Literature DB >> 21114434

A prediction rule for estimating pancreatic cancer risk in chronic pancreatitis patients with focal pancreatic mass lesions with prior negative EUS-FNA cytology.

Quan-Cai Cai1, Yan Chen, Yi Xiao, Wei Zhu, Qin-Feng Xu, Liang Zhong, Shi-Yao Chen, Min-Min Zhang, Luo-Wei Wang, Zhao-Shen Li.   

Abstract

OBJECTIVE: Considerable false-negative endoscopic ultrasound guided fine needle aspiration (EUS-FNA) findings exist in chronic pancreatitis patients with focal pancreatic mass lesions. Our aim was to develop a prediction rule to stratify risk for pancreatic cancer in chronic pancreatitis patients with focal pancreatic mass lesions with prior negative EUS-FNA cytology.
MATERIAL AND METHODS: A total of 138 eligible consecutive patients were identified from three hospitals between January 2000 and May 2008. A final diagnosis of pancreatic mass lesions was confirmed histologically or verified by a follow-up of at least 12 months. A prediction rule was developed from a logistic regression model by using a regression coefficient-based scoring method, and then internally validated by using bootstrapping.
RESULTS: The rate of pancreatic cancer in the cohort was 18.1%. The prediction rule, which was scored from 0 to 10 points, comprised five variables: sex, mass location, mass number, direct bilirubin, and CA 19-9. Among the 87.7% of patients with low-risk scores (≤ 3), the risk of pancreatic cancer was 13.2%; by comparison, this risk was 52.9% (p < 0.001) among the 12.3% of patients with high-risk scores (> 3). If further invasive tests were used for patients with high risk, 36% of patients with pancreatic cancer would not be missed. The prediction rule had good discrimination (area under the receiver operating characteristic curve, 0.72) and calibration (p = 0.96).
CONCLUSIONS: The prediction rule can provide available risk stratification for pancreatic cancer in chronic pancreatitis patients with focal mass lesions with prior negative EUS-FNA cytology. Application of risk stratification may improve clinical decision making.

Entities:  

Mesh:

Substances:

Year:  2010        PMID: 21114434     DOI: 10.3109/00365521.2010.539256

Source DB:  PubMed          Journal:  Scand J Gastroenterol        ISSN: 0036-5521            Impact factor:   2.423


  5 in total

1.  Independent risk factors for true malignancy in atypical cytologic diagnostic category in EUS-FNA/FNB of the pancreas: A novel prediction model.

Authors:  Ping-Ping Zhang; Teng Wang; Shi-Yu Li; Li Li; Xiao-Ju Su; Pei-Yuan Gu; Yi-Ping Qian; Feng Li; Li Gao; Zhen-Dong Jin; Kai-Xuan Wang
Journal:  Endosc Ultrasound       Date:  2022 May-Jun       Impact factor: 5.275

2.  How early can pancreatic cancer be recognized? A case report and review of the literature.

Authors:  Qiang Nai; Hongxiu Luo; Ping Zhang; Mohammed Amzad Hossain; Ping Gu; Ibrahim W Sidhom; Teena Mathew; Mohammed Islam; Abdalla M Yousif; Shuvendu Sen
Journal:  Case Rep Oncol       Date:  2015-02-03

3.  Improving risk prediction for pancreatic cancer in symptomatic patients: a Saudi Arabian study.

Authors:  Anwar E Ahmed; Faris S Alzahrani; Ahmed M Gharawi; Salman A Alammary; Fahad H Almijmaj; Fahad M Alhusayni; Donna K McClish; Hamdan Al-Jahdali; Ashwaq A Al Olayan; Abdul Rahman Jazieh
Journal:  Cancer Manag Res       Date:  2018-10-25       Impact factor: 3.989

4.  Pancreatic Cancer Prediction Through an Artificial Neural Network.

Authors:  Wazir Muhammad; Gregory R Hart; Bradley Nartowt; James J Farrell; Kimberly Johung; Ying Liang; Jun Deng
Journal:  Front Artif Intell       Date:  2019-05-03

Review 5.  Recent advances in artificial intelligence for pancreatic ductal adenocarcinoma.

Authors:  Hiromitsu Hayashi; Norio Uemura; Kazuki Matsumura; Liu Zhao; Hiroki Sato; Yuta Shiraishi; Yo-Ichi Yamashita; Hideo Baba
Journal:  World J Gastroenterol       Date:  2021-11-21       Impact factor: 5.742

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.