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AWT IMAGE

AWT IMAGE
Volume 22, Issue 3 (7-2024)                   Int J Radiat Res 2024, 22(3): 579-584 | Back to browse issues page

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Yang J, Zhu Y, Wang F. Analysis of the predictive value of quantitative parameters of abdominal fat on CT in postoperative intestinal obstruction for gastric cancer. Int J Radiat Res 2024; 22 (3) :579-584
URL: http://ijrr.com/article-1-5588-en.html
Analysis of the predictive value of quantitative parameters of abdominal fat on CT in postoperative intestinal obstruction for gastric cancer
J. Yang , Y. Zhu , F. Wang
Department of Gastrointestinal Tumor Surgery, Changxing County People's Hospital, Huzhou, China , 13868285853@139.com
Abstract:   (1365 Views)
Background: To analyze the value of quantitative parameters of Computed Tomograph (CT) abdominal fat in predicting postoperative intestinal obstruction for gastric cancer. Materials and Methods: A retrospective analysis was conducted on 120 gastric cancer patients treated between January 2017 and December 2021. These patients were divided into two groups: an observation group with postoperative intestinal obstruction (28 patients) and a control group without (92 patients). CT scans were used to measure the Subcutaneous Fat Area (SFA) and Visceral Fat Area(VFA), calculate the SFA-VFA difference, and the VFA/SFA ratio. The receiver operating curve (ROC) was employed to evaluate the predictive efficacy of these CT measurements. Results: The observation group exhibited significantly lower VFA and SFA compared to the control group (P < 0.05), while the differences in VFA/SFA ratio and SFA-VFA were not statistically significant. The area under the ROC curve (AUC) for the combined VFA and SFA in predicting postoperative intestinal obstruction was 0.902, with a 95% confidence interval of 0.859 to 0.956. This combined measure showed higher sensitivity (96.02%) and comparable specificity (85.24%) than single measurements. Logistic regression analysis identified diabetes, malnutrition, C-Reactive Protein (CRP) levels, VFA, and SFA as risk factors for postoperative intestinal obstruction (P < 0.05). Conclusion: The combined quantitative assessment of VFA and SFA using abdominal CT improves the sensitivity of predicting postoperative intestinal obstruction in gastric cancer patients. This complication is multifactorial, emphasizing the importance of a comprehensive approach in the clinical evaluation and management of these patients.
Keywords: Abdominal CT, Fat quantitative parameters, Gastric cancer, ileus.
Full-Text [PDF 743 kb]   (353 Downloads)    
Type of Study: Original Research | Subject: Radiation Biology
References
1. Marano L, Carbone L and Poto GE, et al. (2023) Extended Lymphadenectomy for Gastric Cancer in the Neoadjuvant Era: Current Status, Clinical Implications and Contentious Issues. Current Oncology, 30(1): 875-896. [DOI:10.3390/curroncol30010067]
2. Hirasawa T, Ikenoyama Y and Ishioka M, et al. (2021) Current status and future perspective of artificial intelligence applications in endoscopic diagnosis and management of gastric cancer. Digestive Endoscopy, 33(2): 263-272. [DOI:10.1111/den.13890]
3. Gao Y, Wang YC, Lu CQ, Zeng C, Chang D and Ju S (2018) Correlations between the abdominal fat-related parameters and severity of coronary artery disease assessed by computed tomography. Quantitative Imaging in Medicine and Surgery, 8(6): 579-587. [DOI:10.21037/qims.2018.07.06]
4. Gordic S, Desbiolles L and Stolzmann P, et al. (2014) Advanced modelled iterative reconstruction for abdominal CT: qualitative and quantitative evaluation. Clinical Radiology, 69(12): e497-e504. [DOI:10.1016/j.crad.2014.08.012]
5. Gundogdu E and Emekli E (2022) CT-based Abdominal Adipose Tissue Area Changes in Patients Undergoing Adrenalectomy Due to Cushing's Syndrome and Non-functioning Adenomas. Experimental and Clinical Endocrinology & Diabetes, 130(6): 368-373. [DOI:10.1055/a-1547-9008]
6. van Stiphout JA, Driessen J and Koetzier LR, et al. (2022) The effect of deep learning reconstruction on abdominal CT densitometry and image quality: a systematic review and meta-analysis. European Radiology, 32(5): 2921-2929. [DOI:10.1007/s00330-021-08438-z]
7. Schwartz FR and Alkadhi H (2023) Photon-counting detector CT for abdominal imaging: opportunities and challenges. European Radiology, 33(11): 7805-7806. [DOI:10.1007/s00330-023-09722-w]
8. Lee MH, Lubner MG, Mellnick VM, Menias CO, Bhalla S and Pickhardt PJ (2021) The CT scout view: complementary value added to abdominal CT interpretation. Abdominal Radiology, 46(10): 5021-5036. [DOI:10.1007/s00261-021-03135-3]
9. Choi HG, Chun W and Jung KH (2022) Association between gastric cancer and the family history of gastric cancer: a cross-sectional study using Korean Genome and Epidemiology Study data. European Journal of Cancer Prevention, 31(5): 408-414. [DOI:10.1097/CEJ.0000000000000724]
10. Lv T, Beeharry MK and Zhu ZL (2019) Impact of intra-peritoneal fat distribution on intra-operative bleeding volume with D2 lymphadenectomy in Chinese patients with gastric cancer. Asian Journal of Surgery, 42(7): 768-774. [DOI:10.1016/j.asjsur.2018.11.008]
11. Smyth EC, Nilsson M, Grabsch HI, van Grieken NC and Lordick F (2020) Gastric cancer. Lancet, 396(10251): 635-648. [DOI:10.1016/S0140-6736(20)31288-5]
12. Karimi P, Islami F, Anandasabapathy S, Freedman ND and Kamangar F (2014) Gastric cancer: descriptive epidemiology, risk factors, screening, and prevention. Cancer Epidemiology Biomarkers & Prevention, 23(5): 700-713. [DOI:10.1158/1055-9965.EPI-13-1057]
13. Schwartz FR, Ashton J and Wildman-Tobriner B, et al. (2023) Liver fat quantification in photon counting CT in head to head comparison with clinical MRI - First experience. European Journal of Radiology, 161: 110734. [DOI:10.1016/j.ejrad.2023.110734]
14. Choi MH, Choi JI and Park MY, et al. (2018) Validation of intimate correlation between visceral fat and hepatic steatosis: Quantitative measurement techniques using CT for area of fat and MR for hepatic steatosis. Clinical Nutrition, 37(1): 214-222. [DOI:10.1016/j.clnu.2016.12.006]
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