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The main objective of the study is to evaluate the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with innovative vendor-neutral CT denoising solution based on deep learning technology.
Considering lung cancer-related public health challenges, a reliable lung cancer screening method for high-risk cohorts in Mongolia is needed. Thus, our study aims to assess the detection rate of pulmonary conditions, percentage of ionizing radiation dose reduction, and state of image quality of ULDCT coupling with artificial intelligence based CT denoising technique among various patient groups.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Low dose Chest CT scan | Active Comparator | Underwent low dose chest CT with 30% lower radiation dose Interventions: Radiation: Low radiation dose CT Other: Image quality analysis |
|
| Ultra low dose CT scan with Artificial Intelligence | Experimental | Interventions: Radiation: Low radiation dose CT Image quality Other: Deep-learning based contrast boosting algorithms |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Low radiation dose CT | Radiation | Underwent low dose chest CT with 30% lower radiation dose |
|
| Measure | Description | Time Frame |
|---|---|---|
| Detection rate of pulmonary conditions | Pulmonary condition detection rate on low dose chest CT and ultra dose chest CT with artificial intelligence-based CT denoising solution by blinded reviewers | Within 2 weeks after data collection |
| Contrast media dose | Administered contrast media dose in each patient | Within 2 weeks after data collection |
| Measure | Description | Time Frame |
|---|---|---|
| Image contrast | Signal to Noise, Noise and Edge-rise-distance on a five-point scale (1-5) with a higher score indicates better conspicuity. | Within 2 weeks after data collection |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Bayarbaatar Bold, M.D | Contact | 976-99063486 | bayarbaatar99@gmail.com | |
| Khulan Khurelsukh, M.D, MSc | Contact | 976-88010440 | khulan.kh@intermed.mn |
| Name | Affiliation | Role |
|---|---|---|
| Khulan Khurelsukh, M.D, MSc | Intermed Hospital | Study Chair |
| Delgerekh Sainjargal, M.D, MSc | Intermed Hospital | Principal Investigator |
| Bayarbaatar Bold, M.D |
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| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| Background | International Agency for Research on Cancer. Global Cancer Observatory: cancer today. World Health Organization. https://gco.iarc.fr/today (accessed Feb 14, 2022). | ||
| Background | Health Development Center, WHO. Health Indicators 2019. Mongolian Health Development Center. http://hdc.gov.mn/media/uploads/202108/Eruul_mendiin_uzuulelt_2020.pdf (accessed Feb 14, 2022). | ||
| Background | WHO global report. WHO global report on mortality attributable to tobacco. 2012 | ||
| 24756146 | Background | Zheng W, McLerran DF, Rolland BA, Fu Z, Boffetta P, He J, Gupta PC, Ramadas K, Tsugane S, Irie F, Tamakoshi A, Gao YT, Koh WP, Shu XO, Ozasa K, Nishino Y, Tsuji I, Tanaka H, Chen CJ, Yuan JM, Ahn YO, Yoo KY, Ahsan H, Pan WH, Qiao YL, Gu D, Pednekar MS, Sauvaget C, Sawada N, Sairenchi T, Yang G, Wang R, Xiang YB, Ohishi W, Kakizaki M, Watanabe T, Oze I, You SL, Sugawara Y, Butler LM, Kim DH, Park SK, Parvez F, Chuang SY, Fan JH, Shen CY, Chen Y, Grant EJ, Lee JE, Sinha R, Matsuo K, Thornquist M, Inoue M, Feng Z, Kang D, Potter JD. Burden of total and cause-specific mortality related to tobacco smoking among adults aged >/= 45 years in Asia: a pooled analysis of 21 cohorts. PLoS Med. 2014 Apr 22;11(4):e1001631. doi: 10.1371/journal.pmed.1001631. eCollection 2014 Apr. |
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| ID | Term |
|---|---|
| D008171 | Lung Diseases |
| ID | Term |
|---|---|
| D012140 | Respiratory Tract Diseases |
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| Underwent ultra dose chest CT | Radiation | Underwent ultra dose chest CT with 90% lower radiation dose |
|
| Artificial Intelligence based model | Other | Deep-learning based contrast boosting algorithms |
|
| Intermed Hospital |
| Principal Investigator |
| Background | Fourth national STEPS Survey on the Prevalence of Noncommunicable Disease and Injury Risk Factors-2019. World Health Organization. |
