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Pulmonary tuberculosis diagnosis, differentiation and disease management: A review of radiomics applications Cover

Pulmonary tuberculosis diagnosis, differentiation and disease management: A review of radiomics applications

Open Access
|Dec 2021

References

  1. 1. WHO. Global tuberculosis report 2020. Report. Geneva: World Health Organization, 2020.
  2. 2. Prevention CfDCa [Internet]. Testing for tuberculosis (tb). Available from: https://www.cdc.gov/tb/publications/factsheets/testing/tb_testing.htm
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
DOI: https://doi.org/10.2478/pjmpe-2021-0030 | Journal eISSN: 1898-0309 | Journal ISSN: 1425-4689
Language: English
Page range: 251 - 259
Published on: Dec 23, 2021
Published by: Polish Society of Medical Physics
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2021 Tamarisk Du Plessis, William Ian Duncombe Rae, Mike Michael Sathekge, published by Polish Society of Medical Physics
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.