[1] VELAVAN T P, MEYER C G. The COVID-19 epidemic [J]. Tropical medicine & international health : TM & IH, 2020, 25(3): 278-80.
[2] ZHU N, ZHANG D, WANG W, et al. A Novel Coronavirus from Patients with Pneumonia in China, 2019 [J]. The New England journal of medicine, 2020, 382(8): 727-33.
[3] CHENG Z, LU Y, CAO Q, et al. Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China [M]. AJR American journal of roentgenology. 2020: 1-6.
[4] CHUNG M, BERNHEIM A, MEI X, et al. CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV) [J]. Radiology, 2020, 295(1): 202-7.
[5] ZHOU S, WANG Y, ZHU T, et al. CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China [J]. AJR American journal of roentgenology, 2020, 1-8.
[6] CHUNG J H, COX C W, MONTNER S M, et al. CT Features of the Usual Interstitial Pneumonia Pattern: Differentiating Connective Tissue Disease-Associated Interstitial Lung Disease From Idiopathic Pulmonary Fibrosis [J]. AJR American journal of roentgenology, 2018, 210(2): 307-13.
[7] LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine [J]. Nature reviews Clinical oncology, 2017, 14(12): 749-62.
[8] CUNLIFFE A, ARMATO S G, 3RD, CASTILLO R, et al. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development [J]. International journal of radiation oncology, biology, physics, 2015, 91(5): 1048-56.
[9] CHENG G Z, SAN JOSE ESTEPAR R, FOLCH E, et al. Three-dimensional Printing and 3D Slicer: Powerful Tools in Understanding and Treating Structural Lung Disease [J]. Chest, 2016, 149(5): 1136-42.
[10] FOY J J, ARMATO S G, 3RD, AL-HALLAQ H A. Effects of variability in radiomics software packages on classifying patients with radiation pneumonitis [J]. Journal of medical imaging (Bellingham, Wash), 2020, 7(1): 014504.
[11] YANLING W, DUO G, ZUOJUN G, et al. Radiomics Nomogram Analyses for Differentiating Pneumonia and Acute Paraquat Lung Injury [J].
[12] KOçAK B, DURMAZ E, ATEŞ E, et al. Radiomics with artificial intelligence: a practical guide for beginners [J]. Diagnostic and interventional radiology (Ankara, Turkey), 2019, 25(6): 485-95.
[13] LE T T, FU W, MOORE J H. Scaling tree-based automated machine learning to biomedical big data with a feature set selector [J]. Bioinformatics (Oxford, England), 2020, 36(1): 250-6.
[14] ORLENKO A, KOFINK D, LYYTIKäINEN L P, et al. Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning [J]. Bioinformatics (Oxford, England), 2020, 36(6): 1772-8.
[15] SU X, CHEN N, SUN H, et al. Automated machine learning based on radiomics features predicts H3 K27M mutation in midline gliomas of the brain [J]. Neuro-oncology, 2020, 22(3): 393-401.
[16] ADEGUNSOYE A, OLDHAM J M, VALENZI E, et al. Interstitial Pneumonia With Autoimmune Features: Value of Histopathology [J]. Archives of pathology & laboratory medicine, 2017, 141(7): 960-9.
[17] PETERANDERL C, HEROLD S, SCHMOLDT C. Human Influenza Virus Infections [J]. Seminars in respiratory and critical care medicine, 2016, 37(4): 487-500.
[18] SHAH R D, WUNDERINK R G. Viral Pneumonia and Acute Respiratory Distress Syndrome [J]. Clinics in chest medicine, 2017, 38(1): 113-25.
[19] JANKOWICH M D, ROUNDS S I S. Combined pulmonary fibrosis and emphysema syndrome: a review [J]. Chest, 2012, 141(1): 222-31.