Value of CT enhanced image texture analysis in diagnosis of acute pancreatitis with acute kidney injury_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

JIA Ying ^1,2 , HUANG Zixing ¹ , WANG Yi ¹ , WEI Hong ¹ ,  SONG Bin ¹

1. Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Department of Radiology, Chengdu Second People’s Hospital, Chengdu 610041, P. R. China;

Corresponding author：

SONG Bin, Email: anicesong@vip.sina.com

Keywords：

acute pancreatitis; acute kidney injury; image texture analysis

DOI：

10.7507/1007-9424.201905101

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To determine value of texture analysis based on bi-phasic enhanced CT images in diagnosis of acute pancreatitis (AP) with acute renal injury (AKI).Methods A total of 62 patients with clinically proven AP including 39 patients with AKI and 23 patients without AKI were analyzed retrospectively. The region of interest (ROI) was chosen at the axial CT-enhanced images of bilateral kidneys using the ITK-Snap software and the texture analysis was performed by the Analysis-Kinetics (A.K.) analysis software. Using the Analysis of Variance, Mann-Whitney U test, Spearman correlation analysis and LASSO regression to reduce the features dimension, and screening out the textures by the logistic regression. The receiver operating characteristic (ROC) curve was established to determine the diagnostic performance of the features.Results In the total of 396 image histological features originally extracted from the texture analysis, 6 features were finally screened out through the dimensionality reduction, involving the Haralick correlation, Inertia, Mean value, Cluster prominence, Short run high grey level emphasis, and Surface area. The area under curve (AUC), threshold, sensitivity, specificity, and accuracy in diagnosing of AP with AKI respectively was 0.926, 0.619, 89.4%, 71.4% and 82.7% by the Haralick correlation; which respectively was 0.790, 0.665, 59.6%, 82.1%, 68.0% by the Inertia; which respectively was 0.983, 0.662, 89.4%, 100%, 93.3% by the Mean value; which respectively was 0.903, 0.696, 80.9%, 85.7%, 82.7% by the Cluster prominence; which respectively was 0.980, 0.778, 76.6%, 100%, 85.3% by the Short run high grey level emphasis; which respectively was 0.819, 0.604, 78.7%, 75.0%, 77.3% by the Surface area.Conclusion Textures of contrast-enhanced CT images have better resolving ability and higher accuracy in diagnosis of AP with AKI and diagnostic efficiency of Mean value is the best.

Citation： JIA Ying, HUANG Zixing, WANG Yi, WEI Hong, SONG Bin. Value of CT enhanced image texture analysis in diagnosis of acute pancreatitis with acute kidney injury. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2019, 26(7): 865-869. doi: 10.7507/1007-9424.201905101 Copy