| 20008690 | Background | Smith-Bindman R, Lipson J, Marcus R, Kim KP, Mahesh M, Gould R, Berrington de Gonzalez A, Miglioretti DL. Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch Intern Med. 2009 Dec 14;169(22):2078-86. doi: 10.1001/archinternmed.2009.427. |
| 21714641 | Background | National Lung Screening Trial Research Team; Aberle DR, Adams AM, Berg CD, Black WC, Clapp JD, Fagerstrom RM, Gareen IF, Gatsonis C, Marcus PM, Sicks JD. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011 Aug 4;365(5):395-409. doi: 10.1056/NEJMoa1102873. Epub 2011 Jun 29. |
| 31995683 | Background | de Koning HJ, van der Aalst CM, de Jong PA, Scholten ET, Nackaerts K, Heuvelmans MA, Lammers JJ, Weenink C, Yousaf-Khan U, Horeweg N, van 't Westeinde S, Prokop M, Mali WP, Mohamed Hoesein FAA, van Ooijen PMA, Aerts JGJV, den Bakker MA, Thunnissen E, Verschakelen J, Vliegenthart R, Walter JE, Ten Haaf K, Groen HJM, Oudkerk M. Reduced Lung-Cancer Mortality with Volume CT Screening in a Randomized Trial. N Engl J Med. 2020 Feb 6;382(6):503-513. doi: 10.1056/NEJMoa1911793. Epub 2020 Jan 29. |
| 25551510 | Background | Boyd MA. U.S. radiation protection: role of national and international recommendations and opportunities for collaboration (harmony, not dissonance). Health Phys. 2015 Feb;108(2):278-82. doi: 10.1097/HP.0000000000000236. |
| 23344517 | Background | Katsura M, Matsuda I, Akahane M, Yasaka K, Hanaoka S, Akai H, Sato J, Kunimatsu A, Ohtomo K. Model-based iterative reconstruction technique for ultralow-dose chest CT: comparison of pulmonary nodule detectability with the adaptive statistical iterative reconstruction technique. Invest Radiol. 2013 Apr;48(4):206-12. doi: 10.1097/RLI.0b013e31827efc3a. |
| 26001228 | Background | Kim Y, Kim YK, Lee BE, Lee SJ, Ryu YJ, Lee JH, Chang JH. Ultra-Low-Dose CT of the Thorax Using Iterative Reconstruction: Evaluation of Image Quality and Radiation Dose Reduction. AJR Am J Roentgenol. 2015 Jun;204(6):1197-202. doi: 10.2214/AJR.14.13629. |
| 24442444 | Background | Lee SW, Kim Y, Shim SS, Lee JK, Lee SJ, Ryu YJ, Chang JH. Image quality assessment of ultra low-dose chest CT using sinogram-affirmed iterative reconstruction. Eur Radiol. 2014 Apr;24(4):817-26. doi: 10.1007/s00330-013-3090-9. Epub 2014 Jan 18. |
| 25892051 | Background | Nagatani Y, Takahashi M, Murata K, Ikeda M, Yamashiro T, Miyara T, Koyama H, Koyama M, Sato Y, Moriya H, Noma S, Tomiyama N, Ohno Y, Murayama S; investigators of ACTIve study group. Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis. Eur J Radiol. 2015 Jul;84(7):1401-12. doi: 10.1016/j.ejrad.2015.03.012. Epub 2015 Apr 2. |
| 25794063 | Background | Wang R, Sui X, Schoepf UJ, Song W, Xue H, Jin Z, Schmidt B, Flohr TG, Canstein C, Spearman JV, Chen J, Meinel FG. Ultralow-radiation-dose chest CT: accuracy for lung densitometry and emphysema detection. AJR Am J Roentgenol. 2015 Apr;204(4):743-9. doi: 10.2214/AJR.14.13101. |
| 24713541 | Background | Yanagawa M, Gyobu T, Leung AN, Kawai M, Kawata Y, Sumikawa H, Honda O, Tomiyama N. Ultra-low-dose CT of the lung: effect of iterative reconstruction techniques on image quality. Acad Radiol. 2014 Jun;21(6):695-703. doi: 10.1016/j.acra.2014.01.023. Epub 2014 Apr 6. |
| Background | Tsushima E. Intraclass correlation coefficient as a reliability index [Japanese]. http://www.hs.hirosaki-u.ac.jp/~pteiki/research/stat/icc.pdf. Accessed 9 Feb 2017. |
| 30560356 | Background | Svahn TM, Sjoberg T, Ast JC. Dose estimation of ultra-low-dose chest CT to different sized adult patients. Eur Radiol. 2019 Aug;29(8):4315-4323. doi: 10.1007/s00330-018-5849-5. Epub 2018 Dec 17. |
| 31939889 | Background | Afadzi M, Fossa K, Andersen HK, Aalokken TM, Martinsen ACT. Image Quality Measured From Ultra-Low Dose Chest Computed Tomography Examination Protocols Using 6 Different Iterative Reconstructions From 4 Vendors, a Phantom Study. J Comput Assist Tomogr. 2020 Jan/Feb;44(1):95-101. doi: 10.1097/RCT.0000000000000947. |
| 29120665 | Background | Zhang M, Qi W, Sun Y, Jiang Y, Liu X, Hong N. Screening for lung cancer using sub-millisievert chest CT with iterative reconstruction algorithm: image quality and nodule detectability. Br J Radiol. 2018 Oct;91(1090):20170658. doi: 10.1259/bjr.20170658. Epub 2017 Dec 5. |