Quantitative imaging to evaluate malignant potential of IPMNs
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Alexander N. Hanania1,2, Leonidas E. Bantis3, Ziding Feng3, Huamin Wang4, Eric P. Tamm5, Matthew H. Katz6, Anirban Maitra4 and Eugene J. Koay2
1 University of Texas Medical School, Houston, TX, USA
2 Department of Radiation Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
3 Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, USA
4 Department of Pathology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
5 Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
6 Department of Surgical Oncology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
Eugene J. Koay, email:
Keywords: IPMN, quantitative imaging, radiomics, fukuoka, pancreatic cyst
Received: July 06, 2016 Accepted: August 18, 2016 Published:August 31, 2016
Objective: To investigate using quantitative imaging to assess the malignant potential of intraductal papillary mucinous neoplasms (IPMNs) in the pancreas.
Background: Pancreatic cysts are identified in over 2% of the population and a subset of these, including intraductal papillary mucinous neoplasms (IPMNs), represent pre-malignant lesions. Unfortunately, clinicians cannot accurately predict which of these lesions are likely to progress to pancreatic ductal adenocarcinoma (PDAC).
Methods: We investigated 360 imaging features within the domains of intensity, texture and shape using pancreatic protocol CT images in 53 patients diagnosed with IPMN (34 “high-grade” [HG] and 19 “low-grade” [LG]) who subsequently underwent surgical resection. We evaluated the performance of these features as well as the Fukuoka criteria for pancreatic cyst resection.
Results: In our cohort, the Fukuoka criteria had a false positive rate of 36%. We identified 14 imaging biomarkers within Gray-Level Co-Occurrence Matrix (GLCM) that predicted histopathological grade within cyst contours. The most predictive marker differentiated LG and HG lesions with an area under the curve (AUC) of .82 at a sensitivity of 85% and specificity of 68%. Using a cross-validated design, the best logistic regression yielded an AUC of 0.96 (σ = .05) at a sensitivity of 97% and specificity of 88%. Based on the principal component analysis, HG IPMNs demonstrated a pattern of separation from LG IPMNs.
Conclusions: HG IPMNs appear to have distinct imaging properties. Further validation of these findings may address a major clinical need in this population by identifying those most likely to benefit from surgical resection.
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