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Invited Critique |

Treatment Strategy for Intraductal Papillary Mucinous Neoplasm of the Pancreas Based on Malignant Predictive Factors—Invited Critique

David B. Adams, MD
Arch Surg. 2009;144(4):349-350. doi:10.1001/archsurg.2008.590.
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The Nobel physicist Sir Ernest Rutherford said, “all science is either physics or stamp collecting.”1 It is difficult to realize that surgical science is frequently closer to stamp collecting than to physics. We examine information from our collection of patients and try to define disease on the basis of the data at hand. Much is missing from the record, and we overcome this with a variety of statistical tools and insightful leaps of imagination. The question goes begging, “What do we know about what we do not know?” With IPMN we constantly need to remind ourselves that there is much we do not know. The conventional wisdom is that branch-duct IPMNs are less aggressive than main-duct IPMNs, malignancy is more common in older patients, malignancy is found in about 70% of resected main-duct IPMNs, recurrence is rare after resection of noninvasive IPMNs, and recurrence occurs in the majority after resection of invasive IPMNs.2 Cyst fluid CEA was found to be the most accurate test to differentiate mucinous from nonmucinous cystic lesions, with an optimal cutoff of 192 ng/mL.3 In this report from Hirono and colleagues, the CEA bar is set at 110 ng/mL as an independent predictive factor for malignancy. Other investigators found cyst fluid CEA to be of limited value in differentiating benign from malignant IPMNs.4 Clearly a better marker for dysplasia and malignancy is needed as an aid to operative selection of patients. Until then, careful history, physical examination, and laboratory and radiologic examination with computed tomography, magnetic resonance imaging, and endoscopic ultrasonography will be used with cyst fluid analysis to assess patient risk for operative and expectant management. The natural course of the disease will be clarified as we gather this information and examine the data past and present to better predict the future behavior of IPMNs.

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