[
1. WHO. Global tuberculosis report 2020. Report. Geneva: World Health Organization, 2020.
]Search in Google Scholar
[
2. Prevention CfDCa [Internet]. Testing for tuberculosis (tb). Available from: https://www.cdc.gov/tb/publications/factsheets/testing/tb_testing.htm
]Search in Google Scholar
[
3. Tan JH, Acharya UR, Tan C, Abraham KT, Lim CM. Computer-assisted diagnosis of tuberculosis: A first order statistical approach to chest radiograph. Journal of Medical Systems. 2012; 36(5):2751-9. https://doi.org/10.1007/s10916-011-9751-910.1007/s10916-011-9751-921735251
]Search in Google Scholar
[
4. Lewinsohn DM, Leonard MK, LoBue PA, Cohn DL, Daley CL, Desmond E, et al. Official american thoracic society/infectious diseases society of america/centers for disease control and prevention clinical practice guidelines: Diagnosis of tuberculosis in adults and children. Clinical infectious diseases: an official publication of the Infectious Diseases Society of America. 2017; 64(2):111-5. https://doi.org/10.1093/cid/ciw77810.1093/cid/ciw778550447528052967
]Search in Google Scholar
[
5. Chen RY, Dodd LE, Lee M, Paripati P, Hammoud DA, Mountz JM, et al. Pet/ct imaging correlates with treatment outcome in patients with multidrug-resistant tuberculosis. Science translational medicine. 2014;6(265):265ra166. https://doi.org/10.1126/scitranslmed.300950110.1126/scitranslmed.3009501556778425473034
]Search in Google Scholar
[
6. Drain PK, Gardiner J, Hannah H, Broger T, Dheda K, Fielding K, et al. Guidance for studies evaluating the accuracy of biomarker-based nonsputum tests to diagnose tuberculosis. Journal of Infectious Diseases. 2019;220:S108-S115. https://doi.org/10.1093/infdis/jiz35610.1093/infdis/jiz35631593598
]Search in Google Scholar
[
7. Goletti D, Petruccioli E, Joosten SA, Ottenhoff TH. Tuberculosis Biomarkers: From Diagnosis to Protection. Infect Dis Rep. 201624;8(2):6568. https://doi.org/10.4081/idr.2016.656810.4081/idr.2016.6568492793627403267
]Search in Google Scholar
[
8. Melendez J, Ginneken Bv, Maduskar P, Philipsen RHHM, Ayles H, Sánchez CI. On combining multiple-instance learning and active learning for computer-aided detection of tuberculosis. IEEE Transactions on Medical Imaging. 2016;35(4):1013-24. https://doi.org/10.1109/TMI.2015.250567210.1109/TMI.2015.250567226660889
]Search in Google Scholar
[
9. Santosh KC, Antani S. Automated chest x-ray screening: Can lung region symmetry help detect pulmonary abnormalities? IEEE transactions on medical imaging. 2018;37(5):1168-77. https://doi.org/10.1109/TMI.2017.277563610.1109/TMI.2017.277563629727280
]Search in Google Scholar
[
10. Skoura E, Zumla A, Bomanji J. Imaging in tuberculosis. International Journal of Infectious Diseases. 2015;32:87-93. https://doi.org/10.1016/j.ijid.2014.12.00710.1016/j.ijid.2014.12.00725809762
]Search in Google Scholar
[
11. Chassagnon G, Vakalopoulou M, Paragios N, Revel MP. Artificial intelligence applications for thoracic imaging. European Journal of Radiology. 2020;123:108774. https://doi.org/10.1016/j.ejrad.2019.10877410.1016/j.ejrad.2019.10877431841881
]Search in Google Scholar
[
12. Mettler FA, Jr., Huda W, Yoshizumi TT, Mahesh M. Effective doses in radiology and diagnostic nuclear medicine: A catalog. Radiology. 2008; 248(1):254-63. https://doi.org/10.1148/radiol.248107145110.1148/radiol.248107145118566177
]Search in Google Scholar
[
13. Van’t Hoog AH, Meme HK, van Deutekom H, et al. High sensitivity of chest radiograph reading by clinical officers in a tuberculosis prevalence survey. Int J Tuberc Lung Dis. 2011;15(10):1308-14. https://doi.org/10.5588/ijtld.11.0004.10.5588/ijtld.11.000422283886
