Cut False Positives by 40% in Lung Nodule Detection
Executive Brief
- The News: CT-based deep learning model reduces false positives by 39.4 percent
- Clinical Win: Equivalent AUC for predicting cancer at one year, 98 percent
- Target Specialty: Radiologists managing patients with indeterminate lung nodules
Key Data at a Glance
Study Design: Retrospective study
Sample Size: 4,146 participants
False Positive Reduction: 39.4 percent
AUC at One Year: 98 percent
Benign Lesions Identified as Low Risk: 68.1 percent
Median Smoking History: 38 pack years
Cut False Positives by 40% in Lung Nodule Detection
New research suggests that a computed tomography (CT)-based deep learning model may offer comparable detection of lung cancer while offering a significant reduction of false positive findings in contrast to the Pan-Canadian Early Detection of Lung Cancer (PanCan) risk stratification model.
For the retrospective study, recently published in Radiology, researchers assessed the deep learning model in a cohort of 4,146 participants with a median smoking history of 38 pack years. The cohort included 7,614 benign nodules and 180 malignant nodules, according to the study authors.
The researchers found that the deep learning model offered an equivalent AUC for predicting cancer at one year (98 percent) and a slightly higher AUC at two years (96 percent vs. 94 percent) in comparison to the PanCan model. For cases involving indeterminate nodules on CT, the study authors noted the deep learning model correctly identified 68.1 percent of benign lesions as low risk at one year in comparison to 47.4 percent with the PanCan model.
“We saw that we could reduce the false positive rate relative to (the) PanCan (model) by 39.4 percent, which is substantial, without missing any of the cancers that required urgent care … cancers that were diagnosed within one year during the initial screening trials,” noted lead study author Noa Antonissen, M.D., who is affiliated with the Department of Medical Imaging at Radboud University Medical Center in Nijmegen, the Netherlands.
“… Having an AI tool that can do an accurate risk stratification at the level of an expert radiologist can really help to manage these screenings better, so that we can reduce the number of false positive screens in the end to the minimum that we need to still detect the cancers that we find in the screening program,” added study co-author Colin Jacobs, Ph.D., an associate professor of artificial intelligence in thoracic oncology at the Radboud University Medical Center in Nijmegen, the Netherlands. “I think that is the ultimate goal that we have with this research: that we can support screening programs with AI tools that improve the effectiveness and the efficiency of our screening programs.”
(Editor’s note: For related content, see “Can Deep Learning Enhance Low-Dose Chest CT Assessment of Lung Cancer Risk?,” “Can CT-Based Deep Learning Bolster Prognostic Assessments of Ground-Glass Nodules?” and “Olympus Launches CT-Based AI Software for Emphysema Screening.”)
For more insights from Drs. Antonissen and Jacobs, watch the video below.
Clinical Perspective — Dr. Ritu Saxena, Public Health
Workflow: I'd change my daily routine by incorporating a CT-based deep learning model to assess indeterminate lung nodules, given its ability to correctly identify 68.1% of benign lesions as low risk at one year. This could streamline my workflow, as I'd have more confidence in the model's risk stratification. With a reduction in false positives, I'd spend less time on unnecessary follow-ups.
Economics: The article doesn't address cost directly, but a 39.4% reduction in false positives could lead to significant cost savings by reducing unnecessary procedures and follow-up tests. This, in turn, could improve resource allocation and patient care.
Patient Outcomes: The deep learning model offers a substantial benefit to patients, with a 39.4% reduction in false positives and an equivalent AUC for predicting cancer at one year (98%). This means I can provide more accurate risk assessments, reducing patient anxiety and unnecessary procedures, while still detecting cancers that require urgent care.
Transparency & Corrections
HCP Connect is funded by Stravent LLC and maintains editorial independence from advertisers and pharmaceutical companies. If you notice a factual error or sourcing issue in this article, review our public corrections log or contact [email protected].