Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Zhang, Wei; Ji, Lichen; Zhong, Xugang; Zhu, Senbo; Zhang, Yi; Meng, Guofeng; Kang, Yao; Bi, Qing

doi:10.3389/fpubh.2022.884349

Cited by 11 publications

(10 citation statements)

References 49 publications

(42 reference statements)

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“…In gastrointestinal tumors such as liver, gastric, and colorectal cancers, the risk of concomitant liver metastases was significantly associated with advanced age, pathological type, poor tumor differentiation, bone metastases, lung metastases, high TNM stage, and unsystematic treatment [ 55 , 56 , 57 ]. In this investigation, we found that the risk variables for PC patients with concomitant liver metastasis were: male, main tumor site, pathological type, grade, T2 stage, N1 stage, metastases in the lungs and bones, larger tumor, and chemotherapy, which was a similar outcome to the results of previous studies [ 25 ]. Previous research has found that radiotherapy is a risk variable for bone metastases from pancreatic cancer; however, in our study, we found that radiotherapy was actually beneficial for patients with liver metastases [ 26 ].…”

Section: Discussionsupporting

confidence: 85%

“…Therefore, clinically relevant information needs to be integrated to determine the risk factors and prognosis of liver metastasis in PC patients. Previous studies have constructed nomogram models of distant metastases in pancreatic cancer patients, including those for bone metastases and lung metastases, to predict risk factors and prognosis, respectively [ 11 , 25 , 26 ]. However, predictive models for liver metastasis in PC patients have not been adequately studied.…”

Section: Introductionmentioning

confidence: 99%

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Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Shi

Zhou

et al. 2023

JPM

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The liver is the most prevalent location of distant metastasis for pancreatic cancer (PC), which is highly aggressive. Pancreatic cancer with liver metastases (PCLM) patients have a poor prognosis. Furthermore, there is a lack of effective predictive tools for anticipating the diagnostic and prognostic techniques that are needed for the PCLM patients in current clinical work. Therefore, we aimed to construct two nomogram predictive models incorporating common clinical indicators to anticipate the risk factors and prognosis for PCLM patients. Clinicopathological information on pancreatic cancer that referred to patients who had been diagnosed between the years of 2004 and 2015 was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate logistic regression analyses and a Cox regression analysis were utilized to recognize the independent risk variables and independent predictive factors for the PCLM patients, respectively. Using the independent risk as well as prognostic factors derived from the multivariate regression analysis, we constructed two novel nomogram models for predicting the risk and prognosis of PCLM patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, the consistency index (C-index), and the calibration curve were then utilized to establish the accuracy of the nomograms’ predictions and their discriminability between groups. Using a decision curve analysis (DCA), the clinical values of the two predictors were examined. Finally, we utilized Kaplan–Meier curves to examine the effects of different factors on the prognostic overall survival (OS). As many as 1898 PCLM patients were screened. The patient’s sex, primary site, histopathological type, grade, T stage, N stage, bone metastases, lung metastases, tumor size, surgical resection, radiotherapy, and chemotherapy were all found to be independent risks variables for PCLM in a multivariate logistic regression analysis. Using a multivariate Cox regression analysis, we discovered that age, histopathological type, grade, bone metastasis, lung metastasis, tumor size, and surgery were all independent prognostic variables for PCLM. According to these factors, two nomogram models were developed to anticipate the prognostic OS as well as the risk variables for the progression of PCLM in PCLM patients, and a web-based version of the prediction model was constructed. The diagnostic nomogram model had a C-index of 0.884 (95% CI: 0.876–0.892); the prognostic model had a C-index of 0.686 (95% CI: 0.648–0.722) in the training cohort and a C-index of 0.705 (95% CI: 0.647–0.758) in the validation cohort. Subsequent AUC, calibration curve, and DCA analyses revealed that the risk and predictive model of PCLM had high accuracy as well as efficacy for clinical application. The nomograms constructed can effectively predict risk and prognosis factors in PCLM patients, which facilitates personalized clinical decision-making for patients.