]Search in Google Scholar
[
14. Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749-762. https://doi.org/10.1038/nrclinonc.2017.14110.1038/nrclinonc.2017.14128975929
]Search in Google Scholar
[
15. Hogeweg L, Sánchez CI, Maduskar P, Philipsen R, Story A, Dawson R, et al. Automatic detection of tuberculosis in chest radiographs using a combination of textural, focal, and shape abnormality analysis. IEEE Transactions on Medical Imaging. 2015;34(12):2429-42. https://doi.org/10.1109/TMI.2015.240576110.1109/TMI.2015.240576125706581
]Search in Google Scholar
[
16. Ginneken Bv, Katsuragawa S, ter Haar Romeny, Kunio D, Viergever MA. Automatic detection of abnormalities in chest radiographs using local texture analysis. IEEE Transactions on Medical Imaging. 2002;21(2):139-49. https://doi.org/10.1109/42.99313210.1109/42.99313211929101
]Search in Google Scholar
[
17. Shen R, Cheng I, Basu A. A hybrid knowledge-guided detection technique for screening of infectious pulmonary tuberculosis from chest radiographs. IEEE Transactions on Biomedical Engineering. 2010;57(11):2646-56. https://doi.org/10.1109/TBME.2010.205750910.1109/TBME.2010.205750920624701
]Search in Google Scholar
[
18. Melendez J, Ginneken Bv, Maduskar P, Philipsen RHHM, Reither K, Breuninger M, et al. A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest x-rays. IEEE Transactions on Medical Imaging. 2015;34(1):179-92. https://doi.org/10.1109/TMI.2014.235053910.1109/TMI.2014.235053925163057
]Search in Google Scholar
[
19. Jaeger S, Juarez-Espinosa OH, Candemir S, Poostchi M, Yang F, Kim L, et al. Detecting drug-resistant tuberculosis in chest radiographs. International journal of computer assisted radiology and surgery. 2018;13(12):1915-25. https://doi.org/10.1007/s11548-018-1857-910.1007/s11548-018-1857-9622376230284153
]Search in Google Scholar
[
20. Abideen ZU, Ghafoor M, Munir K, Saqib M, Ullah A, Zia T, et al. Uncertainty assisted robust tuberculosis identification with bayesian convolutional neural networks. IEEE Access. 2020;8:22812-25. https://doi.org/10.1109/ACCESS.2020.297002310.1109/ACCESS.2020.2970023717603732391238
]Search in Google Scholar
[
21. Summers RM. Are we at a crossroads or a plateau? Radiomics and machine learning in abdominal oncology imaging. Abdominal Radiology. 2019;44(6):1985-9. https://doi.org/10.1007/s00261-018-1613-110.1007/s00261-018-1613-129730736
]Search in Google Scholar
[
22. Pesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: Threat or opportunity? Radiologists again at the forefront of innovation in medicine. European Radiology Experimental. 2018;2(1):1-10. https://doi.org/10.1186/s41747-018-0061-610.1186/s41747-018-0061-6619920530353365
]Search in Google Scholar
[
23. Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: The facts and the challenges of image analysis. European Radiology Experimental. 2018;2(1):1-8. https://doi.org/10.1186/s41747-018-0068-z10.1186/s41747-018-0068-z623419830426318
]Search in Google Scholar
[
24. Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data. Radiology. 2016; 278(2):563-77. https://doi.org/10.1148/radiol.201515116910.1148/radiol.2015151169473415726579733
]Search in Google Scholar
[
25. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: The process and the challenges. Magnetic resonance imaging. 2012;30(9):1234-48. https://doi.org/10.1016/j.mri.2012.06.01010.1016/j.mri.2012.06.010356328022898692
]Search in Google Scholar
[