1.	Kong L, Santiago N, Han TQ, et al. Clinical characteristics and prognostic factors of severe acute pancreatitis. World J Gastroenterol, 2004, 10(22): 3336-3338.
2.	Pelichovská M, Cvachovec K, Hoch J. Intensive care for patients with severe acute pancreatitis with a significant multiorgan dysfunction. Rozhl Chir, 2004, 83(9): 443-450.
3.	Uchikov A, Shopov A, Markova D. Renal complications in severe acute pancreatitis. Khirurgiia (Sofiia), 2003, 59(3): 9-10.
4.	Kes P, Vucicević Z, Ratković-Gusić I, et al. Acute renal failure complicating severe acute pancreatitis. Ren Fail, 1996, 18(4): 621-628.
5.	中华医学会消化病学分会胰腺疾病学组, 中华胰腺病杂志编辑委员会, 中华消化杂志编辑委员会. 中国急性胰腺炎诊治指南(2013, 上海). 胃肠病学, 2013, 18(7): 428-433.
6.	Bradley EL 3rd. A clinically based classification system for acute pancreatitis. Summary of the International Symposium on Acute Pancreatitis, Atlanta, Ga, September 11 through 13, 1992. Arch Surg, 1993, 128(5): 586-590.
7.	Petejova N, Martinek A. Acute kidney injury following acute pancreatitis: A review. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub, 2013, 157(2): 105-113.
8.	Tran DD, Oe PL, de Fijter CW, et al. Acute renal failure in patients with acute pancreatitis: prevalence, risk factors, and outcome. Nephrol Dial Transplant, 1993, 8(10): 1079-1084.
9.	Takase K, Takeyama Y, Nishikawa J, et al. Apoptotic cell death of renal tubules in experimental severe acute pancreatitis. Surgery, 1999, 125(4): 411-420.
10.	Coca SG, Yalavarthy R, Concato J, et al. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int, 2008, 73(9): 1008-1016.
11.	Belcher JM, Edelstein CL, Parikh CR. Clinical applications of biomarkers for acute kidney injury. Am J Kidney Dis, 2011, 57(6): 930-940.
12.	Lewington AJ, Sayed A. Acute kidney injury: how do we define it? Ann Clin Biochem, 2010, 47(Pt 1): 4-7.
13.	徐登云, 王云秀. 基于灰度共生矩阵对纹理特征的分析. 西部资源, 2012, (2): 112-114.
14.	Hodgdon T, McInnes MD, Schieda N, et al. Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology, 2015, 276(3): 787-796.
15.	Yan L, Liu Z, Wang G, et al. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol, 2015, 22(9): 1115-1121.
16.	Schieda N, Thornhill RE, Al-Subhi M, et al. Diagnosis of sarcomatoid renal cell carcinoma with CT: Evaluation by qualitative imaging features and texture analysis. AJR Am J Roentgenol, 2015, 204(5): 1013-1023.
17.	Kocak B, Durmaz ES, Ates E, et al. Radiogenomics in clear cell renal cell carcinoma: machine learning-based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status. AJR Am J Roentgenol, 2019, 212(3): W55-W63.
18.	丁玖乐, 邢兆宇, 陈真, 等. CT纹理分析术前预测肾脏透明细胞癌Fuhrman分级的价值. 中华放射学杂志, 2018, 52(8): 614-618.
19.	李小虎, 裴子璐, 刘云鹏, 等. 肾脏CT容积纹理分析及机器学习相结合的影像组学评价肾透明细胞癌病理分级的价值初探. 中华放射学杂志, 2018, 52(5): 344-348.
20.	Ding J, Xing Z, Jiang Z, et al. Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging. Eur Radiol, 2019, 29(5): 2293-2301.
21.	Abbasian Ardakani A, Mohammadi A, Khalili Najafabad B, et al. Assessment of kidney function after allograft transplantation by texture analysis. Iran J Kidney Dis, 2017, 11(2): 157-164.
22.	周海龙, 张古沐阳, 石冰, 等. 定量CT纹理分析鉴别诊断透明细胞型与非透明细胞型肾癌. 中国医学影像技术, 2017, 33(12): 1768-1773.
23.	Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern, 1973, 3(6): 610-621.
24.	Tixier F, Hatt M, Le Rest CC, et al. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med, 2012, 53(5): 693-700.
25.	Tang X. Texture information in run-length matrices. IEEE Trans Image Process, 1998, 7(11): 1602-1609.

