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. 2022 Sep 21:12:952531.
doi: 10.3389/fonc.2022.952531. eCollection 2022.

Risk factors and survival prediction of pancreatic cancer with lung metastases: A population-based study

Zong-Xi Yao  1 , Jun-Hao Tu  1 , Bin Zhou  1 , Yang Huang  1 , Yu-Lin Liu  1 , Xiao-Feng Xue  2
Affiliations

Risk factors and survival prediction of pancreatic cancer with lung metastases: A population-based study

Zong-Xi Yao et al. Front Oncol. .

Abstract

Background: The risk and prognosis of pancreatic cancer with lung metastasis (PCLM) are not well-defined. Thus, this study aimed to identify the risk and prognostic factors for these patients, and establish predictive nomogram models.

Methods: Patients diagnosed with PCLM between 2010 and 2016 were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors and prognostic factors were identified using logistic regression and Cox regression analyses. Nomograms were constructed to predict the risk and survival of PCLM, and the area under the curve (AUC), C-index, and calibration curve were used to determine the predictive accuracy and discriminability of the established nomogram, while the decision curve analysis was used to confirm the clinical effectiveness.

Results: A total of 11287 cases with complete information were included; 601 (5.3%) patients with PC had lung metastases. Multivariable logistic analysis demonstrated that primary site, histological subtype, and brain, bone, and liver metastases were independent risk factors for lung metastases. We constructed a risk prediction nomogram model for the development of lung metastases among PC patients. The c-index of the established diagnostic nomogram was 0.786 (95%CI 0.726-0.846). Multivariable Cox regression analysis demonstrated that primary site, liver metastases, surgery, and chemotherapy were independent prognostic factors for both overall survival (OS) and cancer-specific survival (CSS), while bone metastases were independent prognostic factors for CSS. The C-indices for the OS and CSS prediction nomograms were 0.76 (95% CI 0.74-0.78) and 0.76 (95% CI 0.74-0.78), respectively. Based on the AUC of the receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analysis (DCA), we concluded that the risk and prognosis model of PCBM exhibits excellent performance.

Conclusions: The present study identified the risk and prognostic factors of PCLM and further established nomograms, which can help clinicians effectively identify high-risk patients and predict their clinical outcomes.

Keywords: SEER database; decision curve analysis; lung metastases; nomogram; pancreatic cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Forest plot showing the potential risk factor for lung metastasis among pancreatic cancer patients based on the logistic regression analysis.
Figure 2
Figure 2
Nomogram to estimate the risk of lung metastasis in patients with pancreatic cancer (A). The area under ROC curve was utilized to judge the advantages and disadvantages of nomogram (B). Calibration plot for the diagnostic nomogram. The diagonal 45-degree line indicates perfect prediction (C). Decision curve analysis for the diagnostic nomogram (D). HP, head of pancreas; BP, body of pancreas; OLP, overlapping lesion of pancreas; TP, tail of pancreas; AC, Adenocarcinoma; IDC, Infiltrating duct carcinoma.
Figure 3
Figure 3
Kaplan–Meier curves comparing OS (A) and CSS (B) stratified by the absence or presence of lung metastases among PC patients. LM, Lung metastasis.
Figure 4
Figure 4
Prognostic nomogram to predict OS (A) and CSS (B) for the 6-, 12-, and 24-month survival for PCLM patients.
Figure 5
Figure 5
Calibration plot (A: OS, B: CSS) for prognostic nomograms.
Figure 6
Figure 6
Area under ROC curve (A, left: OS, right: CSS), and DCA (B, left: OS, right: CSS) for prognostic nomograms.
See this image and copyright information in PMC

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