Literature DB >> 29980829

MRI of pancreatic ductal adenocarcinoma: texture analysis of T2-weighted images for predicting long-term outcome.

Moon Hyung Choi1,2, Young Joon Lee3,4, Seung Bae Yoon5,2, Joon-Il Choi1,2, Seung Eun Jung1,2, Sung Eun Rha1.   

Abstract

PURPOSE: To assess the association between T2-weighted imaging (T2WI) texture-analysis parameters and the pathological aggressiveness or long-term outcomes in pancreatic ductal adenocarcinoma (PDAC) patients.
METHODS: A total of 66 patients (mean age 65.3 ± 9.0 years) who underwent preoperative MRI followed by pancreatectomy for PDAC between 2013 and 2015 were included in this study. A radiologist performed a texture analysis twice on one axial image using commercial software. Differences in the tex parameters, according to pathological factors, were analyzed using a Student's t test or an ANOVA with Tukey's test. Univariate and multivariate Cox proportional hazards regression analyses were used to evaluate the association between tex parameters and recurrence-free survival (RFS) or overall survival (OS).
RESULTS: The mean follow-up time was 18.5 months, and there were 58 recurrences and 39 deaths. The mean of the positive pixel (MPP)-related factors was significantly lower in poorly differentiated tumors than in well-differentiated tumors as well as in cases with perineural invasion. The univariate Cox proportional hazards analysis showed a significant association between the tex parameters and RFS or OS. However, only tumor size was statistically significant after the multivariate analysis. Only tumor size and entropy with medium texture were significantly associated with OS after the multivariate analysis.
CONCLUSIONS: Tumor size was a significant predictive factor for RFS and OS in PDAC patients. Although entropy with medium texture analysis was significantly associated with OS, there were also limitations in the texture analysis; thus, further study is necessary.

Entities:  

Keywords:  Adenocarcinoma; Magnetic resonance imaging; Pancreatic neoplasms; Recurrence

Mesh:

Year:  2019        PMID: 29980829     DOI: 10.1007/s00261-018-1681-2

Source DB:  PubMed          Journal:  Abdom Radiol (NY)


  8 in total

1.  Predicting pathological subtypes and stages of thymic epithelial tumors using DWI: value of combining ADC and texture parameters.

Authors:  Bo Li; Yong-Kang Xin; Gang Xiao; Gang-Feng Li; Shi-Jun Duan; Yu Han; Xiu-Long Feng; Wei-Qiang Yan; Wei-Cheng Rong; Shu-Mei Wang; Yu-Chuan Hu; Guang-Bin Cui
Journal:  Eur Radiol       Date:  2019-03-15       Impact factor: 5.315

2.  MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma.

Authors:  Aydin Eresen; Jia Yang; Junjie Shangguan; Yu Li; Su Hu; Chong Sun; Yury Velichko; Vahid Yaghmai; Al B Benson; Zhuoli Zhang
Journal:  J Transl Med       Date:  2020-02-10       Impact factor: 5.531

3.  Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors.

Authors:  Tahereh Mahmoudi; Zahra Mousavi Kouzahkanan; Amir Reza Radmard; Raheleh Kafieh; Aneseh Salehnia; Amir H Davarpanah; Hossein Arabalibeik; Alireza Ahmadian
Journal:  Sci Rep       Date:  2022-02-23       Impact factor: 4.379

4.  The Feasibility of Combining ADC Value With Texture Analysis of T2WI, DWI and CE-T1WI to Preoperatively Predict the Expression Levels of Ki-67 and p53 of Endometrial Carcinoma.

Authors:  Xueyan Jiang; Haodong Jia; Zhongyuan Zhang; Chao Wei; Chuanbin Wang; Jiangning Dong
Journal:  Front Oncol       Date:  2022-01-20       Impact factor: 6.244

Review 5.  Application of artificial intelligence to pancreatic adenocarcinoma.

Authors:  Xi Chen; Ruibiao Fu; Qian Shao; Yan Chen; Qinghuang Ye; Sheng Li; Xiongxiong He; Jinhui Zhu
Journal:  Front Oncol       Date:  2022-07-22       Impact factor: 5.738

6.  The utility of texture analysis of kidney MRI for evaluating renal dysfunction with multiclass classification model.

Authors:  Yuki Hara; Keita Nagawa; Yuya Yamamoto; Kaiji Inoue; Kazuto Funakoshi; Tsutomu Inoue; Hirokazu Okada; Masahiro Ishikawa; Naoki Kobayashi; Eito Kozawa
Journal:  Sci Rep       Date:  2022-08-30       Impact factor: 4.996

7.  Development and multicenter validation of a CT-based radiomics signature for discriminating histological grades of pancreatic ductal adenocarcinoma.

Authors:  Na Chang; Lingling Cui; Yahong Luo; Zhihui Chang; Bing Yu; Zhaoyu Liu
Journal:  Quant Imaging Med Surg       Date:  2020-03

8.  Development of a Novel Multiparametric MRI Radiomic Nomogram for Preoperative Evaluation of Early Recurrence in Resectable Pancreatic Cancer.

Authors:  Tian-Yu Tang; Xiang Li; Qi Zhang; Cheng-Xiang Guo; Xiao-Zhen Zhang; Meng-Yi Lao; Yi-Nan Shen; Wen-Bo Xiao; Shi-Hong Ying; Ke Sun; Ri-Sheng Yu; Shun-Liang Gao; Ri-Sheng Que; Wei Chen; Da-Bing Huang; Pei-Pei Pang; Xue-Li Bai; Ting-Bo Liang
Journal:  J Magn Reson Imaging       Date:  2019-12-23       Impact factor: 4.813

  8 in total

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