Caracteristicile abdomenului și pelvisului în evaluarea CT a pacienților cu afecțiuni maligne

Autori

Cuvinte cheie:

statistica în oncologie, tomografie computerizată, neoplasme abdominale, neoplasme pelvine, stadializarea în oncologie, programe de postprocesare în tomografie computerizată

Rezumat

Potrivit American Cancer Center, cancerul cauzează aproximativ 1 din 6 decese la nivel mondial, mai mult decât SIDA, tuberculoza și malaria luate împreună, este a doua cauză de deces, după bolile cardiovasculare. Examinările imagistice pentru examinarea abdomenului și pelvisului sunt metodele de elecție în depistarea formațiunilor neoplazice cu furnizarea de informații care sunt esențiale pentru managementul ulterioar al acestor pacienți. Din bazele de date PubMed și motorul de căutare Google Scholar au fost select ate articolele publicate în perioada 2010-2020, în funcție de cuvintele cheie specifice. Informațiile privind studiile științifice internaționale privind statisticile patologiei oncologice au fost selectate și procesate la nivel global, conform datelor de la Centrul American de Cancer și Agenția Internațională pentru Cercetare a Cancerului, metode inovatoare de evaluare a stadializării pacienților cu neoplasme abdominale și pelvine și postprocesare modernă. în cazul examinării prin tomografie computerizată a pacienţilor cu neoplasme abdominale şi pelvine. După prelucrarea informațiilor din baza de date Google Scholar și PubMed, conform criteriilor de căutare, au fost găsite 346 de articole pe tema propusă. Bibliografia finală conține 176 de surse relevante, dintre care 49 au fost considerate reprezentative pentru elaborarea acestui articol de sinteză. Trebuie să ne propunem să justificăm, să optimizăm și să personalizăm fiecare procedură imagistică pentru pacienții cu neoplasme, deoarece aceștia sunt expuși frecvent la examinări imagistice.

Referințe

1. Global Cancer Facts and Figures, 4th Edition. The American Cancer Society, ©2018. Available at: https://www. cancer.org/content/dam/cancer-org/research/cancerfacts-and-statistics/global-cancer-facts-and-figures/global-cancer-facts-and-figures-4th-edition.pdf

2. National Institutes of Health (NIH). Understanding clinical studies [Internet]. October 18, 2016. https://www.nih.gov/about-nih/what-we-do/sciencehealth-public-trust/perspectives/understandingclinical-studies

3. HAN J., HARRISONL., PATZELT L. et al. Imaging modalities for diagnosis and monitoring of cancer cachexia. In: EJNMMI Res 2021, 11(1):94. https://doi.org/10.1186/s13550-021-00834-2

4. KROSCHKE J., VON STACKELBERG O., HEUSSEL C.P. et al. Imaging Biomarkers in Thoracic Oncology: Current Advances in the Use of Radiomics in Lung Cancer Patients and its Potential Use for Therapy Response Prediction and Monitoring. In: Rofo 2022. doi: 10.1055/a-1729-1516. Online ahead of print. https://doi.org/10.1055/a-1729-1516

5. TAO J., WANG Y., LIANG Y., ZHANG A. Evaluation and Monitoring of Endometrial Cancer Based on Magnetic Resonance Imaging Features of Deep Learning. In: Contrast Media Mol Imaging. 2022, 2022:5198592. https://doi.org/10.1155/2022/5198592

6. The Radiological Society of North America (RSNA). RSNA Radiology Reporting Initiative. 2016. https://reportingwiki.rsna.org/index.php/RSNA_Radiology_Reporting_Initiative.

7. The American College of Radiology (ACR). Practice parameter for communication of diagnostic imaging findings. 2020. Available from https://www.acr.org/-/media/acr/files/practice-parameters/communicationdiag.pdf.

