Evaluating IPMN and pancreatic carcinoma utilizing quantitative histopathology
AuthorGlazer, Evan S.
Zhang, Hao Helen
Hill, Kimberly A.
Kha, Stephanie T.
Yozwiak, Michael L.
Nafissi, Nellie N.
Watkins, Joseph C.
Alberts, David S.
Krouse, Robert S.
KeywordsIntraductal papillary mucinous neoplasms
MetadataShow full item record
CitationEvaluating IPMN and pancreatic carcinoma utilizing quantitative histopathology 2016, 5 (10):2841 Cancer Medicine
RightsCopyright © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License.
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AbstractIntraductal papillary mucinous neoplasms (IPMN) are pancreatic lesions with uncertain biologic behavior. This study sought objective, accurate prediction tools, through the use of quantitative histopathological signatures of nuclear images, for classifying lesions as chronic pancreatitis (CP), IPMN, or pancreatic carcinoma (PC). Forty-four pancreatic resection patients were retrospectively identified for this study (12 CP; 16 IPMN; 16 PC). Regularized multinomial regression quantitatively classified each specimen as CP, IPMN, or PC in an automated, blinded fashion. Classification certainty was determined by subtracting the smallest classification probability from the largest probability (of the three groups). The certainty function varied from 1.0 (perfectly classified) to 0.0 (random). From each lesion, 180 +/- 22 nuclei were imaged. Overall classification accuracy was 89.6% with six unique nuclear features. No CP cases were misclassified, 1/16 IPMN cases were misclassified, and 4/16 PC cases were misclassified. Certainty function was 0.75 +/- 0.16 for correctly classified lesions and 0.47 +/- 0.10 for incorrectly classified lesions (P = 0.0005). Uncertainty was identified in four of the five misclassified lesions. Quantitative histopathology provides a robust, novel method to distinguish among CP, IPMN, and PC with a quantitative measure of uncertainty. This may be useful when there is uncertainty in diagnosis.
NoteOpen Access Journal.
VersionFinal published version
SponsorsNational Cancer Institute (Arizona Cancer Center) [CA023074]; National Institutes of Health, Bethesda, MD [T35HL007479]; National Science Foundation, Arlington, VA [NSF DMS-1309507, NSF DMS-1418172]; Graduate Medical Education Office at the University of Arizona; Jim Click Family Foundation, Tucson, AZ; J. Russell Skelton Family, Phoenix, AZ
Except where otherwise noted, this item's license is described as Copyright © 2016 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License.
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