Health
Deep learning models may predict lung cancer risk from a single CT scan
Deep learning evaluation of a single low-dose computed tomography (LDCT) scan may predict future lung cancer with great accuracy, according to a newly published study.
In a retrospective study published in Journal of Clinical Oncology, researchers used a total of 35,001 LDCT scans from participants in the National Lung Screening Trial (NLST) to train and develop deep learning algorithms. When predicting future lung cancer based on LDCT assessments alone, deep learning algorithms showed a 92% area under the curve (AUC) at 1 year, an 86% AUC at 2 years, and a concordance index (C-index) at 6 years. ). ), according to a survey, 75%.
In subsequent external validation tests, the researchers used a dataset of 13,309 LDCTs (6,392 patients) from Massachusetts General Hospital (MGH) and a dataset of 12,480 LDCTs (10,696 patients) from Chang Gung Memorial Hospital (CGMH). to test deep learning algorithms. in Taiwan. The authors of this study found that deep learning algorithms have a 1-year AUC of 86% and a 6-year C-index of 81%, and a 1-year AUC of 94% and a C-index of 80% on the MGH dataset. Did. CGMH dataset.
While acknowledging that prospective clinical trials are needed to confirm the clinical utility of these findings, the investigators believe that deep learning algorithms can help eliminate the need for pulmonary nodules in patients considered to have low-risk pulmonary nodules. suggesting that it may help reduce the number of follow-up images that are unsatisfactory.
“Based on our clinical results, one potential clinical application is using[deep learning algorithms]to reduce follow-up scans or biopsies in patients with low-risk nodules.” writes Dr. Regina Barzilay. She is affiliated with her Science Department of Electrical Engineering and Computers at the Massachusetts Institute of Technology in Cambridge, Massachusetts and she is affiliated with the Jameel Clinic. “…in evaluating the NLST test set, the (deep learning algorithm) further reduced her FPR (false positive rate) for baseline scans to 8%, compared to 14% for Lung-RADS 1.0, comparable maintains the sensitivity of
(Editor’s Note: For related content, seeNine points from a recent meta-analysis of lung cancer screening with low-dose CT” When “Is ultra-low-dose CT effective for lung cancer screening in current or former smokers?”)
The researchers also noted a correlation with deep learning algorithms between predicting high cancer risk and identifying where malignant nodules develop.
“We noted an association between[the deep learning algorithm’s]ability to accurately level future cancer locations and the likelihood that the LDCT would receive a high-risk score. If so, the signals it uses are localized to specific areas of risk rather than spread evenly across the chest,” Barzilay and colleagues noted.
Regarding study limitations, the authors acknowledged the retrospective nature of the study. Noting that the study cohort consisted of patients participating in a lung cancer screening program, Deephe stated that he could not assess the ability of his learning model to predict lung cancer in people not participating in a lung cancer screening program. increase. The study authors also acknowledged suboptimal diversity in the study cohort and lack of a true comparative model.
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