The texture analysis of CT images used for the discrimination of nonhypervascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

ZHANG Yongchang , YU Haopeng , LI Mou , HUANG Zixing ,  SONG Bin

Department of Radiology, West China Hospital of Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

SONG Bin, Email: cjr.songbin@vip.163.com

Keywords：

CT images; texture analysis; nonhypervascular pancreatic neuroendocrine tumor; pancreatic ductal adenocarcinoma; differential diagnosis

DOI：

10.7507/1007-9424.201805073

Video：

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Objective To determine feasibility of texture analysis of CT images for the discrimination of nonhypervascular pancreatic neuroendocrine tumor (PNET) from pancreatic ductal adenocarcinoma (PDAC). Methods CT images of 15 pathologically proved as PNETs and 30 PDACs in West China Hospital of Sichuan University from January 2009 to January 2017 were retrospectively analyzed. Results Thirty best texture parameters were automatically selected by the combination of Fisher coefficient (Fisher)+classification error probability combined with average correlation coefficients (PA)+mutual information (MI). The 30 texture parameters of arterial phase (AP) CT images were distributed in co-occurrence matrix (18 parameters), run-length matrix (10 parameters), and autoregressive model (2 parameters). The distribution of parameters in portal venous phase (PVP) were co-occurrence matrix (15 parameters), run-length matrix (10 parameters), histogram (1 parameter), absolute gradient (1 parameter), and autoregressive model (3 parameters). In AP and PVP, the parameter with the highest diagnostic performance were both Teta2, and the area under curve (AUC) value was 0.829 and 0.740 (P<0.001,P=0.009), respectively. By the B11 of MaZda, the misclassification rate of raw data analysis (RDA)/K nearest neighbor classification (KNN), principal component analysis (PCA)/KNN, linear discriminant analysis (LDA)/KNN, and nonlinear discriminant analysis (NDA)/artificial neural network (ANN) was 28.89% (13/45), 28.89% (13/45), 0 (0/45), and 4.44% (2/45), respectively. In PVP, the misclassification rate of RDA/KNN, PCA/KNN, LDA/KNN, and NDA/ANN was 35.56% (16/45), 33.33% (15/45), 4.44% (2/45), and 11.11% (5/45), respectively. Conclusions CT texture analysis is feasible in the discrimination of nonhypervascular PNET and PDAC. Teta2 is the parameter with the highest diagnostic performance, and in AP, LDA/KNN modality has the lowest misclassification rate.

Citation： ZHANG Yongchang, YU Haopeng, LI Mou, HUANG Zixing, SONG Bin. The texture analysis of CT images used for the discrimination of nonhypervascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2018, 25(6): 748-753. doi: 10.7507/1007-9424.201805073 Copy

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15.	Chae HD, Park CM, Park SJ, et al. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology, 2014, 273(1): 285-293.
16.	Dennie C, Thornhill R, Sethi-Virmani V, et al. Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules. Quant Imaging Med Surg, 2016, 6(1): 6-15.
17.	刘露, 吴霜, 伍兵. CT 图像纹理分析鉴别肝上皮样血管内皮瘤与结肠癌肝转移瘤的初步研究. 中国普外基础与临床杂志, 2018, 25(4): 483-487.
18.	Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, et al. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed, 2018, 159: 23-30.
19.	Weiss GJ, Ganeshan B, Miles KA, et al. Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic. PLoS One, 2014, 9(7): e100244.
20.	Manuel-Vazquez A, Ramia JM, Latorre-Fragua R, et al. Pancreatic neuroendocrine tumors and intraductal papillary mucinous neoplasm of the pancreas: a systematic review. Pancreas, 2018, 47(5): 551-555.
21.	Krishna SG, Li F, Bhattacharya A, et al. Differentiation of pancreatic ductal adenocarcinoma from other neoplastic solid pancreatic lesions: a tertiary oncology center experience. Gastrointest Endosc, 2015, 81(2): 370-379.
22.	Takumi K, Fukukura Y, Higashi M, et al. Pancreatic neuroendocrine tumors: correlation between the contrast-enhanced computed tomography features and the pathological tumor grade. Eur J Radiol, 2015, 84(8): 1436-1443.
23.	Luo Y, Dong Z, Chen J, et al. Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification. Eur Radiol, 2014, 24(11): 2945-2952.
24.	Worhunsky DJ, Krampitz GW, Poullos PD, et al. Pancreatic neuroendocrine tumours: hypoenhancement on arterial phase computed tomography predicts biological aggressiveness. HPB (Oxford), 2014, 16(4): 304-311.
25.	Peeken JC, Bernhofer M, Wiestler B, et al. Radiomics in radiooncology－challenging the medical physicist. Phys Med, 2018, 48: 27-36.

