[1]
|
Siegel, R.L., Miller, K.D. and Jemal, A. (2020) Cancer Statistics, 2020. CA: A Cancer Journal for Clinicians, 70, 7-30.
https://doi.org/10.3322/caac.21590
|
[2]
|
Valente, I.R., Cortez, P.C., Neto, E.C., Soares, J.M., de Albuquerque, V.H. and Tavares, J.M. (2016) Automatic 3D Pulmonary Nodule Detection in CT Images: A Survey. Computer Methods and Programs in Biomedicine, 124, 91-107.
https://doi.org/10.1016/j.cmpb.2015.10.006
|
[3]
|
Aberle, D.R., Adams, A.M., Berg, C.D., et al. (2011) Reduced Lung-Cancer Mortality with Low-Dose Computed Tomographic Screening. The New England Journal of Medicine, 365, 395-409. https://doi.org/10.1056/NEJMoa1102873
|
[4]
|
Kinsinger, L.S., Anderson, C., Kim, J., et al. (2017) Implementation of Lung Cancer Screening in the Veterans Health Administration. JAMA Internal Medicine, 177, 399-406. https://doi.org/10.1001/jamainternmed.2016.9022
|
[5]
|
Lambin, P., Rios-Velazquez, E., Leijenaar, R., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446.
https://doi.org/10.1016/j.ejca.2011.11.036
|
[6]
|
Wu, W., Parmar, C., Grossmann, P., et al. (2016) Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology. Frontiers in Oncology, 6, Article No. 71. https://doi.org/10.3389/fonc.2016.00071
|
[7]
|
Berenguer, R., Pastor-Juan, M.D.R., Canales-Vazquez, J., et al. (2018) Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology, 288, 407-415.
https://doi.org/10.1148/radiol.2018172361
|
[8]
|
Li, Y.J., Lu, L., Xiao, M.J., et al. (2018) CT Slice Thickness and Convolution Kernel Affect Performance of a Radiomic Model for Predicting EGFR Status in Non-Small Cell Lung Cancer: A Preliminary Study. Scientific Reports, 8, Article No. 17913. https://doi.org/10.1038/s41598-018-36421-0
|
[9]
|
Xu, C.C., Howey, J., Ohorodnyk, P., et al. (2020) Segmentation and Quantification of Infarction without Contrast Agents via Spatiotemporal Generative Adversarial Learning. Medical Image Analysis, 59, Article ID: 101568.
https://doi.org/10.1016/j.media.2019.101568
|
[10]
|
Shakibapour, E., Cunha, A., Aresta, G., et al. (2019) An Unsupervised Metaheuristic Search Approach for Segmentation and Volume Measurement of Pulmonary Nodules in Lung CT Scans. Expert Systems with Applications, 119, 415-428. https://doi.org/10.1016/j.eswa.2018.11.010
|
[11]
|
Wang, X.Y., Cui, H., Gong, G.Z., et al. (2018) Computational Delineation and Quantitative Heterogeneity Analysis of Lung Tumor on 18F-FDG PET for Radiation Dose-Escalation. Scientific Reports, 8, Article No. 10649.
https://doi.org/10.1038/s41598-018-28818-8
|
[12]
|
Mariottoni, E.B., Jammal, A.A., Urata, C.N., et al. (2020) Quantification of Retinal Nerve Fibre Layer Thickness on Optical Coherence Tomography with a Deep Learning Segmentation-Free Approach. Scientific Reports, 10, Article No. 402. https://doi.org/10.1038/s41598-019-57196-y
|
[13]
|
Gillies, R.J., Kinahan, P.E. and Hricak, H. (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology, 278, 563-577. https://doi.org/10.1148/radiol.2015151169
|
[14]
|
Wang, S., Shi, J.Y., Ye, Z.X., et al. (2019) Predicting EGFR Mutation Status in Lung Adenocarcinoma on Computed Tomography Image Using Deep Learning. European Respiratory Journal, 53, Article ID: 1800986.
https://doi.org/10.1183/13993003.00986-2018
|
[15]
|
Zwanenburg, A., Vallieres, M., Abdalah, M.A., et al. (2020) The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-Based Phenotyping. Radiology, 295, Article ID: 191145.
https://doi.org/10.1148/radiol.2020191145
|
[16]
|
Zhou, K.B., et al. (2020) A Gradient Boosting Decision Tree Algorithm Combining Synthetic Minority Over-Sampling Technique for Lithology Identification. Geophysics, 85, Article ID: WA147.
https://doi.org/10.1190/geo2019-0429.1
|
[17]
|
Hawkins, S., Wang, H., Liu, Y., et al. (2016) Predicting Malignant Nodules from Screening CT Scans. Journal of Thoracic Oncology, 11, 2120-2128.
|
[18]
|
Lee, S.H., Lee, S.M., Goo, J.M., et al. (2014) Usefulness of Texture Analysis in Differentiating Transient from Persistent Part-Solid Nodules (PSNs): A Retrospective Study. PLoS ONE, 9, e85167.
https://doi.org/10.1371/journal.pone.0085167
|
[19]
|
Chae, H.D., Park, C.M., Park, S.J., et al. (2014) Computerized Texture Analysis of Persistent Part-Solid Ground-Glass Nodules: Differentiation of Preinvasive Lesions from Invasive Pulmonary Adenocarcinomas. Radiology, 273, 285-293.
https://doi.org/10.1148/radiol.14132187
|
[20]
|
Liu, Y., Kim, J., Balagurunathan, Y., et al. (2016) Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas. Clinical Lung Cancer, 17, 441-448.E6. https://doi.org/10.1016/j.cllc.2016.02.001
|
[21]
|
Coroller, T.P., Agrawal, V., Narayan, V., et al. (2016) Radiomic Phenotype Features Predict Pathological Response in Non-Small Cell Lung Cancer. Radiotherapy & Oncology, 119, 480-486. https://doi.org/10.1016/j.radonc.2016.04.004
|
[22]
|
Coroller, T.P., Grossmann, P., Hou, Y., et al. (2015) CT-Based Radiomic Signature Predicts Distant Metastasis in Lung Adenocarcinoma. Radiotherapy & Oncology, 114, 345-350. https://doi.org/10.1016/j.radonc.2015.02.015
|
[23]
|
Mattonen, S.A., Palma, D.A., Johnson, C., et al. (2016) Detection of Local Cancer Recurrence after Stereotactic Ablative Radiation Therapy for Lung Cancer: Physician Performance versus Radiomic Assessment. International Journal of Radiation Oncology, Biology, Physics, 94, 1121-1128. https://doi.org/10.1016/j.ijrobp.2015.12.369
|