ArticleFrontiers in oncology2022
Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A narrative review on CT-based evaluation and prediction of lung nodule growth: current status and future directions.Translational lung cancer research · 2026Review
- [Advances in the Growth Risk Assessment and Precision Management of Pulmonary Subsolid Nodules].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2026Review
- Predicting pulmonary nodule growth from a single time point: a fusion model of radiomics and deep learning to optimize follow-up strategies.Journal of thoracic disease · 2026Article
- The intersection of artificial intelligence and lung nodule research: current applications and future prospects.International journal of surgery (London, England) · 2026Article
- Advantages of integrating artificial intelligence and spectral CT for lung nodule classification and prognostic judgment: a narrative review.Journal of thoracic disease · 2025Review
- Prediction model for growth trends of subpleural subsolid nodules using CT radiomics and radiological features.Scientific reports · 2025Article
- Nomogram integrating clinical-radiological and radiomics features for differentiating invasive from non-invasive pulmonary adenocarcinomas presenting as ground-glass nodules.American journal of cancer research · 2025Article
- Prediction of subsolid pulmonary nodule growth rate using radiomics.BMC medical imaging · 2023Article
- A proposed methodology for detecting the malignant potential of pulmonary nodules in sarcoma using computed tomographic imaging and artificial intelligence-based models.Frontiers in oncology · 2023Article
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18 authors.
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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.
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