8. FERLAY J., COLOMBET M., SOERJOMATARAM I. et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. In: Int J Cancer. 2019, 144(8):1941-1953 https://doi.org/10.1002/ijc.31937

9. LAUBY-SECRETAN B., SCOCCIANTI C., LOOMIS D. et al. Body Fatness and Cancer--Viewpoint of the IARC Working Group. In: N Engl J Med. 2016, 375(8):794-798. https://doi.org/10.1056/NEJMsr1606602

10. GARBINON., BRANCATOV., SALVATORE M., CAVALIERE C. A Systematic Review on the Role of the Perfusion Computed Tomography in Abdominal Cancer. In: Dose Response. 2021, 19(4):15593258211056199. https://doi.org/10.1177/15593258211056199

11. PARK Y., COLDITZ G.A. Diabetes and adiposity: a heavy load for cancer. In: Lancet Diabetes Endocrinol. 2018, 6(2):82-83. https://doi.org/10.1016/S2213-8587(17)30396-0

12. SULLIVAND.C., SCHWARTZ L.H., ZHAOB. The imaging viewpoint: how imaging affects determination of progression-free survival. In: Clin Cancer Res. 2013, 19(10):2621-2628. https://doi.org/10.1158/1078-0432.CCR-12-2936

13. RAHMAN A., JAHANGIR C., LYNCHS.M. et al. Advances in tissue-based imaging: impact on oncology research and clinical practice. In: Expert Rev Mol Diagn. 2020, 20(10):1027-1037. https://doi.org/10.1080/14737159.2020.1770599

14. STRITTMATTER N., MOSS J.I., RACE A.M. ET al. Multimodal molecular imaging maps the correlation between tumor microenvironments and nanomedicine distribution. In: Theranostics. 2022, 12(5):2162-2174. https://doi.org/10.7150/thno.68000

15. TEO K.Y., DAESCU O., CEDERBERG K., SENGUPTA A., LEAVEY P.J. Correlation of histopathology and multimodal magnetic resonance imaging in childhood osteosarcoma: Predicting tumor response to chemotherapy. In: PLoS One. 2022, 17(2):e0259564. https://doi.org/10.1371/journal.pone.0259564

16. AZHDEH S., KAVIANI A., SADIGHI N., RAHMANI M. Accurate Estimation of Breast Tumor Size: A Comparison Between Ultrasonography, Mammography, Magnetic Resonance Imaging, and Associated Contributing Factors. In: Eur J Breast Health. 2021, 17(1):53-61. https://doi.org/10.4274/ejbh.2020.5888

17. HOANG J.K., MIDDLETON W.D., FARJAT A.E. et al. Interobserver Variability of Sonographic Features Used in the American College of Radiology Thyroid Imaging Reporting and Data System. In: AJR Am J Roentgenol. 2018, 211(1):162-167. https://doi.org/10.2214/AJR.17.19192

18. WATANABE K., KATAYAMA N., KATSUI K. et al. Interobserver variability of 3.0-tesla and 1.5-tesla magnetic resonance imaging/computed tomography fusion image-based post-implant dosimetry of prostate brachytherapy. In: J Radiat Res. 2019, 60(4):483-489. https://doi.org/10.1093/jrr/rrz012

19. OXNARD G.R., ZHAO B., SIMA C.S. et al. Variability of lung tumor measurements on repeat computed tomography scans taken within 15 minutes. In: J Clin Oncol 2011, 29(23):3114-3119. https://doi.org/10.1200/JCO.2010.33.7071

20. REN J., ERIKSEN J.G., NIJKAMP J., KORREMAN S.S. Comparing different CT, PET and MRI multi-modality image combinations for deep learning-based head and neck tumor segmentation. In: Acta Oncol. 2021, 60(11):1399-1406. https://doi.org/10.1080/0284186X.2021.1949034

21. SOLIMAN M.A., GUCCIONE J., REITER A.M. et al. Current Concepts in Multi-Modality Imaging of Solid Tumor Angiogenesis. In: Cancers (Basel). 2020, 12(11). 22. DING H., WU C., LIAON. ET al. Radiomics in Oncology: A 10-Year Bibliometric Analysis. In: Front Oncol. 2021, 11:689802. https://doi.org/10.3390/cancers12113239

23. BUCKLER A.J., BRESOLIN L., DUNNICK N.R. et al. A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging. In: Radiology. 2011, 258(3):906-914. https://doi.org/10.1148/radiol.10100799

24. American Cancer Society Guidelines for the Early Detection of Cancer, 2020. Available from https://www.iaff.org/wp-content/uploads/Cancer-screeningguidelines.pdf.

