Share：
Share this content in WeChat
X
[Chinese][PDF]942104
• Clinical Article •
The value-based T2 histogram analysis for differential diagnosis in solid pancreatic lesions
PENG Lin  ZHA Yunfei  ZENG Feifei  LIU Baiyu  YAN Yuchen 

Cite this article as: Peng L, Zha YF, Zeng FF, et al. The value-based T2 histogram analysis for differential diagnosis in solid pancreatic lesions. Chin J Magn Reson Imaging, 2020, 11(3): 201-206. DOI:10.12015/issn.1674-8034.2020.03.008.


[Abstract] Objective: To investigate the diagnostic value of 3.0 T magnetic resonance imaging (T2WI) grayscale histogram texture features in solid pancreatic lesions.Materials and Methods: The clinical and MRI imaging data of 117 patients with solid pancreatic mass were retrospectively analyzed, 69 cases of ductal adenocarcinoma, 12 cases of solid pseudopapillary, 15 cases of neuroendocrine tumor and 21 cases of mass pancreatitis were confirmed by surgical pathology. They were divided into malignant tumor group (ductal adenocarcinoma), benign to low-grade malignant tumor group (solid pseudopapilloma and neuroendocrine tumor), and non-tumor group (mass pancreatitis). The region of interest (ROI) was delineated at the maximum lesion on T2WI sequence using GE Omni-kinetics software and automatically generate grayscale texture parameters of straight, comparison between the 3 groups using one-way analysis of variance (ANOVA), Pairwise comparisons between groups were made by LSD-t-test (homogeneity of variance) or by Kruskal-walls test of multiple independent samples (heterogeneity of variance). Parameters with a statistical difference were screened, and the ROC curve was drawn to evaluate the efficacy of differential diagnosis of solid pancreatic lesions.Results: Mean, Variance, Energy, Entropy, 5th percentile, 10th percentile, 25th percentile, 50th percentile, 75th percentile, 90th percentile, and 95th percentile were statistically significant (all P<0.05). There was no statistical difference in skewness and kurtosis between the three groups (all P>0.05). The sensitivity of variance to differentiate PDAC and MFCP was 82.6%, the specificity was 85.7%, the area under the curve was 0.899, and the best cut-off value was 5915.87. The sensitivity of mean to differentiate PDAC and SPT+pNET was 64.4%, the specificity was 87.0% and the area under the curve was 0.688, the cut-off was 1113.55. The sensitivity of 90th percentile to differentiate MFCP and SPT+pNET was 88.9%, the specificity was 85.7% and the area under the curve was 0.924, the cut-off was 837.59. They had a high identification efficiency.Conclusions: There are significant differences in the texture parameters of the T2WI histogram between solid pancreatic lesions. Mean and Percentile have significant clinical value in the qualitative and benign or malignant differentiation of solid pancreatic lesions.
[Keywords] solid lesions；the pancreas；magnetic resonance imaging；texture analysis；diagnostic value

PENG Lin Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China

ZHA Yunfei* Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China

ZENG Feifei Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China

LIU Baiyu Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China

YAN Yuchen Department of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China

*Correspondence to: Zha YF, E-mail: zhayunfei999@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  The National Natural Science Foundation of China No. 81871332
Received  2019-09-17
Accepted  2019-11-21
DOI: 10.12015/issn.1674-8034.2020.03.008
Cite this article as: Peng L, Zha YF, Zeng FF, et al. The value-based T2 histogram analysis for differential diagnosis in solid pancreatic lesions. Chin J Magn Reson Imaging, 2020, 11(3): 201-206. DOI:10.12015/issn.1674-8034.2020.03.008.

