Topping the list of the world’s worst malignancies is lung cancer, which is expected to claim 1.7 million lives worldwide by 2020. Knowing that early detection of lung cancer improves the prognosis is crucial here. .
New drugs have been developed to fight lung cancer, but unfortunately, the disease still claims the lives of most patients. Patients are typically screened for lung cancer with low-dose computed tomography (LDCT) scans in the hope of detecting the disease at an early and more treatable stage.
Sybil, an artificial intelligence tool developed by scientists at MIT’s Abdul Latif Jameel Clinic for Machine Learning in Health, the Mass General Cancer Center (MGCC), and Chang Gung Memorial Hospital (CGMH), has been proposed in a recent study. for use in determining the probability of developing lung cancer. With Sybil, screening is taken to the next level by independently evaluating LDCT imaging data to predict a patient’s likelihood of developing lung cancer over the next six years without the need for intervention by a radiologist.
The findings show that Sybil achieved C-scores of 0.75, 0.81, and 0.80 over six years using National Lung Cancer Screening Trial (NLST), CHLA, and CHLA lung LDCT scans, respectively. Even better, Sybil’s yearly predicted ROC-AUC ranged from 0.86 to 0.94, with 1.00 being the best possible score.
Because early-stage lung cancer only occupies small sections of the lung, the imaging data used to train Sybil was largely devoid of any evidence of disease. When it came to predicting which lung would get cancer, the researchers found that the model had some predictive power even when humans couldn’t fully determine where the malignancy was. Therefore, the team believes that Sybil can help close the gap in lung cancer screening deployment in the United States and internationally.
Sybil was created from NLST scans collected between 2002 and 2004, with the vast majority of participants (92% white) hailing from the United States. Before testing Sybil on CT scans with no obvious symptoms of cancer, the team labeled hundreds of CT scans with obvious malignancies to ensure that Sybil could adequately estimate cancer risk.
Since advances in CT technology over the years could potentially affect Sybil’s translation, the team chose to independently validate against more recent cohorts. They had already leaked scans with images thicker than 2.5mm since Sybil’s initial build, but the data showed that the thickness of the image slice varied over time. Sybil successfully generalized to these contemporary multi-ethnic validation sets despite the prevalence of new technologies. Sybil’s continued success at CGMH is especially noteworthy given that this demographic is largely made up of non-smokers.
A practical use of Sybil could be to reduce the number of scans or biopsies performed in patients with low-risk nodules. In fact, the adoption of the Lung-RADS system as the gold standard in the United States is based on the fact that it increases the specificity of LDCT detection compared to the nodule assessment algorithm used in NLST research. Sybil improved Lung-RADS 1.0 on the NLST suite assessment by lowering the FPR on baseline scans from 14% to 8% while holding sensitivity constant.
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Tanushree Shenwai is a consulting intern at MarktechPost. She is currently pursuing her B.Tech at the Indian Institute of Technology (IIT), Bhubaneswar. She is a data science enthusiast and has a strong interest in the scope of application of artificial intelligence in various fields. She is passionate about exploring new advances in technology and its real life application.