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Shi

Zhou

et al. 2023

JPM

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“…Similar to previous ndings, we found that age, primary site, degree of differentiation, histological subtype, N stage, surgery, radiotherapy, lung metastasis, and bone metastasis are risk factors for PCLM (17,18). Furthermore, the prognostic factors for PCLM, including age, degree of differentiation, histological subtype, surgery, radiotherapy, chemotherapy, and lung metastases, were largely consistent with those for liver metastases of gastric cancer (19) and rectal cancer (20), bone metastases of pancreatic cancer (21), and lung metastases of PC (22). It was reported that the median survival of patients with untreated distant metastases is no more than 6 months (23) and the median survival of PCLM patients in this study was 5 months for both the SEER cohort and our cohort.…”

Section: Discussionsupporting

confidence: 54%

Construction and Validation of Risk Factors and Prognostic Model for Liver Metastasis of Pancreatic Cancer

Zhang

Shi

Renjun

et al. 2023

Preprint

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Background Pancreatic cancer (PC) is a common malignancy that often metastasizes to the liver. The presence of liver metastasis (LM) in PC significantly impacts treatment selection and prognosis, but factors affecting the occurrence and prognosis of pancreatic cancer with liver metastasis (PCLM) are not well described. Methods Patients diagnosed with PC between 2010 and 2015 were selected from the Surveillance Epidemiology and End Results (SEER) database. Independent risk factors for PCLM were identified using univariable and multivariable logistic regression. Independent prognostic factors affecting the overall survival (OS) of PCLM patients were analyzed by univariate and multivariate Cox regression, and two nomograms were constructed to predict the risk and prognosis of PCLM. Nomograms were evaluated by receiver operating characteristic (ROC) analysis, C-index, calibration plots, and decision curve analysis (DCA). Results Multivariate logistic regression showed that age, primary site, grade, histological subtype, N stage, radiotherapy, surgery, bone metastasis, and lung metastasis were independent risk factors for PCLM. Multivariable COX regression showed that age, grade, histological subtype, surgery, radiotherapy, chemotherapy and lung metastasis were independent prognostic factors for PCLM. Diagnostic and prognostic nomograms were constructed based on the ROC curves, C-index, calibration curves and DCA curves, and both nomograms showed good predictive performance and clinical utility. Conclusion The two nomograms constructed in this study exhibit good predictive performance in the risk and prognosis of PCLM and may thus serve as a guide for future clinical management of PCLM.

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“…For PNET, TNM stage, a classic indicator, still has advantages for pathological staging [ 11 ]. For many cancers, the nomogram grading approach is superior to conventional staging systems and has become a new standard [ 12 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study

Li¹,

Huang²,

Liao³

et al. 2023

BMC Cancer

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Background Pancreatic neuroendocrine tumors (PNETs) are one of the most common endocrine tumors, and liver metastasis (LMs) are the most common location of metastasis from PNETS; However, there is no valid nomogram to predict the diagnosis and prognosis of liver metastasis (LMs) from PNETs. Therefore, we aimed to develop a valid predictive model to aid physicians in making better clinical decisions. Methods We screened patients in the Surveillance, Epidemiology, and End Results (SEER) database from 2010–2016. Feature selection was performed by machine learning algorithms and then models were constructed. Two nomograms were constructed based on the feature selection algorithm to predict the prognosis and risk of LMs from PNETs. We then used the area under the curve (AUC), receiver operating characteristic (ROC) curve, calibration plot and consistency index (C-index) to evaluate the discrimination and accuracy of the nomograms. Kaplan-Meier (K-M) survival curves and decision curve analysis (DCA) were also used further to validate the clinical efficacy of the nomograms. In the external validation set, the same validation is performed. Results Of the 1998 patients screened from the SEER database with a pathological diagnosis of PNET, 343 (17.2%) had LMs at the time of diagnosis. The independent risk factors for the occurrence of LMs in PNET patients included histological grade, N stage, surgery, chemotherapy, tumor size and bone metastasis. According to Cox regression analysis, we found that histological subtype, histological grade, surgery, age, and brain metastasis were independent prognostic factors for PNET patients with LMs. Based on these factors, the two nomograms demonstrated good performance in model evaluation. Conclusion We developed two clinically significant predictive models to aid physicians in personalized clinical decision-makings.

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Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study

Cited by 11 publications

References 49 publications

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis

Construction and Validation of Risk Factors and Prognostic Model for Liver Metastasis of Pancreatic Cancer

Two machine learning-based nomogram to predict risk and prognostic factors for liver metastasis from pancreatic neuroendocrine tumors: a multicenter study

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