Prognosis Based Definition of Resectability in Pancreatic Cancer: A Road Map to New Guidelines.

放射科 普通外科 胰腺切除术 胰腺
作者
Atsushi Oba,C Croce,Patrick Hosokawa,Cheryl Meguid,Robert J. Torphy,Mohammed Al-Musawi,Steven S. Ahrendt,Ana Gleisner,Richard D. Schulick,Marco Del Chiaro
出处
期刊:Annals of Surgery [Ovid Technologies (Wolters Kluwer)]
被引量:23
标识
DOI:10.1097/sla.0000000000003859
摘要

Objective To identify objective preoperative prognostic factors that are able to predict long-term survival of patients affected by PDAC. Summary of background data In the modern era of improved systemic chemotherapy for PDAC, tumor biology, and response to chemotherapy are essential in defining prognosis and an improved approach is needed for classifying resectability beyond purely anatomic features. Methods We queried the National Cancer Database regarding patients diagnosed with PDAC from 2010 to 2016. Cox proportional hazard models were used to select preoperative baseline factors significantly associated with survival; final models for overall survival (OS) were internally validated and formed the basis of the nomogram. Results A total of 7849 patients with PDAC were included with a median follow-up of 19 months. On multivariable analysis, factors significantly associated with OS included carbohydrate antigen 19-9, neoadjuvant treatment, tumor size, age, facility type, Charlson/Deyo score, primary site, and sex; T4 stage was not independently associated with OS. The cumulative score was used to classify patients into 3 groups: good, intermediate, and poor prognosis, respectively. The strength of our model was validated by a highly significant randomization test, Log-rank test, and simple hazard ratio; the concordance index was 0.59. Conclusion This new PDAC nomogram, based solely on preoperative variables, could be a useful tool to patients and counseling physicians in selecting therapy. This model suggests a new concept of resectability that is meant to reflect the biology of the tumor, thus partially overcoming existing definitions, that are mainly based on tumor anatomic features.
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