Our study established a predictive model to estimating recurrence survival in patients with Pancreatic ductal adenocarcinoma, based on the routinely measured clinical factors and tumor features at CT images available within two weeks before the surgery. Previous predictive models had limited clinical utility as depending on postsurgical pathology findings to some extent [13, 17, 18]. Having a simple preoperative prognostic model could potentially ensure a better selection of optimal candidates for upfront surgery.
The performance of this model was satisfactory in discrimination and calibration aspects in both the development and validation sets. Factors in the model were tumor size (hazard ratio [HR]1.277; P=0.002), tumor density in the portal venous phase (hazard ratio [HR]1.277; P=0.002), suspicious metastatic lymph node(hazard ratio [HR]2.561; P<0.001), peripancreatic infiltration(hazard ratio [HR]4.151; P<0.001) and NLR (hazard ratio [HR]1.111; P=0.020). These imaging and clinical features indicated the tumor development and progression of PDAC. The risk nomogram points reliably predicted discrimination capability of 0.84 calculated from the areas under the receiver operating characteristic curve in the validation set. The five factors we used to predict 1-year recurrence risk are easily acquired in clinical datasets. So, this nomogram can be provided as an accessible tool for clinicians to assess patients’ risk of recurrence. When patients’ risk of recurrence in one year assessed is low, the clinician may suggest upfront surgery. For patients with high risk, neoadjuvant therapy may be needed .
Preoperative NLR as an only clinical factor is in our model. The previous studies have provided evidence that inflammation is participated in outcome in patients with cancer. The neutrophils play important roles in systemic inflammatory response, which promote tumor growth, facilitate tumorigenesis, metastasis and stimulate tumor angiogenesis[20, 21]. According to prior studies, NLR is a predictive marker in survival prognosis of pancreatic invasive carcinoma[21–23]. This relationship could explain the correlation between high NLR and short RFS in current study.
CA19-9, CEA and CA125 are commonly considered as tumor biomarkers for the prognosis of pancreatic cancer, among which CA19-9 is the most valuable factor used for auxiliary diagnosis and recurrence monitoring and correlated with clinical course of disease [24–26]. However, in our study, the CA19-9 is excluded from the predictive model. One reason may be that CA19-9 level can also elevated in some patients with biliary infection, inflammation and obstruction, which confound the survival outcome.
As non-invasive imaging assessment, now contrast-enhanced pancreatic CT scans play an important part when decide treatment regimen for patients with pancreatic cancer. Our study shows that CT characteristics such as tumor size, suspicious metastatic lymph nodes, peripancreatic tumor infiltration, and tumor density in PVP can helping us predicting adverse outcomes, which are independent prognosis factors of RFS.
Several limitations in this study should be acknowledged. First, retrospective single-institution study was more prone to bias than prospective study, despite our efforts to minimize selection bias and avoid bias from missing data. Second, during the follow-up period, the consistency in determining recurrence in each patient is absent. Only a few patients had recurrence masses that were confirmed by pathologic finding. In many other cases, it’s according to symptoms, the increasing of tumor biomarkers or radiologic findings to diagnose disease relapse. In addition, a longer follow-up period is needed and enrolled patients rechecked in scheduled visits should be ensured. Third, in our predictive model, a critical point for distinguish between low-risk and high-risk groups need to furtherly determined.