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dc.contributor.authorGlazer, Evan S.
dc.contributor.authorZhang, Hao Helen
dc.contributor.authorHill, Kimberly A.
dc.contributor.authorPatel, Charmi
dc.contributor.authorKha, Stephanie T.
dc.contributor.authorYozwiak, Michael L.
dc.contributor.authorBartels, Hubert
dc.contributor.authorNafissi, Nellie N.
dc.contributor.authorWatkins, Joseph C.
dc.contributor.authorAlberts, David S.
dc.contributor.authorKrouse, Robert S.
dc.date.accessioned2017-02-02T00:32:11Z
dc.date.available2017-02-02T00:32:11Z
dc.date.issued2016-10
dc.identifier.citationEvaluating IPMN and pancreatic carcinoma utilizing quantitative histopathology 2016, 5 (10):2841 Cancer Medicineen
dc.identifier.issn20457634
dc.identifier.pmid27666740
dc.identifier.doi10.1002/cam4.923
dc.identifier.urihttp://hdl.handle.net/10150/622340
dc.description.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.
dc.description.sponsorshipNational 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, AZen
dc.language.isoenen
dc.publisherWILEY-BLACKWELLen
dc.relation.urlhttp://doi.wiley.com/10.1002/cam4.923en
dc.rightsThis is an open access article under the terms of the Creative Commons Attribution License.en
dc.subjectIntraductal papillary mucinous neoplasmsen
dc.subjectkaryometryen
dc.subjectpancreatic carcinomaen
dc.subjectquantitative histopathologyen
dc.titleEvaluating IPMN and pancreatic carcinoma utilizing quantitative histopathologyen
dc.typeArticleen
dc.contributor.departmentUniv Arizonaen
dc.identifier.journalCancer Medicineen
dc.description.noteOpen Access Journal.en
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
dc.contributor.institutionUniversity of Tennessee Health Sciences Center; Memphis Tennessee
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionUniversity of Colorado; Denver Colorado
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionThe University of Arizona; Tucson Arizona
dc.contributor.institutionCMC Veterans Affairs Medical Center; Philadelphia Pennsylvania
refterms.dateFOA2018-06-12T09:20:37Z
html.description.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.


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