[1]
Chantarojanasiri T, Kongkam P. Endoscopic ultrasound elastography for solid pancreatic lesions. World J Gastrointest Endosc, 2017, 9(10): 506-513.
[2]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446.
[3]
于泳，丁玉芹，花村，等．多期增强MRI和表观扩散系数直方图鉴别肝内胆管囊腺瘤及囊腺癌的价值．中华放射学杂志, 2018, 52(6): 442-446.
[4]
张竹伟，华婷，徐婷婷等．常规MRI纹理分析鉴别乳腺良、恶性病变的价值初探．中华放射学杂志, 2017, 51(8): 588-591.
[5]
胡征宇，沈起钧，冯湛，等. CT纹理分析在量化胰腺囊腺瘤影像表型中的诊断价值．中华胰腺病杂志, 2017, 17(5): 330-334.
[6]
黄子星，李谋，于浩鹏，等. CT图像纹理分析鉴别不典型胰腺实性假乳头状肿瘤与胰腺导管腺癌的初步研究．中国普外基础与临床杂志, 2018, 25(10): 1249-1253.
[7]
van der Pol CB, Lee S, Tsai S, et al. Differentiation of pancreatic neuroendocrine tumors from pancreas renal cell carcinoma metastases on CT using qualitative and quantitative features. Abdominal Radiology, 2019, 44(3): 992-999.
[8]
Huang Z, Li M, He D, et al. Two-dimensional texture analysis based on CT images to differentiate pancreatic lymphoma and pancreatic adenocarcinoma: a preliminary study. Acad Radiol, 2019, e189-e195.
[9]
Li J, Lu J, Liang P, et al. Differentiation of atypical pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas: using whole-tumor CT texture analysis as quantitative biomarkers. Cancer Med, 2018, 7(10): 4924-4931.
[10]
Guo C, Zhuge X, Wang Q, et al. The differentiation of pancreatic neuroendocrine carcinoma from pancreatic ductal adenocarcinoma: the values of CT imaging features and texture analysis. Cancer Imaging, 2018, 18(1): 37.
[11]
梁萌，赵丽，马霄虹，等．囊液CT纹理分析在鉴别胰腺浆液性囊腺瘤与黏液性囊性肿瘤中的价值．实用放射学杂志, 2018, 34(9): 1381-1385.
[12]
王波涛，何蕾，刘刚，等．磁共振成像纹理特征分析在胰腺浆液性囊腺瘤及黏液性囊腺瘤鉴别诊断中的价值．中国医学科学院学报, 2018, 40(2): 187-193.
[13]
Guo C, Zhuge X, Wang Z, et al. Textural analysis on contrast-enhanced CT in pancreatic neuroendocrine neoplasms: association with WHO grade. Abdominal Radiology, 2019, 44(2): 576-585.
[14]
D Onofrio M, Ciaravino V, Cardobi N, et al. CT enhancement and 3D texture analysis of pancreatic neuroendocrine neoplasms. Sci Rep, 2019, 9(1): 2176.
[15]
Choi TW, Kim JH, Yu MH, et al. Pancreatic neuroendocrine tumor: prediction of the tumor grade using CT findings and computerized texture analysis. Acta Radiologica, 2017, 59(4): 383-392.
[16]
Choi MH, Lee YJ, Yoon SB, et al. MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome. Abdominal Radiology, 2019, 44(1): 122-130.
[17]
Attiyeh MA, Chakraborty J, Gazit L, et al. Preoperative risk prediction for intraductal papillary mucinous neoplasms by quantitative CT image analysis. HPB (Oxford), 2019, 21(2): 212-218.
[18]
Numata K, Ozawa Y, Kobayashi N, et al. Contrast-enhanced sonography of pancreatic carcinoma: correlations with pathological findings. J Gastroenterol, 2005, 40(6): 631-640.
[19]
朱晨迪，张勇，程敬亮，等. MRI灰度直方图分析在儿童后颅窝常见肿瘤中的鉴别诊断价值．放射学实践, 2018, 33(3): 285-289.
[20]
Lu SS, Kim SJ, Kim N, et al. Histogram analysis of apparent diffusion coefficient maps for differentiating primary CNS lymphomas from tumefactive demyelinating lesions. AJR Am J Roentgenol, 2015, 204(4): 827-834.

PREV Predicting microvascular invasion of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature
NEXT Diagnosis and clinical value of magnetic resonance imaging in scar pregnancy
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn