Skip to main content

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

Publication ,  Journal Article
Chakraborty, J; Midya, A; Gazit, L; Attiyeh, M; Langdon-Embry, L; Allen, PJ; Do, RKG; Simpson, AL
Published in: Med Phys
November 2018

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.

Duke Scholars

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

November 2018

Volume

45

Issue

11

Start / End Page

5019 / 5029

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Risk Assessment
  • Prognosis
  • Pancreatic Intraductal Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Chakraborty, J., Midya, A., Gazit, L., Attiyeh, M., Langdon-Embry, L., Allen, P. J., … Simpson, A. L. (2018). CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys, 45(11), 5019–5029. https://doi.org/10.1002/mp.13159
Chakraborty, Jayasree, Abhishek Midya, Lior Gazit, Marc Attiyeh, Liana Langdon-Embry, Peter J. Allen, Richard K. G. Do, and Amber L. Simpson. “CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas.Med Phys 45, no. 11 (November 2018): 5019–29. https://doi.org/10.1002/mp.13159.
Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys. 2018 Nov;45(11):5019–29.
Chakraborty, Jayasree, et al. “CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas.Med Phys, vol. 45, no. 11, Nov. 2018, pp. 5019–29. Pubmed, doi:10.1002/mp.13159.
Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ, Do RKG, Simpson AL. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys. 2018 Nov;45(11):5019–5029.

Published In

Med Phys

DOI

EISSN

2473-4209

Publication Date

November 2018

Volume

45

Issue

11

Start / End Page

5019 / 5029

Location

United States

Related Subject Headings

  • Tomography, X-Ray Computed
  • Risk Assessment
  • Prognosis
  • Pancreatic Intraductal Neoplasms
  • Nuclear Medicine & Medical Imaging
  • Image Processing, Computer-Assisted
  • Humans
  • 5105 Medical and biological physics
  • 4003 Biomedical engineering
  • 1112 Oncology and Carcinogenesis