Erschienen in:
18.05.2020 | Pancreatic Tumors
Grading Solid Pseudopapillary Tumors of the Pancreas: the Fudan Prognostic Index
verfasst von:
Feng Yang, MD, PhD, Wenchuan Wu, MD, PhD, Xiaoyi Wang, MD, Qiongyan Zhang, MD, Yun Bao, MD, Zhongwen Zhou, MD, PhD, Chen Jin, MD, PhD, Yuan Ji, MD, PhD, John A. Windsor, MD, FRACS, Wenhui Lou, MD, PhD, Deliang Fu, MD, PhD
Erschienen in:
Annals of Surgical Oncology
|
Ausgabe 1/2021
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Abstract
Background
Ki-67 has been shown to predict outcome of patients with solid pseudopapillary tumor of the pancreas (SPTP) but has not been incorporated into a formal classification system to predict recurrence-free survival (RFS).
Methods
This is a retrospective cohort study of patients with histologically confirmed diagnosis of SPTP who had at least 1 year of follow-up at two tertiary academic centers. Survival data were assessed by Kaplan–Meier method and multivariable Cox regression model. Prognostic performance was compared among various systems.
Results
A total of 193 consecutive patients were included, ranging in age from 12 to 70 years (median 33 years). Seven patients (3.6%) developed tumor recurrence. The 3-, 5-, and 10-year RFS rates were estimated at 96.9%, 96.1%, and 94.8%, respectively. For the AJCC staging system, patients with stage I had similar prognosis to those with stage II. For the ENETS staging system, patients with stage I to III had similar prognosis. Grade based on Ki-67 was superior to both the AJCC and ENETS systems for predicting survival. Multivariate analysis revealed that large tumor size [> 10 cm; hazard ratio (HR), 6.177 95% confidence interval (CI), 1.289–29.603; P = 0.023] and Ki-67 (HR, 17.199 95% CI, 4.001–73.930; P < 0.001) were independent predictors for RFS. The Fudan Prognostic Index based on the combination of Ki-67 and tumor size showed excellent discrimination for RFS and was more accurate and informative than other grading/staging systems.
Conclusion
The Fudan Prognostic Index better predicts RFS compared with either Ki-67 alone or the current AJCC and ENETS TNM-based staging systems.