25. SUNDIN A., ARNOLD R., BAUDIN E., et al. ENETS Consensus Guidelines for the Standards of Care in Neuroendocrine Tumors: Radiological, Nuclear Medicine & Hybrid Imaging. In: Neuroendocrinology. 2017, 105(3):212-244. https://doi.org/10.1159/000471879

26. PAVEL M., OBERG K., FALCONI M., et al. Gastroenteropancreatic neuroendocrine neoplasms: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2020, 31(7):844-860. https://doi.org/10.1016/j.annonc.2020.03.304

27. VERSLYPE C., ROSMORDUC O., ROUGIER P., GROUP EGW. Hepatocellular carcinoma: ESMO-ESDO Clinical Practice Guidelines for diagnosis, treatment and follow-up. In: Ann Oncol. 2012, 23 Suppl 7:vii41-48. https://doi.org/10.1093/annonc/mds225

28. ARORA A., KUMAR A. Treatment Response Evaluation and Follow-up in Hepatocellular Carcinoma. In: J Clin Exp Hepatol. 2014, 4(Suppl 3):S126-129. https://doi.org/10.1016/j.jceh.2014.05.005

29. BEBBINGTON N.A., JORGENSEN T., DUPONT E., MICHEELSEN M.A. Validation of CARE kV automated tube voltage selection for PET-CT: PET quantification and CT radiation dose reduction in phantoms. In: EJNMMI Phys. 2021, 8(1):29. https://doi.org/10.1186/s40658-021-00373-8

30. DANE B., O'DONNELL T., LIU S. et al. Radiation dose reduction, improved isocenter accuracy and CT scan time savings with automatic patient positioning by a 3D camera. In: Eur J Radiol. 2021, 136:109537. https://doi.org/10.1016/j.ejrad.2021.109537

31. DASHTBANIMOGHARIM., ZHOUL., YUB. et al. Efficient radiation dose reduction in whole-brain CT perfusion imaging using a 3D GAN: performance and clinical feasibility. In: Phys Med Biol. 2021, 66(7). https://doi.org/10.1088/1361-6560/abe917

32. MASUDA T., FUNAMA Y., NAKAURA T. et al. Radiation dose reduction method combining the ECG-Edit function and high helical pitch in retrospectively-gated CT angiography. In: Radiography (Lond). 2022, 28(3):766-771. https://doi.org/10.1016/j.radi.2022.03.004

33. ZHONG J., GALLAGHER M., HOUNSLOW C., IBALL G., WAHT. Radiation dose reduction in CT-guided cryoablation of renal tumors. In: Diagn Interv Radiol. 2021, 27(2):244-248. https://doi.org/10.5152/dir.2021.19548

34. JOYCE S., O'CONNOR O.J., MAHER M.M., MCENTEE M.F. Strategies for dose reduction with specific clinical indications during computed tomography. In: Radiography (Lond). 2020, 26 Suppl 2:S62-S68. https://doi.org/10.1016/j.radi.2020.06.012

35. BAKER S.I., KAMBOJ S. Applying ALARA Principles in the Design of New Radiological Facilities. In: Health Phys. 2022, 122(3):452-462. https://doi.org/10.1097/HP.0000000000001515

36. COHEN M.D. ALARA, image gently and CT-induced cancer. In: Pediatr Radiol. 2015, 45(4):465-470. https://doi.org/10.1007/s00247-014-3198-3

37. HINIKER S.M., DONALDSON S.S. ALARA: in radiation oncology and diagnostic imaging alike. In: Oncology (Williston Park). 2014, 28(3):247-248.

