BMC Cancer (Aug 2023)

A neoadjuvant therapy compatible prognostic staging for resected pancreatic ductal adenocarcinoma

  • Lingyu Zhu,
  • Shuo Shen,
  • Huan Wang,
  • Guoxiao Zhang,
  • Xiaoyi Yin,
  • Xiaohan Shi,
  • Suizhi Gao,
  • Jiawei Han,
  • Yiwei Ren,
  • Jian Wang,
  • Hui Jiang,
  • Shiwei Guo,
  • Gang Jin

DOI
https://doi.org/10.1186/s12885-023-11181-x
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 12

Abstract

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Abstract Objective To improve prediction, the AJCC staging system was revised to be consistent with upfront surgery (UFS) and neoadjuvant therapy (NAT) for PDAC. Background The AJCC staging system was designed for patients who have had UFS for PDAC, and it has limited predictive power for patients receiving NAT. Methods We examined 146 PDAC patients who had resection after NAT and 1771 who had UFS at Changhai Hospital between 2012 and 2021. The clinicopathological factors were identified using Cox proportional regression analysis, and the Neoadjuvant Therapy Compatible Prognostic (NATCP) staging was developed based on these variables. Validation was carried out in the prospective NAT cohort and the SEER database. The staging approach was compared to the AJCC staging system regarding predictive accuracy. Results The NAT cohort’s multivariate analysis showed that tumor differentiation and the number of positive lymph nodes independently predicted OS. The NATCP staging simplified the AJCC stages, added tumor differentiation, and restaged the disease based on the Kaplan-Meier curve survival differences. The median OS for NATCP stages IA, IB, II, and III was 31.7 months, 25.0 months, and 15.8 months in the NAT cohort and 30.1 months, 22.8 months, 18.3 months, and 14.1 months in the UFS cohort. Compared to the AJCC staging method, the NATCP staging system performed better and was verified in the validation cohort. Conclusions Regardless of the use of NAT, NATCP staging demonstrated greater predictive abilities than the existing AJCC staging approach for resected PDAC and may facilitate clinical decision-making based on accurate prediction of patients’ OS.

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