Pediatric-specific computer-aided detection of lung nodules in computed tomography scans.

Purpose: In pediatric patients with cancer, the presence of small lung nodules and micronodules on computed tomography (CT) scans can be clinically significant and indicative of metastatic cancer. Lung nodule computer-aided detection (CAD) systems trained with larger adult nodules have been shown to be less sensitive to smaller nodules in pediatric patients. To our knowledge, no pediatric-trained lung nodule CAD systems have been evaluated in the literature. To help address this gap, we compare the performance of two CAD systems, both trained on adult data and separately with pediatric data. Approach: We developed a lung nodule CAD system, Deep FlyerScan, and compared it with the Medical Open Network for Artificial Intelligence (MONAI) lung nodule detection system. Both systems are initially trained using adult data and then each is fine-tuned with pediatric data. Performance is evaluated using a retrospective pediatric dataset comprising 75 CT scans. Reference standard nodules have been provided by multiradiologist review from an earlier study. Results: The pediatric fine-tuned Deep FlyerScan and MONAI systems detected 74.8% (404/540) and 64.6% (349/540) of pediatric nodules at 5 false positives per scan, respectively. This compares with 62.0% (335/540) and 55.0% (297/540), respectively, for their counterparts trained using only adult data. Conclusions: The two CAD systems studied showed increased sensitivity in detecting lung nodules in pediatric CT scans when fine-tuned using pediatric data compared with training using only adult data. The improvement may be related to reference standard nodule size differences between the adult and pediatric datasets.