Research progress on predicting the growth of pulmonary nodules based on CT imaging_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

XUE Wenhe ^1,2 , CHEN Nan ¹ ,  LIU Lunxu ¹

1. Department of Thoracic Surgery, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. West China School of Medicine, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

LIU Lunxu, Email: lunxu_liu@aliyun.com

Keywords：

Pulmonary nodules; CT imaging; growth prediction; radiomics; deep learning; personalized treatment

DOI：

10.7507/1007-4848.202503063

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.

Citation： XUE Wenhe, CHEN Nan, LIU Lunxu. Research progress on predicting the growth of pulmonary nodules based on CT imaging. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2025, 32(5): 581-586. doi: 10.7507/1007-4848.202503063 Copy

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45.	Wang Y, Zhou C, Ying L, et al. Enhancing early lung cancer diagnosis: Predicting lung nodule progression in follow-up low-dose CT scan with deep generative model. Cancers (Basel), 2024, 16(12).
46.
47.	Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep, 2019, 9(1): 3358.

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45. Wang Y, Zhou C, Ying L, et al. Enhancing early lung cancer diagnosis: Predicting lung nodule progression in follow-up low-dose CT scan with deep generative model. Cancers (Basel), 2024, 16(12).
46.
47. Wei JW, Tafe LJ, Linnik YA, et al. Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks. Sci Rep, 2019, 9(1): 3358.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Research progress on predicting the growth of pulmonary nodules based on CT imaging

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content