CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas Journal Article

Authors: Chakraborty, J.; Midya, A.; Gazit, L.; Attiyeh, M.; Langdon-Embry, L.; Allen, P. J.; Do, R. K. G.; Simpson, A. L.
Article Title: CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas
Abstract: Purpose: Intraductal papillary mucinous neoplasms (IPMNs) are radiographically visible precursor lesions of pancreatic cancer. Despite standard criteria for assessing risk, only 18% of cysts are malignant at resection. Thus, a large number of patients undergo unnecessary invasive surgery for benign disease. The ability to identify IPMNs with low or high risk of transforming into invasive cancer would optimize patient selection and improve surgical decision-making. The purpose of this study was to investigate quantitative CT imaging features as markers for objective assessment of IPMN risk. Methods: This retrospective study analyzed pancreatic cyst and parenchyma regions extracted from CT scans in 103 patients to predict IPMN risk. Patients who underwent resection between 2005 and 2015 with pathologically proven branch duct (BD)-IPMN and a preoperative CT scan were included in the study. Expert pathologists categorized IPMNs as low or high risk following resection as part of routine clinical care. We extracted new radiographically inspired features as well as standard texture features and designed prediction models for the categorization of high- and low-risk IPMNs. Five clinical variables were also combined with imaging features to design prediction models. Results: Using images from 103 patients and tenfold cross-validation technique, the novel radiographically inspired imaging features achieved an area under the receiver operating characteristic curve (AUC) of 0.77, demonstrating their predictive power. The combination of these features with clinical variables obtained the best performance (AUC = 0.81). Conclusion: The present study demonstrates that features extracted from pretreatment CT images can predict the risk of IPMN. Development of a preoperative model to discriminate between low-risk and high-risk IPMN will improve surgical decision-making. © 2018 American Association of Physicists in Medicine
Keywords: risk stratification; image processing; intraductal papillary mucinous neoplasms; texture analysis; random forest classifier
Journal Title: Medical Physics
Volume: 45
Issue: 11
ISSN: 0094-2405
Publisher: American Association of Physicists in Medicine  
Date Published: 2018-11-01
Start Page: 5019
End Page: 5029
Language: English
DOI: 10.1002/mp.13159
PUBMED: 30176047
PROVIDER: scopus
Notes: Article -- Export Date: 3 December 2018 -- Source: Scopus
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MSK Authors
  1. Peter Allen
    440 Allen
  2. Kinh Gian Do
    115 Do
  3. Amber L Simpson
    51 Simpson
  4. Marc   Attiyeh
    22 Attiyeh
  5. Lior Gazit
    6 Gazit
  6. Abhishek Midya
    9 Midya