Dive Brief:

• Researchers at MIT have developed a deep learning model that can predict individuals’ risk of lung cancer six years into the future from a single low-dose computed tomography (LDCT) scan.
• Writing in the Journal of Clinical Oncology, researchers describe the training and validation of an artificial intelligence tool designed to spot visual cues in LDCT scans associated with lung-cancer risk.
• The team has deployed the model, which they have made freely available, at clinics for research use to validate it in subpopulations that were underrepresented in the original data and to show how it fits into day-to-day clinical activities.

Dive Insight:

LDCT is an effective lung-cancer screening tool and the U.S. Preventive Services Task Force recommends annual scans for people aged 50 years and older with a 20-pack-a-year history of smoking. However, only a fraction of the target population are screened as per the recommendations, creating a need for ways to increase the rate and thereby enable timely treatment of more people.

A research team at MIT’s Jameel Clinic identified deep learning as a way to tackle the problem and worked with collaborators at other institutions to train and validate a model. Professor Regina Barzilay, MIT Jameel Clinic’s AI faculty lead, explained the process in an email to MedTech Dive.

“When training, the model is given these LDCTs and it tries to predict whether or not each participant develops cancer in the next year, in the next two years, …,  or in the next six years. From the mistakes the model makes, it learns to refine its predictions and improve its accuracy. In this way, the model is not told to look at anything specific; instead, it learns the visual cues that are predictive of future lung cancer by looking at a large set of such imaging data,” Barzilay said. 

Barzilay and her collaborators trained the model, which they call Sybil, using LDCTs from the National Lung Screening Trial (NLST). The team validated the model using more than 6,000 NLST scans that they excluded from the training dataset, as well as on almost 9,000 scans from Massachusetts General Hospital (MGH) and more than 12,000 scans from Chang Gung Memorial Hospital (CGMH) in Taiwan. The CGMH LDCTs included scans of nonsmokers, while the other datasets were limited to people who smoked. 

On an accuracy measure that puts 0.5 as random and 1.0 as perfect, Sybil’s lung cancer prediction in the year after the scan ranged from 0.86 to 0.94 across the three validation datasets. Accuracy fell further out from the scan, sliding by year five to 0.78 in the MGH data and by year six to 0.75 the NLST data and 0.74 the CGMH data.

The results encouraged the researchers to keep developing the model. Current goals include showing how the model performs in African-American patients and those of Hispanic/Latinx descent, groups that were underrepresented in the validation datasets, and how it generalizes to nonsmokers and can be useful to radiologists.

“To answer all of these questions, Sybil is being deployed in different clinics solely for research purposes at the moment,” Barzilay said. “Additionally, a major aim of our research was to make the model publicly available, so there is no intention to commercialize it. The tool will only be used within a research framework for now. It will require regulation if it’s to be used as a medical device, but the regulation of medical AI is also an interesting and rapidly evolving space.”