Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model

Computers in Biology and Medicine - Tập 150 - Trang 106113 - 2022
Riqiang Gao1, Thomas Li1, Yucheng Tang2, Kaiwen Xu1, Mirza Khan3, Michael Kammer3, Sanja L. Antic3, Stephen Deppen3, Yuankai Huo1, Thomas A. Lasko3, Kim L. Sandler3, Fabien Maldonado3, Bennett A. Landman1,3
1Vanderbilt University, Nashville, TN 37235, USA
2Vanderbilt University, Nashville, TN, 37235, USA
3Vanderbilt University Medical Center, Nashville, TN, 37235, USA

Tài liệu tham khảo

Siegel, 2019, vol. 69, 7 Siegel, 2021, Cancer statistics, 2021, CA A Cancer J. Clin., 71, 7, 10.3322/caac.21654 Gould, 2015, Recent trends in the identification of incidental pulmonary nodules, Am. J. Respir. Crit. Care Med., 192, 1208, 10.1164/rccm.201505-0990OC Siegel, 2018 Aberle, 2011, Reduced lung-cancer mortality with low-dose computed tomographic screening, N. Engl. J. Med., 365, 395, 10.1056/NEJMoa1102873 Gould, 2013, Evaluation of Individuals with Pulmonary Nodules: when Is it Lung Cancer? Diagnosis and Management of Lung Cancer, vol. 143, e93S Paez, 2021, Risk stratification of indeterminate pulmonary nodules, Curr. Opin. Pulm. Med., 27, 240, 10.1097/MCP.0000000000000780 Ost, 2012, Decision making in patients with pulmonary nodules, Am. J. Respir. Crit. Care Med., 185, 363, 10.1164/rccm.201104-0679CI Gould, 2007, Evaluation of Patients with Pulmonary Nodules: when Is it Lung Cancer?, vol. 132, 108S Swensen, 1997, The probability of malignancy in solitary pulmonary nodules, Arch. Intern. Med., 157, 849, 10.1001/archinte.1997.00440290031002 Bornhop, 2016, Origin and prediction of free-solution interaction studies performed label-free, Proc. Natl. Acad. Sci. U. S. A., 113, e1595, 10.1073/pnas.1515706113 Kammer, 2021, vol. 204, 1306 Lecun, 2015, vol. 521, 436 He, 2016, Deep residual learning for image recognition, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016-Decem, 770 He, 2020, IEEE Trans. Pattern Anal. Mach. Intell., 42, 386, 10.1109/TPAMI.2018.2844175 Ren, 2017, Towards real-time object detection with region proposal networks, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1137, 10.1109/TPAMI.2016.2577031 Liao, 2019, Evaluate the malignancy of pulmonary nodules using the 3-D deep leaky noisy-or network, IEEE Transact. Neural Networks Learn. Syst., 1 Ardila, 2019, End-to-end Lung Cancer Screening with Three-Dimensional Deep Learning on Low-Dose Chest Computed Tomography, vol. 25, 954 Setio, 2016, Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks, IEEE Trans. Med. Imag., 35, 1160, 10.1109/TMI.2016.2536809 Dou, 2017, Multilevel contextual 3-D CNNs for false positive reduction in pulmonary nodule detection, IEEE (Inst. Electr. Electron. Eng.) Trans. Biomed. Eng., 64, 1558 Massion, 2020, Assessing the accuracy of a deep learning method to risk stratify indeterminate pulmonary nodules, Am. J. Respir. Crit. Care Med., 202, 241, 10.1164/rccm.201903-0505OC Liu, 2018, Mtmr-net: multi-task deep learning with margin ranking loss for lung nodule analysis, MICCAI, 11045, 74 Shen, 2015, Multi-scale convolutional neural networks for lung nodule classification, 9123, 588 Gao, 2020, Multi-path x-D recurrent neural networks for collaborative image classification, Neurocomputing, 397, 48, 10.1016/j.neucom.2020.02.033 Gao, 2020, Time-distanced gates in long short-term memory networks, Med. Image Anal., 65, 101785, 10.1016/j.media.2020.101785 2011, The national lung screening trial: overview and study design, Radiology, 258, 243, 10.1148/radiol.10091808 Gao, 2021 Gao, 2021, Deep multi-path network integrating incomplete biomarker and chest CT data for evaluating lung cancer risk, SPIEL, 46 Tammemägi, 2013, Selection criteria for lung-cancer screening, N. Engl. J. Med., 368, 728, 10.1056/NEJMoa1211776 Ronneberger, 2015, U-net: convolutional networks for biomedical image segmentation, 9351, 234 Ilse, 2018 Fawcett, 2006, An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861, 10.1016/j.patrec.2005.10.010 Paynter, 2013, A bias-corrected net reclassification improvement for clinical subgroups, Med. Decis. Making, 33, 154, 10.1177/0272989X12461856 McWilliams, 2013, Probability of cancer in pulmonary nodules detected on first screening CT, N. Engl. J. Med., 369, 910, 10.1056/NEJMoa1214726 Baldwin, 2015, The British Thoracic Society guidelines on the investigation and management of pulmonary nodules, Thorax, 70, 794, 10.1136/thoraxjnl-2015-207221 Kumari, 2022, Ensembling off-the-shelf models for gan training