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, Jiang Ben-Yuan, Dong Song, Nie Qiang, Zhong Wen-Zhao, Wu Yi-Long, Yang Xue-Ning
Guangdong Lung Cancer Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Guangzhou Shiyuan Electronics Co., Ltd, Guangzhou, China.
Front Oncol. 2022 Oct 12;12:1002953. doi: 10.3389/fonc.2022.1002953. eCollection 2022.
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.
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.
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.
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.
评估肺亚实性结节（SSN）的生长情况对于其随访期间的成功管理至关重要。本研究的目的是：（1）研究SSN直径、体积和质量测量对于识别生长的敏感性；（2）试图建立一种基于深度学习的模型来预测SSN的生长。
回顾性收集了2523例至少有2年亚实性结节检查记录的患者。来自NLST数据集的2358例有3120个SSN的患者被随机分为训练集和验证集。收集来自宜康美健康管理中心和广东省人民医院的患者作为外部测试集（165例患者有213个SSN）。基于LUNA16和Lndb19数据集训练的模型用于自动获取SSN的直径、体积和质量。然后，研究癌症组和非癌症组测量值的增长率，以评估识别与生长相关肺癌的最合适方法。此外，根据所选测量值，将所有SSN分为两组：生长组和非生长组。基于这些数据，开发并验证了基于深度学习的模型（暹罗模型）和放射组学模型。
癌症组和非癌症组中，直径、体积和质量的倍增时间分别为711天对963天（P = 0.20）、552天对621天（P = 0.04）和488天对623天（P<0.001）。在NLST验证集和外部测试集中，我们提出的暹罗模型的表现均优于放射组学模型，在验证集中的AUC分别为0.858（95%CI 0.786 - 0.921）和0.760（95%CI 0.646 - 0.857），在外部测试集中分别为0.862（95%CI 0.789 - 0.927）和0.681（95%CI 0.506 - 0.841）。此外，我们的暹罗模型可以使用首次CT数据预测SSN的生长，在NLST验证集中的AUC为0.855（95%CI 0.793 - 0.908），在外部测试集中为0.821（95%CI 0.725 - 0.904）。
质量增加率比直径和体积增加率更能敏感地反映与肺癌相关的SSN的生长情况。基于深度学习的模型在预测SSN生长方面具有很大潜力。
