Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No. 17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
School of Electronic Engineering and Computer Science, Peking University, No. 5 Yiheyuan Rd., Haidian District, Beijing, 100871, China.
Eur Radiol. 2020 Feb;30(2):744-755. doi: 10.1007/s00330-019-06344-z. Epub 2019 Sep 4.
To investigate the natural history of persistent pulmonary pure ground-glass nodules (pGGNs) with deep learning-assisted nodule segmentation.
Between January 2007 and October 2018, 110 pGGNs from 110 patients with 573 follow-up CT scans were included in this retrospective study. pGGN automatic segmentation was performed on initial and all follow-up CT scans using the Dr. Wise system based on convolution neural networks. Subsequently, pGGN diameter, density, volume, mass, volume doubling time (VDT), and mass doubling time (MDT) were calculated automatically. Enrolled pGGNs were categorized into growth, 52 (47.3%), and non-growth, 58 (52.7%), groups according to volume growth. Kaplan-Meier analyses with the log-rank test and Cox proportional hazards regression analysis were conducted to analyze the cumulative percentages of pGGN growth and identify risk factors for growth.
The mean follow-up period of the enrolled pGGNs was 48.7 ± 23.8 months. The median VDT of the 52 pGGNs having grown was 1448 (range, 339-8640) days, and their median MDT was 1332 (range, 290-38,912) days. The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p < 0.001). The growth pattern of pGGNs may conform to the exponential model. Lobulated sign (p = 0.044), initial mean diameter (p < 0.001), volume (p = 0.003), and mass (p = 0.023) predicted pGGN growth.
Persistent pGGNs showed an indolent course. Deep learning can assist in accurately elucidating the natural history of pGGNs. pGGNs with lobulated sign and larger initial diameter, volume, and mass are more likely to grow.
• The pure ground-glass nodule (pGGN) segmentation accuracy of the Dr. Wise system based on convolution neural networks (CNNs) was 96.5% (573/594). • The median volume doubling time (VDT) of 52 pure ground-glass nodules (pGGNs) having grown was 1448 days (range, 339-8640 days), and their median mass doubling time (MDT) was 1332 days (range, 290-38,912 days). The mean time to growth in volume was 854 ± 675 days (range, 116-2856 days). • The 12-month, 24.7-month, and 60.8-month cumulative percentages of pGGN growth were 10%, 25.5%, and 51.1%, respectively, and they significantly differed among the initial diameter, volume, and mass subgroups (all p values < 0.001). The growth pattern of pure ground-glass nodules may conform to exponential model.
利用深度学习辅助结节分割研究持续性肺部纯磨玻璃结节(pGGN)的自然史。
本回顾性研究纳入了 2007 年 1 月至 2018 年 10 月间 110 名患者的 110 个 pGGN 患者的 573 次随访 CT 扫描。使用基于卷积神经网络的 Dr. Wise 系统对初始和所有随访 CT 扫描中的 pGGN 进行自动分割。随后,自动计算 pGGN 直径、密度、体积、质量、体积倍增时间(VDT)和质量倍增时间(MDT)。根据体积生长将纳入的 pGGN 分为生长组(52 个,47.3%)和非生长组(58 个,52.7%)。Kaplan-Meier 分析结合对数秩检验和 Cox 比例风险回归分析,分析 pGGN 生长的累积百分比,并确定生长的危险因素。
纳入的 pGGN 平均随访时间为 48.7±23.8 个月。52 个生长的 pGGN 的中位 VDT 为 1448(范围 339-8640)天,中位 MDT 为 1332(范围 290-38912)天。pGGN 生长的 12 个月、24.7 个月和 60.8 个月累积百分比分别为 10%、25.5%和 51.1%,在初始直径、体积和质量亚组之间差异有统计学意义(均 p<0.001)。pGGN 的生长模式可能符合指数模型。分叶征(p=0.044)、初始平均直径(p<0.001)、体积(p=0.003)和质量(p=0.023)预测 pGGN 生长。
持续性 pGGN 呈惰性过程。深度学习可以准确阐明 pGGN 的自然史。具有分叶征和较大初始直径、体积和质量的 pGGN 更有可能生长。