Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Ri-Qiang Liao; An-Wei Li; Hong-Hong Yan; Jun-Tao Lin; Si-Yang Liu; Jing-Wen Wang; Jian-Sheng Fang; Hong-Bo Liu; Yong-He Hou; Chao Song; Hui-Fang Yang; Bin Li; Ben-Yuan Jiang; Song Dong; Qiang Nie; Wen-Zhao Zhong; Yi-Long Wu; Xue-Ning Yang

doi:10.3389/fonc.2022.1002953

Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images

Front Oncol. 2022 Oct 12:12:1002953. doi: 10.3389/fonc.2022.1002953. eCollection 2022.

Authors

Ri-Qiang Liao¹, An-Wei Li², Hong-Hong Yan¹, Jun-Tao Lin¹, Si-Yang Liu¹, Jing-Wen Wang², Jian-Sheng Fang², Hong-Bo Liu², Yong-He Hou³, Chao Song³, Hui-Fang Yang³, Bin Li⁴, Ben-Yuan Jiang¹, Song Dong¹, Qiang Nie¹, Wen-Zhao Zhong¹, Yi-Long Wu¹, Xue-Ning Yang¹

Affiliations

¹ Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
² Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China.
³ Yibicom Health Management Center, CVTE, Guangzhou, China.
⁴ Automation Science and Engineering, South China University of Technology, Guangzhou, China.

Abstract

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 2,523 patients underwent at least 2-year examination records retrospectively collected with sub-solid nodules. A total of 2,358 patients with 3,120 SSNs from the NLST dataset were randomly divided into training and validation sets. Patients from the 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 the 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) and 488 vs. 623 days (P< 0.001) in the cancer and non-cancer groups, 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 the 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 rates. A deep learning-based model has a great potential to predict the growth of SSNs.

Keywords: deep learning; growth; mass; radiomics; sub solid pulmonary nodules.