Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images - Liao, Ri-qiang; Li, An-wei; Yan, Hong-hong; Lin, Jun-tao; Liu, Si-yang; Wang, Jing-wen; Fang, Jian-sheng; Liu, Hong-bo; Hou, Yong-he; Song, Chao; Yang, Hui-fang; Li, Bin; et al. (Frontiers in Oncology, 2022)
Background: Estimating the growth of pulmonary sub-solid nodules (SSNs) is crucial to the successful management of them during follow-up periods. The purpose of this study is to (1) investigate the measurement sensitivity of diameter, volume, and mass of SSNs for identifying growth, and (2) seek to establish a deep learning-based model to predict the growth of SSNs. Methods: A total of 2523 patients underwent at least 2-year examination retrospectively collected with SSNs. A total of 2358 patients with 3120 SSNs from the NLST dataset were randomly divided into a training and validation set. Patients from Yibicom Health Management Center and Guangdong Provincial People’s Hospital were collected as an external test set (165 patients with 213 SSN). Trained models based on LUNA16 and Lndb19 datasets were employed to automatically obtain diameter, volume, and mass of SSNs. Then, the increase rate in measurements between cancer and non-cancer groups was studied to evaluate the most appropriate way to identify growth associated lung cancer. Further, according to the selected measurement, all SSNs were classified into two groups: growth and non-growth. Based on the data, the deep learning-based model (SiamModel) and radiomics model were developed and verified. Results: The double time of diameter, volume, and mass were 711 vs 963 days (p=0.20), 552 vs 621 days (p=0.04), 488 vs 623 days (p<0.001) in the cancer and non-cancer group, respectively. Our proposed SiamModel performed better than the radiomics model in both the NLST validation set and external test set, with an AUC of 0.858 (95% CI 0.786–0.921) and 0.760(95% CI 0.646–0.857) in the validation set, and 0.862 (95% CI 0.789–0.927) and 0.681 (95% CI 0.506–0.841) in the external test set, respectively. Furthermore, our SiamModel could use the data from first time CT to predict growth of SSNs, with an AUC of 0.855 (95% CI 0.793–0.908) in the NLST validation set and 0.821 (95% CI 0.725–0.904) in the external test set. Conclusion: Mass increase rate can reflect more sensitively the growth of SSNs associated with lung cancer than diameter and volume increase rate. A deep learning-based model has a great potential to predict the growth of SSNs.