard to treat, especially once it has reached an advanced stage,” says Florian Fintelmann, MGCC thoracic interventional radiologist and co-author on the new work. “In this case, it’s important to know that if you detect lung cancer early, the long-term outcome is significantly better. Your five-year survival rate is closer to 70 percent, whereas if you detect it when it’s advanced, the five-year survival rate is just short of 10 percent.”

Although there has been a surge in new therapies introduced to combat lung cancer in recent years, the majority of patients with lung cancer still succumb to the disease. Low-dose computed tomography (LDCT) scans of the lung are currently the most common way patients are screened for lung cancer with the hope of finding it in the earliest stages, when it can still be surgically removed. Sybil takes the screening a step further, analyzing the LDCT image data without the assistance of a radiologist to predict the risk of a patient developing a future lung cancer within six years.

In their new paper published in the Journal of Clinical Oncology, Jameel Clinic, MGCC, and CGMH researchers demonstrated that Sybil obtained C-indices of 0.75, 0.81, and 0.80 over the course of six years from diverse sets of lung LDCT scans taken from the National Lung Cancer Screening Trial (NLST), Mass General Hospital (MGH), and CGMH, respectively — models achieving a C-index score over 0.7 are considered good and over 0.8 is considered strong. The ROC-AUCs for one-year prediction using Sybil scored even higher, ranging from 0.86 to 0.94, with 1.00 being the highest score possible.