Model Reduces False Positives
In the retrospective study, the researchers trained their in-house developed deep learning algorithm to estimate the risk for malignancy for lung nodules using data from the National Lung Screening Trial which included 16,077 nodules (1,249 malignant).
External testing was conducted using baseline CT scans from the Danish Lung Cancer Screening Trial, the Multicentric Italian Lung Detection trial and the Dutch–Belgian NELSON trial. The pooled cohort included 4,146 participants (median age 58 years, 78% male, median smoking history 38 pack-years) with 7,614 benign and 180 malignant nodules.
The researchers assessed the algorithm’s performance for the pooled cohort and two subsets: indeterminate nodules (5-15 mm) and malignant nodules that were size-matched to benign nodules.
“We selected nodules sized 5–15 mm, due to their diagnostic challenges and frequent need for short-term follow-up,” Dr. Antonissen said. “Accurate risk classification of these nodules could reduce unnecessary procedures.”
For comparison, the algorithm’s performance was evaluated against the PanCan model at nodule and participant levels using the area under the receiver operating characteristic curve (AUC), among other parameters.
In the pooled cohort, the deep learning model achieved AUCs of 0.98, 0.96, and 0.94 for cancers diagnosed within one year, two years, and throughout screening, respectively, compared to PanCan at 0.98, 0.94, and 0.93.
For indeterminate nodules (129 malignant, 2,086 benign), the deep learning model significantly outperformed PanCan across all timeframes with AUCs of 0.95, 0.94, 0.90 vs. 0.91, 0.88, 0.86. For the cancers size-matched to benign nodules, (180 malignant, 360 benign), the deep learning model’s AUC was 0.79 versus PanCan at 0.60.
At 100% sensitivity for cancers diagnosed within 1 year, the deep learning model classified 68.1% of benign cases as low risk compared to 47.4% using the PanCan model, representing a 39.4% relative reduction in false positives.
“Deep learning algorithms can assist radiologists in deciding whether follow-up imaging is needed, but prospective validation is required to determine the clinical applicability of these tools and to guide their implementation in practice,” Dr. Antonissen said. “Reducing false positive results will make lung cancer screening more feasible.”
For More Information
Access the Radiology study, “External Test of a Deep Learning Algorithm for Pulmonary Nodule Malignancy Risk Stratification Using European Screening Data.”
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