Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images
BackgroundEstimating 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 est...
| Main Authors: | , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-10-01
|
| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.1002953/full |
| _version_ | 1811239276897107968 |
|---|---|
| author | 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 |
| author_facet | 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 |
| author_sort | Ri-qiang Liao |
| collection | DOAJ |
| description | BackgroundEstimating 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.MethodsA 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.ResultsThe 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.ConclusionMass 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. |
| first_indexed | 2024-04-12T12:56:54Z |
| format | Article |
| id | doaj.art-75f8ac0969224488b65fbafd6ea25706 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T12:56:54Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-75f8ac0969224488b65fbafd6ea257062022-12-22T03:32:18ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-10-011210.3389/fonc.2022.10029531002953Deep learning-based growth prediction for sub-solid pulmonary nodules on CT imagesRi-qiang Liao0An-wei Li1Hong-hong Yan2Jun-tao Lin3Si-yang Liu4Jing-wen Wang5Jian-sheng Fang6Hong-bo Liu7Yong-he Hou8Chao Song9Hui-fang Yang10Bin Li11Ben-yuan Jiang12Song Dong13Qiang Nie14Wen-zhao Zhong15Yi-long Wu16Xue-ning Yang17Guangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangzhou Shiyuan Electronics Co., Ltd, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangzhou Shiyuan Electronics Co., Ltd, Guangzhou, ChinaGuangzhou Shiyuan Electronics Co., Ltd, Guangzhou, ChinaGuangzhou Shiyuan Electronics Co., Ltd, Guangzhou, ChinaYibicom Health Management Center, CVTE, Guangzhou, ChinaYibicom Health Management Center, CVTE, Guangzhou, ChinaYibicom Health Management Center, CVTE, Guangzhou, ChinaAutomation Science and Engineering, South China University of Technology, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaGuangdong Lung Cancer Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, ChinaBackgroundEstimating 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.MethodsA 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.ResultsThe 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.ConclusionMass 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.https://www.frontiersin.org/articles/10.3389/fonc.2022.1002953/fullsub solid pulmonary nodulesgrowthmassdeep learningradiomics |
| spellingShingle | 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 Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images Frontiers in Oncology sub solid pulmonary nodules growth mass deep learning radiomics |
| title | Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images |
| title_full | Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images |
| title_fullStr | Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images |
| title_full_unstemmed | Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images |
| title_short | Deep learning-based growth prediction for sub-solid pulmonary nodules on CT images |
| title_sort | deep learning based growth prediction for sub solid pulmonary nodules on ct images |
| topic | sub solid pulmonary nodules growth mass deep learning radiomics |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.1002953/full |
| work_keys_str_mv | AT riqiangliao deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT anweili deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT honghongyan deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT juntaolin deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT siyangliu deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT jingwenwang deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT jianshengfang deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT hongboliu deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT yonghehou deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT chaosong deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT huifangyang deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT binli deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT benyuanjiang deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT songdong deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT qiangnie deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT wenzhaozhong deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT yilongwu deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages AT xueningyang deeplearningbasedgrowthpredictionforsubsolidpulmonarynodulesonctimages |