1. Gumbs AA, Moore PS, Falconi M, et al. Review of the clinical, histological, and molecular aspects of pancreatic endocrine neoplasms. J Surg Oncol, 2002, 81(1): 45-53.
2. Manfredi R, Bonatti M, Mantovani W, et al. Non-hyperfunctioning neuroendocrine tumours of the pancreas: MR imaging appearance and correlation with their biological behaviour. Eur Radiol, 2013, 23(11): 3029-3039.
3. Cloyd JM, Poultsides GA. Non-functional neuroendocrine tumors of the pancreas: advances in diagnosis and management. World J Gastroenterol, 2015, 21(32): 9512-9525.
4. Wong KP, Tsang JS, Lang BH. Role of surgery in pancreatic neuroendocrine tumor. Gland Surg, 2018, 7(1): 36-41.
5. Lewis RB, Lattin GE Jr, Paal E. Pancreatic endocrine tumors: radiologic-clinicopathologic correlation. Radiographics, 2010, 30(6): 1445-1464.
6. Humphrey PE, Alessandrino F, Bellizzi AM, et al. Non-hyperfunctioning pancreatic endocrine tumors: multimodality imaging features with histopathological correlation. Abdom Imaging, 2015, 40(7): 2398-2410.
7. Kim JH, Eun HW, Kim YJ, et al. Pancreatic neuroendocrine tumour (PNET): staging accuracy of MDCT and its diagnostic performance for the differentiation of PNET with uncommon CT findings from pancreatic adenocarcinoma. Eur Radiol, 2016, 26(5): 1338-1347.
8. Lubner MG, Smith AD, Sandrasegaran K, et al. CT Texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
9. 朱碧云, 陈卉. 医学图像纹理分析的方法及应用. 中国医学装备, 2013, 10(8): 77-81.
10. Castellano G, Bonilha L, Li LM, et al. Texture analysis of medical images. Clin Radiol, 2004, 59(12): 1061-1069.
11. 潘立阳. 原发性肝癌相关因素 1∶2 病例对照研究. 大连: 大连医科大学, 2008.
12. Szczypiński PM, Strzelecki M, Materka A, et al. MaZda－a software package for image texture analysis. Comput Methods Programs Biomed, 2009, 94(1): 66-76.
13. 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.
14. 任继亮, 吴颖为, 陶晓峰. 常规 MRI 纹理分析鉴别诊断眼眶淋巴瘤与炎性假瘤. 中国医学影像技术, 2017, 33(7): 980-984.
15. Chae HD, Park CM, Park SJ, et al. Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology, 2014, 273(1): 285-293.
16. Dennie C, Thornhill R, Sethi-Virmani V, et al. Role of quantitative computed tomography texture analysis in the differentiation of primary lung cancer and granulomatous nodules. Quant Imaging Med Surg, 2016, 6(1): 6-15.
17. 刘露, 吴霜, 伍兵. CT 图像纹理分析鉴别肝上皮样血管内皮瘤与结肠癌肝转移瘤的初步研究. 中国普外基础与临床杂志, 2018, 25(4): 483-487.
18. Ferreira Junior JR, Koenigkam-Santos M, Cipriano FEG, et al. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases. Comput Methods Programs Biomed, 2018, 159: 23-30.
19. Weiss GJ, Ganeshan B, Miles KA, et al. Noninvasive image texture analysis differentiates K-ras mutation from pan-wildtype NSCLC and is prognostic. PLoS One, 2014, 9(7): e100244.
20. Manuel-Vazquez A, Ramia JM, Latorre-Fragua R, et al. Pancreatic neuroendocrine tumors and intraductal papillary mucinous neoplasm of the pancreas: a systematic review. Pancreas, 2018, 47(5): 551-555.
21. Krishna SG, Li F, Bhattacharya A, et al. Differentiation of pancreatic ductal adenocarcinoma from other neoplastic solid pancreatic lesions: a tertiary oncology center experience. Gastrointest Endosc, 2015, 81(2): 370-379.
22. Takumi K, Fukukura Y, Higashi M, et al. Pancreatic neuroendocrine tumors: correlation between the contrast-enhanced computed tomography features and the pathological tumor grade. Eur J Radiol, 2015, 84(8): 1436-1443.
23. Luo Y, Dong Z, Chen J, et al. Pancreatic neuroendocrine tumours: correlation between MSCT features and pathological classification. Eur Radiol, 2014, 24(11): 2945-2952.
24. Worhunsky DJ, Krampitz GW, Poullos PD, et al. Pancreatic neuroendocrine tumours: hypoenhancement on arterial phase computed tomography predicts biological aggressiveness. HPB (Oxford), 2014, 16(4): 304-311.
25. Peeken JC, Bernhofer M, Wiestler B, et al. Radiomics in radiooncology－challenging the medical physicist. Phys Med, 2018, 48: 27-36.

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The texture analysis of CT images used for the discrimination of nonhypervascular pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas

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