Deep learning-based lung cancer risk assessment using chest computed tomography images without pulmonary nodules ≥8 mm.

Background: Low-dose chest computed tomography (LDCT) screening improves early detection of lung cancer but poses challenges such as false positives and overdiagnosis, especially for nodules smaller than 8 mm where follow-up guidelines are unclear. Traditional risk prediction models have limitations, and deep learning (DL) algorithms offer potential improvements but often require large datasets. This study aimed to develop a DL-based, label-free lung cancer risk prediction model using alternative LDCT images and validate it in individuals without non-calcified solid pulmonary nodules larger than 8 mm. Methods: We utilized LDCT scans from individuals without non-calcified solid nodules larger than 8 mm to develop a DL-based lung cancer risk prediction model. An alternative training dataset included 1,064 LDCT scans: 380 from patients with pathologically confirmed lung cancer and 684 from control individuals without lung cancer development over 5 years. For the lung cancer group, only the contralateral lung (without the tumor) was analyzed to represent high-risk individuals without large nodules. The LDCT scans were randomly divided into training and validation sets in a 3:1 ratio. Four three-dimensional (3D) convolutional neural networks (CNNs; 3D-CNN, MobileNet v2, SEResNet18, EfficientNet-B0) were trained using densely connected U-Net (DenseUNet)-segmented lung parenchyma images. The models were validated on a real-world test dataset comprising 1,306 LDCT scans (1,254 low-risk and 52 high-risk individuals) and evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), Brier scores, and calibration measures. Results: In the validation dataset, the AUC values were 0.801 for 3D-CNN, 0.802 for MobileNet v2, 0.755 for EfficientNet-B0, and 0.833 for SEResNet18. Corresponding Brier scores were 0.169, 0.175, 0.217, and 0.156, respectively, indicating good calibration, especially for SEResNet18. In the test dataset, the AUC values were 0.769 for 3D-CNN, 0.753 for MobileNet v2, 0.681 for EfficientNet-B0, and 0.820 for SEResNet18, with Brier scores of 0.169, 0.180, 0.202, and 0.138, respectively. The SEResNet18 model demonstrated the best performance, achieving the highest AUC and lowest Brier score in both validation and test datasets. Conclusions: Our study demonstrated that DL-based, label-free lung cancer risk prediction models using alternative LDCT images can effectively predict lung cancer development in individuals without non-calcified solid pulmonary nodules larger than 8 mm. By analyzing lung parenchyma on LDCT images without relying on nodule detection, these models may enhance the efficiency of LDCT screening programs. Further prospective studies are needed to determine their clinical utility and impact on screening protocols, and validation in larger, diverse populations is necessary to ensure generalizability.