To evaluate the relationship between computed tomography reconstruction kernels and the sensitivity of deep learning algorithms for lung nodule detection. We attempted to understand consistencies and discrepancies for the same.
A 20-layer deep residual convolutional neural network based on 3D Feature pyramid networks was trained and validated on 888 CTs from the LIDC-IDRI dataset for lung nodule detection. The LIDC-IDRI dataset had a good mixture of CTs with different reconstruction kernels: soft-tissue (58%), lung (27%), and sharp (13%). 100 pairs of CTs with soft-tissue and lung kernels were randomly sampled from the NLST dataset. Ground truth for the same studies was annotated by a pair of junior and senior radiologists, with the AI’s prediction used as priors. A post-study analysis was done to measure the relationship between sensitivity and the reconstruction kernels (soft-tissue and lung) used in the CTs from NLST at different false-positive rates.
The AI showed a sensitivity of 88% at 1 FP/scan for detection of >= 4 mm nodules on 177 LIDC-IDRI CTs when compared against the consensus of 3 out of 4 radiologists, and a sensitivity of 75% at 1.5 FPs/scan for 100 lung kernel CTs and 75% at 1.6 FPs/scan for 100 soft-tissue kernel CTs from NLST with AI + radiologists as ground truth. The difference in performance by the AI in soft-tissue kernel CTs and lung kernel CTs was not found to be statistically significant as the one-way ANOVA revealed p=0.9938 and p=0.90, respectively, for sensitivity and false-positives per scan.
Deep learning algorithms are proving to be robust in the detection of lung nodules between CTs reconstructed from different kernels.