38. OENNING A.C., JACOBS R., SALMON B., GROUP D.R. ALADAIP, beyond ALARA and towards personalized optimization for paediatric cone-beam CT. In: Int J Paediatr Dent. 2021, 31(5):676-678. https://doi.org/10.1111/ipd.12797

39. SOLOMON D.Z., AYALEW B., DELLIE S.T., ADMASIE D. Justification and Optimization Principles of ALARA in Pediatric CT at a Teaching Hospital in Ethiopia. In: Ethiop J Health Sci. 2020, 30(5):761-766. 40. HRICAK H., BRENNER D.J., ADELSTEINS.J. et al. Managing radiation use in medical imaging: a multifaceted challenge. In: Radiology. 2011, 258(3):889-905. https://doi.org/10.4314/ejhs.v30i5.16

41. Rules of the International Commission on Radiological Protection: Amended by the ICRP Main Commission on 2018 April 29. In: Ann ICRP. 2018, 47(3-4):363-413. https://doi.org/10.1177/0146645318793728

42. SUBRAMANIAM R.M., KURTH D.A., WALDRIP C.A., RYBICKI F.J. American College of Radiology Appropriateness Criteria: Advancing Evidence-Based Imaging Practice. In: Semin Nucl Med. 2019, 49(2):161-165. https://doi.org/10.1053/j.semnuclmed.2018.11.011

43. NAGAYAMA Y., ODA S., NAKAURA T. ET al. Radiation Dose Reduction at Pediatric CT: Use of Low Tube Voltage and Iterative Reconstruction. In: Radiographics. 2018, 38(5):1421-1440. https://doi.org/10.1148/rg.2018180041

44. PARAKH A., KORTESNIEMI M., SCHINDERA S.T. CT Radiation Dose Management: A Comprehensive Optimization Process for Improving Patient Safety. In: Radiology. 2016, 280(3):663-673. https://doi.org/10.1148/radiol.2016151173

45. RAJIAH P., GUILD J., BROWNING T., VENKATARAMAN V., ABBARA S. A Comprehensive CT Radiation Dose Reduction and Protocol Standardization Program in a Complex, Tertiary Hospital System. In: Curr Probl Diagn Radiol. 2020, 49(5):340-346. https://doi.org/10.1067/j.cpradiol.2020.04.007

46. YU L., LIU X., LENG S. et al. Radiation dose reduction in computed tomography: techniques and future perspective. In: Imaging Med. 2019, 1(1):65-84. https://doi.org/10.2217/iim.09.5

47. FURUYA K., AKIYAMA S., NAMBU A., SUZUKI Y., HASEBE Y. A Method for the Automatic Exposure Control in Pediatric Abdominal CT: Application to the Standard Deviation Value and Tube Current Methods by Using Patient's Age and Body Size. In: Nihon Hoshasen Gijutsu Gakkai Zasshi. 2017, 73(1):33-41. https://doi.org/10.6009/jjrt.2017_JSRT_73.1.33

48. HARPER K.D., LI S., JENNINGS R. et al. The Relative Effects of Manual Versus Automatic Exposure Control on Radiation Dose to Vital Organs in Total Hip Arthroplasty. In: J Am Acad Orthop Surg. 2018, 26(1):27-34. https://doi.org/10.5435/JAAOS-D-16-00713

49. INOUE Y., NAGAHARA K., KUDO H., ITOH H. CT dose modulation using automatic exposure control in whole-body PET/CT: effects of scout imaging direction and arm positioning. In: Am J Nucl Med Mol Imaging. 2018, 8(2):143-152.

Descărcări

Publicat

20.04.2026

Număr

Secțiune

Articles

Cum cităm

[1]
Staver, N. and Staver, N. 2026. Caracteristicile abdomenului și pelvisului în evaluarea CT a pacienților cu afecțiuni maligne. Sănătate Publică, Economie şi Management în Medicină. 2(93) (Apr. 2026), 47–54.

Articole similare

1-10 of 57

Puteți, de asemenea, începeți o căutare avansată de similaritate pentru acest articol.