3 years ago

Retrospective Study of the Correlation Between Pathological Tumor Size and Survival After Curative Resection of T3 Pancreatic Adenocarcinoma: Proposal for Reclassification of the Tumor Extending Beyond the Pancreas Based on Tumor Size

Hiroki Yamaue, Minako Nagai, Yoshiaki Murakami, Goro Honda, Sohei Satoi, for the Multicenter Study Group of Pancreatobiliary Surgery (MSG-PBS), Michiaki Unno, Masahiko Gosho, Fuyuhiko Motoi, Takumi Fukumoto, Ippei Matsumoto, Hiroaki Yanagimoto, Manabu Kawai, Kenichiro Uemura, Masanao Kurata, Masayuki Sho



Even though most patients who undergo resection of pancreatic adenocarcinoma have T3 disease with extra-pancreatic tumor extension, T3 disease is not currently classified by tumor size. The aim of this study was to modify the current TNM classification of pancreatic adenocarcinoma to reflect the influence of tumor size.


A total of 847 consecutive pancreatectomy patients were recruited from multiple centers. Optimum tumor size cutoff values were calculated by receiver operating characteristics analysis for tumors limited to the pancreas (T1/2) and for T3 tumors. In our modified TNM classification, stage II was divided into stages IIA (T3aN0M0), IIB (T3bN0M0), and IIC (T1-3bN1M0) using tumor size cutoff values. The usefulness of the new classification was compared with that of the current classification using Akaike’s information criterion (AIC).


The optimum tumor size cutoff value distinguishing T1 and T2 was 2 cm, while T3 was divided into T3a and T3b at a tumor size of 3 cm. The median survival time of the stages IIA, IIB, and IIC were 44.7, 27.6, and 20.3 months, respectively. There were significant differences of survival between stages IIA and IIB (P = 0.02) and between stages IIB and IIC (P = 0.03). The new classification showed better performance compared with the current classification based on the AIC value.


This proposed new TNM classification reflects the influence of tumor size in patients with extra-pancreatic tumor extension (T3 disease), and the classification is useful for predicting mortality.

Publisher URL: https://link.springer.com/article/10.1007/s00268-017-4077-5

DOI: 10.1007/s00268-017-4077-5

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