Purpose or Learning Objective: Nodule size, morphology, and growth are important factors for accurately estimating nodule malignancy risk in lung cancer screening CT examinations. In this work, we aimed to develop a deep learning (DL) algorithm that uses a current and a prior CT examination to estimate the malignancy risk of pulmonary nodules.
Methods or Background: We developed a dual time-point DL algorithm by stacking the nodules from the current and prior CT examinations in the input channels of convolutional neural networks. We used 3,011 nodules (286 malignant) and 994 nodules (73 malignant) as development and hold-out test cohorts from the National Lung Screening Trial, respectively. The reference standard was set by histopathologic confirmation or CT follow-up of more than two years. We compared the performance of the algorithm against PanCan model 2b and a previously published single time-point DL algorithm that only processed a single CT examination. We used the area under the receiver operating characteristic curve (AUC) to measure discrimination performance and a standard permutation test with 10,000 random permutations to compute p-values.
Results or Findings: The dual time-point DL algorithm achieved an AUC of 0.94 (95% CI: 0.91 - 0.97) on the hold-out test cohort. The algorithm outperformed the single time-point DL algorithm and the PanCan model, which had AUCs of 0.92 (95% CI: 0.89 - 0.95; p = 0.055) and 0.88 (95% CI: 0.85 - 0.91; p < 0.001), respectively.
Conclusion: Deep learning algorithms using current and prior CT examinations have the potential to accurately estimate the malignancy risk of pulmonary nodules.
Limitations: External validation is needed on other screening datasets to generate further evidence.
Ethics committee approval: Institutional review board approval was obtained at each of the 33 centers involved in the NLST.
Funding for this study: Research grant from MeVis Medical Solutions AG.