Lung malignancies have been extensively characterized through radiomics and deep learning. (2015) 50:571–83. Table 1 listed the detailed demographic characteristics of the patients in two datasets. Among these nodules, 55 GGNs were AIS, 64 GGNs were MIA, and 127 GGNs were IA. No use, distribution or reproduction is permitted which does not comply with these terms. Considering the variety of approaches to Radiomics, further improvements require a … doi: 10.1148/radiol.14132187, 13. Copyright © 2020 Xia, Gong, Hao, Yang, Lin, Wang and Peng. Precision denoted the precision value (Precision=TPTP+FP), and Recall denoted the recall value (Recall=TPTP+FP). The human intervention may also affect the scheme performance. As an analytic pipeline for quantitative imaging feature extraction and analysis, radiomics has grown rapidly in the past decade. Materials and Methods 2.1. (2017) 9:4967–78. J Thorac Oncol. Before building the scheme, we first used a series of preprocessing technique to process the initial CT images. In addition, we used the positions delineated by radiologist to crop GGN patches and generate the training and testing images. The persistent presence of ground-glass nodules (GGN) in computed tomography (CT) image usually serves as an indicator of the presence of lung adenocarcinoma or its precursors (1). Since the number of non-IA GGNs is larger than that of IA GGNs in our testing dataset, it indicated that the number of negative GGNs (i.e., non-IA GGNs) miscategorized into IA class by senior radiologist was larger. At present, radiomics and deep learning are still in development, and challenges still exist – e.g., how to automatically extract features with clinical meanings, how to train a deep network with a small number of data samples, how to fuse multi-source information, and how to design representation learning with high interpretability. In a comparison with two radiologists, our new model yields higher accuracy of 80.3%. The citation should include all the papers from … Figure 4 illustrates the boxplots of GGN mean CT values in training and testing dataset. doi: 10.2214/AJR.17.17857, 14. JG performed data analysis and wrote the manuscript. The editor and reviewers' affiliations are the latest provided on their Loop research profiles and may not reflect their situation at the time of review. In order to train and test our proposed schemes, we divided the GGNs into two parts. Comparing with DL scheme and radiomics scheme (the area under a receiver operating characteristic curve (AUC): 0.83 ± 0.05, 0.87 ± 0.04), our new fusion scheme (AUC: 0.90 ± 0.03) significant improves the risk classification performance (p < 0.05). In this study, we implemented the above model building and performance evaluation processes on the Python 3.6 by using a computer with Intel Core i7-8700 CPU 3.2 GHz × 2, 16 GB RAM and a NVIDIA GeForce GTX 1,070 graphics processing unit. Five sigma values including 1, 2, 3, 4, and 5 were used to calculate the LoG features. If the robustness of our model was confirmed with more diverse and larger dataset in future studies, the proposed AI scheme would have a high impact on assisting radiologists in their clinical diagnosis of GGNs. Computer-aided diagnosis of ground-glass opacity nodules using open-source software for quantifying tumor heterogeneity. X.W, L.Z X,Y and L.T contributed equally to this work. It demonstrated that transferring segmentation DL model to classification task was feasible. Son JY, Lee HY, Kim J-H, Han J, Jeong JY, Lee KS, et al. The ongoing development of new technology needs to be validated in clinical trials and incorporated into the clinical workflow. We report initial production of a combined deep learning and radiomics … doi: 10.1118/1.3528204, 23. Radiology. Pedersen JH, Saghir Z, Wille MMW, Thomsen LHH, Skov BG, Ashraf H. Ground-glass opacity lung nodules in the era of lung cancer CT, screening: radiology, pathology, and clinical management. The details of our dataset were listed in Table 1. Deep learning and radiomics Project aim Interreg has awarded a new Artificial Intelligence project (DAME, Deep learning Algorithms for Medical image Evaluation) worth 1.1 million euros, to Peter van Ooijen from the UMCG Center for Medical Imaging (CMI). Med Phys. For stage-I lung adenocarcinoma, the 5-years DFS of AIS and MIA is 100%, but IA is only 38–86% (4, 5). 2-4. Background: Deep learning methods for radiomics/computer-aided diagnosis (CADx) are often prohibited by small datasets, long computation time, and the need for extensive image preprocessing. 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