Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.
Prediction of future imagery of lung nodule as growth modeling with follow-up computed tomography scans using deep learning: a retrospective cohort study.
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使用深度学习通过后续计算机断层扫描将肺结节的未来图像预测为生长模型：一项回顾性队列研究
DOI：
10.21037/tlcr-22-59
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发表时间：
2022-03
影响因子：
4
通讯作者：
Ye X
中科院分区：
文献类型：
--
作者：
Tao G;Zhu L;Chen Q;Yin L;Li Y;Yang J;Ni B;Zhang Z;Koo CW;Patil PD;Chen Y;Yu H;Xu Y;Ye X
Background Risk prediction models of lung nodules have been built to alleviate the heavy interpretative burden on clinicians. However, the malignancy scores output by those models can be difficult to interpret in a clinically meaningful manner. In contrast, the modeling of lung nodule growth may be more readily useful. This study developed a CT-based visual forecasting system that can visualize and quantify a nodule in three dimensions (3D) in any future time point using follow-up CT scans. Methods We retrospectively included 246 patients with 313 lung nodules with at least 1 follow-up CT scan. For the manually segmented nodules, we calculated geometric properties including CT value, diameter, volume, and mass, as well as growth properties including volume doubling time (VDT), and consolidation-to-tumor ratio (CTR) at follow-ups. These nodules were divided into growth and non-growth groups by thresholding their VDTs. We then developed a convolutional neural network (CNN) to model the imagery change of the nodules from baseline CT image (combined with the nodule mask) to follow-up CT image with a particular time interval. The model was evaluated on the geometric and radiological properties using either logistic regression or receiver operating characteristic (ROC) curve. Results The lung nodules consisted of 115 ground glass nodules (GGN) and 198 solid nodules and were followed up for an average of 354 days with 2 to 11 scans. The 2 groups differed significantly in most properties. The prediction of our forecasting system was highly correlated with the ground truth with small relative errors regarding the four geometric properties. The prediction-derived VDTs had an area under the curve (AUC) of 0.857 and 0.843 in differentiating growth and non-growth nodules for GGN and solid nodules, respectively. The prediction-derived CTRs had an AUC of 0.892 in classifying high- and low-risk nodules. Conclusions This proof-of-concept study demonstrated that the deep learning-based model can accurately forecast the imagery of a nodule in a given future for both GGNs and solid nodules and is worthy of further investigation. With a larger dataset and more validation, such a system has the potential to become a prognostication tool for assessing lung nodules.
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影响因子：
6.2
作者：
Ma, Jiemin;Ward, Elizabeth M.;Jemal, Ahmedin
通讯作者：
Jemal, Ahmedin
影响因子：
3.8
作者：
Wang, Chuang;Rimner, Andreas;Zhang, Pengpeng
通讯作者：
Zhang, Pengpeng
影响因子：
5
作者：
Goo, Jin Mo;Park, Chang Min;Lee, Hyun Ju
通讯作者：
Lee, Hyun Ju
影响因子：
5.9
作者：
Qi, Lin-Lin;Wu, Bo-Tong;Wang, Jian-Wei
通讯作者：
Wang, Jian-Wei
影响因子：
3.7
作者：
Vabalas, Andrius;Gowen, Emma;Casson, Alexander J.
通讯作者：
Casson, Alexander J.