1. Kong L, Santiago N, Han TQ, et al. Clinical characteristics and prognostic factors of severe acute pancreatitis. World J Gastroenterol, 2004, 10(22): 3336-3338.
2. Pelichovská M, Cvachovec K, Hoch J. Intensive care for patients with severe acute pancreatitis with a significant multiorgan dysfunction. Rozhl Chir, 2004, 83(9): 443-450.
3. Uchikov A, Shopov A, Markova D. Renal complications in severe acute pancreatitis. Khirurgiia (Sofiia), 2003, 59(3): 9-10.
4. Kes P, Vucicević Z, Ratković-Gusić I, et al. Acute renal failure complicating severe acute pancreatitis. Ren Fail, 1996, 18(4): 621-628.
5. 中华医学会消化病学分会胰腺疾病学组, 中华胰腺病杂志编辑委员会, 中华消化杂志编辑委员会. 中国急性胰腺炎诊治指南(2013, 上海). 胃肠病学, 2013, 18(7): 428-433.
6. Bradley EL 3rd. A clinically based classification system for acute pancreatitis. Summary of the International Symposium on Acute Pancreatitis, Atlanta, Ga, September 11 through 13, 1992. Arch Surg, 1993, 128(5): 586-590.
7. Petejova N, Martinek A. Acute kidney injury following acute pancreatitis: A review. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub, 2013, 157(2): 105-113.
8. Tran DD, Oe PL, de Fijter CW, et al. Acute renal failure in patients with acute pancreatitis: prevalence, risk factors, and outcome. Nephrol Dial Transplant, 1993, 8(10): 1079-1084.
9. Takase K, Takeyama Y, Nishikawa J, et al. Apoptotic cell death of renal tubules in experimental severe acute pancreatitis. Surgery, 1999, 125(4): 411-420.
10. Coca SG, Yalavarthy R, Concato J, et al. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int, 2008, 73(9): 1008-1016.
11. Belcher JM, Edelstein CL, Parikh CR. Clinical applications of biomarkers for acute kidney injury. Am J Kidney Dis, 2011, 57(6): 930-940.
12. Lewington AJ, Sayed A. Acute kidney injury: how do we define it? Ann Clin Biochem, 2010, 47(Pt 1): 4-7.
13. 徐登云, 王云秀. 基于灰度共生矩阵对纹理特征的分析. 西部资源, 2012, (2): 112-114.
14. Hodgdon T, McInnes MD, Schieda N, et al. Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology, 2015, 276(3): 787-796.
15. Yan L, Liu Z, Wang G, et al. Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images. Acad Radiol, 2015, 22(9): 1115-1121.
16. Schieda N, Thornhill RE, Al-Subhi M, et al. Diagnosis of sarcomatoid renal cell carcinoma with CT: Evaluation by qualitative imaging features and texture analysis. AJR Am J Roentgenol, 2015, 204(5): 1013-1023.
17. Kocak B, Durmaz ES, Ates E, et al. Radiogenomics in clear cell renal cell carcinoma: machine learning-based high-dimensional quantitative CT texture analysis in predicting PBRM1 mutation status. AJR Am J Roentgenol, 2019, 212(3): W55-W63.
18. 丁玖乐, 邢兆宇, 陈真, 等. CT纹理分析术前预测肾脏透明细胞癌Fuhrman分级的价值. 中华放射学杂志, 2018, 52(8): 614-618.
19. 李小虎, 裴子璐, 刘云鹏, 等. 肾脏CT容积纹理分析及机器学习相结合的影像组学评价肾透明细胞癌病理分级的价值初探. 中华放射学杂志, 2018, 52(5): 344-348.
20. Ding J, Xing Z, Jiang Z, et al. Evaluation of renal dysfunction using texture analysis based on DWI, BOLD, and susceptibility-weighted imaging. Eur Radiol, 2019, 29(5): 2293-2301.
21. Abbasian Ardakani A, Mohammadi A, Khalili Najafabad B, et al. Assessment of kidney function after allograft transplantation by texture analysis. Iran J Kidney Dis, 2017, 11(2): 157-164.
22. 周海龙, 张古沐阳, 石冰, 等. 定量CT纹理分析鉴别诊断透明细胞型与非透明细胞型肾癌. 中国医学影像技术, 2017, 33(12): 1768-1773.
23. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern, 1973, 3(6): 610-621.
24. Tixier F, Hatt M, Le Rest CC, et al. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med, 2012, 53(5): 693-700.
25. Tang X. Texture information in run-length matrices. IEEE Trans Image Process, 1998, 7(11): 1602-1609.

Previous Article
覆膜支架治疗难治性股动脉假性动脉瘤的探讨
Next Article
Research progress of DNA methylation and cholangiocarcinoma

CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Value of CT enhanced image texture analysis in diagnosis of acute pancreatitis with acute kidney injury

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content