26. Papanikolaou N, Matos C, Koh DM. How to develop a meaningful radiomic signature for clinical use in oncologic patients. Cancer Imaging. 2020;20(1):33. https://doi.org/10.1186/s40644-020-00311-410.1186/s40644-020-00311-4719580032357923
]Search in Google Scholar
[
27. Liu Q, Li J, Liu F, Yang W, Ding J, Chen W, et al. A radiomics nomogram for the prediction of overall survival in patients with hepatocellular carcinoma after hepatectomy. Cancer Imaging. 2020;20(1):82. https://doi.org/10.1186/s40644-020-00360-910.1186/s40644-020-00360-9766780133198809
]Search in Google Scholar
[
28. van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer research. 2017;77(21):e104-e7. https://doi.org/10.1158/0008-5472.CAN-17-033910.1158/0008-5472.CAN-17-0339567282829092951
]Search in Google Scholar
[
29. Bei W, Min L, He M, Fangfang H, Yan W, Shunying Z, et al. Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children. BMC Medical Imaging. 2019;19:63. https://doi.org/10.1186/s12880-019-0355-z10.1186/s12880-019-0355-z668834131395012
]Search in Google Scholar
[
30. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: Extracting more information from medical images using advanced feature analysis. European Journal of Cancer. 2012;48(4):441-6. https://doi.org/10.1016/j.ejca.2011.11.03610.1016/j.ejca.2011.11.036453398622257792
]Search in Google Scholar
[
31. Moher D, Liberati A, Tetzlaff J, Altman DG, The PG. Preferred reporting items for systematic reviews and meta-analyses: The prisma statement. PLOS Medicine. 2009;6(7):e1000097. https://doi.org/10.1371/journal.pmed.100009710.1371/journal.pmed.1000097270759919621072
]Search in Google Scholar
[
32. Shi W, Zhou L, Peng X, Ren H, Wang Q, Shan F, et al. Hiv-infected patients with opportunistic pulmonary infections misdiagnosed as lung cancers: The clinicoradiologic features and initial application of ct radiomics. Journal of thoracic disease. 2019;11(6):2274-86. https://doi.org/10.21037/jtd.2019.06.2210.21037/jtd.2019.06.22662677731372264
]Search in Google Scholar
[
33. Feng B, Chen X, Chen Y, Liu K, Li K, Liu X, et al. Radiomics nomogram for preoperative differentiation of lung tuberculoma from adenocarcinoma in solitary pulmonary solid nodule. European Journal of Radiology. 2020;128. https://doi.org/10.1016/j.ejrad.2020.10902210.1016/j.ejrad.2020.10902232371184
]Search in Google Scholar
[
34. Cui EN, Yu T, Shang S-J, Wang X-Y, Jin Y-L, Dong Y, et al. Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans. World Journal of Clinical Cases. 2020;8(21):5203-12. https://doi.org/10.12998/wjcc.v8.i21.520310.12998/wjcc.v8.i21.5203767472733269256
]Search in Google Scholar
[
35. Du D, Gu J, Chen X, Lv W, Feng Q, Rahmim A, et al. Integration of pet/ct radiomics and semantic features for differentiation between active pulmonary tuberculosis and lung cancer. Molecular Imaging & Biology. 2021;23(2):287-298. https://doi.org/10.1007/s11307-020-01550-410.1007/s11307-020-01550-433030709
]Search in Google Scholar
[
36. Cui EN, Yu T, Shang SJ, Wang XY, Jin YL, Dong Y, et al. Radiomics model for distinguishing tuberculosis and lung cancer on computed tomography scans. World journal of clinical cases. 2020;8(21):5203-12. https://doi.org/10.12998/wjcc.v8.i21.520310.12998/wjcc.v8.i21.5203
]Search in Google Scholar
[
37. Zwanenburg A, Vallières M, Abdalah MA, Aerts HJWL, Andrearczyk V, Apte A, et al. The image biomarker standardisation initiative: Standardised quantitative radiomics for high-throughput image-based phenotyping. Radiology. 2020;295(2):328-38. https://doi.org/10.1148/radiol.202019114510.1148/radiol.2020191145719390632154773
]Search in Google Scholar