AI Tool Developed to Predict Risk of Lung Cancer

Lung cancer is the leading cause of cancer death in the United States and around the world. Low-dose chest computed tomography (LDCT) is recommended to screen people between 50 and 80 years of age with a significant history of smoking, or who currently smoke. Lung cancer screening with LDCT has been shown to reduce death from lung cancer by up to 24 percent.

But as rates of lung cancer climb among non-smokers, new strategies are needed to screen and accurately predict lung cancer risk across a wider population. A study led by investigators from the Mass General Cancer Center, a member of Mass General Brigham, in collaboration with researchers at the Massachusetts Institute of Technology (MIT), developed and tested an artificial intelligence tool known as Sybil. Based on analyses of LDCT scans from patients in the U.S. and Taiwan, Sybil accurately predicted the risk of lung cancer for individuals with or without a significant smoking history. Results are published in the Journal of Clinical Oncology.

"Lung cancer rates continue to rise among people who have never smoked or who haven’t smoked in years, suggesting that there are many risk factors contributing to lung cancer risk, some of which are currently unknown," said corresponding author Lecia Sequist, MD, MPH, director of the Center for Innovation in Early Cancer Detection and a lung cancer medical oncologist at the Mass General Cancer Center. "Instead of assessing individual environmental or genetic risk factors, we’ve developed a tool that can use images to look at collective biology and make predictions about cancer risk."

The U.S. Preventive Service Task Force recommends annual LDCTs for people over the age of 50 with a history of 20 pack-years, who either currently smoke or have quit smoking within the last 15 years. But less than 10 percent of eligible patients are screened annually. To help improve the efficiency of lung cancer screening and provide individualized assessments, Sequist and colleagues at the Mass General Cancer Center teamed up with investigators from the Jameel Clinic at MIT. Using data from the National Lung Screening Trial (NLST), the team developed Sybil, a deep-learning model that analyzes scans and predicts lung cancer risk for the next one to six years.

"Sybil requires only one LDCT and does not depend on clinical data or radiologist annotations," said co-author Florian Fintelmann, MD, of the Department of Radiology, Division of Thoracic Imaging & Intervention at Massachusetts General Hospital. "It was designed to run in real-time in the background of a standard radiology reading station which enables point-of care clinical decision support."

The team validated Sybil using three independent data sets - a set of scans from more than 6,000 NLST participants who Sybil had not previously seen; 8,821 LDCTs from Massachusetts General Hospital (MGH); and 12,280 LDCTs from Chang Gung Memorial Hospital in Taiwan. The latter set of scans included people with a range of smoking history, including those who never smoked.

Sybil was able to accurately predict risk of lung cancer across these sets. The researchers determined how accurate Sybil was using Area Under the Curve (AUC), a measure of how well a test can distinguish between disease and normal samples and in which 1.0 is a perfect score. Sybil predicted cancer within one year with AUCs of 0.92 for the additional NLST participants, 0.86 for the MGH dataset, and 0.94 for the dataset from Taiwan. The program predicted lung cancer within six years with AUCs of 0.75, 0.81, and 0.80, respectively, for the three datasets.

"Sybil can look at an image and predict the risk of a patient developing lung cancer within six years," said co-author and Jameel Clinic faculty lead Regina Barzilay, PhD, a member of the Koch Institute for Integrative Cancer Research. "I am excited about translational efforts led by the MGH team that are aiming to change outcomes for patients who would otherwise develop advanced disease."

The researchers note that this is a retrospective study, and prospective studies that follow patients going forward are needed to validate Sybil. In addition, the U.S. participants in the study were overwhelmingly white (92 percent), and future studies will be needed to determine if Sybil can accurately predict lung cancer among diverse populations.

Sequist and colleagues will be opening a prospective clinical trial to put Sybil to test in the real world and understand how it complements the work of radiologists. The code has also been made publicly available.

"In our study, Sybil was able to detect patterns of risk from the LDCT that were not visible to the human eye," said Sequist. "We're excited to further test this program to see if it can add information that helps radiologists with diagnostics and sets us on a path to personalize screening for patients."

Mikhael PG, Wohlwend J, Yala A, Karstens L, Xiang J, Takigami AK, Bourgouin PP, Chan P, Mrah S, Amayri W, Juan YH, Yang CT, Wan YL, Lin G, Sequist LV, Fintelmann FJ, Barzilay R.
Sybil: A Validated Deep Learning Model to Predict Future Lung Cancer Risk From a Single Low-Dose Chest Computed Tomography.
J Clin Oncol. 2023 Jan 12:JCO2201345. doi: 10.1200/JCO.22.01345

Most Popular Now

With Huge Patient Dataset, AI Accurately…

Scientists have designed a new artificial intelligence (AI) model that emulates randomized clinical trials at determining the treatment options most effective at preventing stroke in people with heart disease. The model...

Radboud University Medical Center and Ph…

Royal Philips (NYSE: PHG, AEX: PHIA), a global leader in health technology, and Radboud University Medical Center have signed a hospital-wide, long-term strategic partnership that delivers the latest patient monitoring...

GPT-4, Google Gemini Fall Short in Breas…

Use of publicly available large language models (LLMs) resulted in changes in breast imaging reports classification that could have a negative effect on patient management, according to a new international...

ChatGPT fails at heart risk assessment

Despite ChatGPT's reported ability to pass medical exams, new research indicates it would be unwise to rely on it for some health assessments, such as whether a patient with chest...

Virtual Reality Shows Promise in Fightin…

A new study published in JMIR Mental Health sheds light on the promising role of virtual reality (VR) in treating major depressive disorder (MDD). Titled "Examining the Efficacy of Extended...

AXREM and Highland Marketing Partner to …

AXREM represents member companies that collectively provide UK hospitals with most of their diagnostic medical imaging technology, and radiotherapy equipment. The association has seen substantial growth in recent years, with membership...

Virtual Reality Environment for Teens ma…

Social media. The climate crisis. Political polarization. The tumult of a pandemic and online learning. Teens today are dealing with unprecedented stressors, and over the past decade their mental health...

AI Predicts Tumor-Killing Cells with Hig…

Using artificial intelligence, Ludwig Cancer Research scientists have developed a powerful predictive model for identifying the most potent cancer killing immune cells for use in cancer immunotherapies. Combined with additional algorithms...

Somerset NHS Foundation Trust Works with…

Somerset NHS Foundation Trust is working with Oleeo to help to support its recruitment processes and deliver a better experience for recruitment managers and candidates. The trust, which employs 14,000 people...

Researchers Use Foundation Models to Dis…

Researchers at Mass General Brigham have harnessed the technology behind foundation models, which power tools like ChatGPT, to discover new cancer imaging biomarkers that could transform how patterns are identified...

Why Standards are Key to Building Trust …

Opinion Article by Dean Mawson, Clinical Director and Founder, DPM Digital Health Consultancy. There's considerable interest in the potential uses of AI in healthcare at the moment; but there is also...

AI Tool to Improve Heart Failure Care

UVA Health researchers have developed a powerful new risk assessment tool for predicting outcomes in heart failure patients. The researchers have made the tool publicly available for free to clinicians. The...