&lt;p&gt;Development and validation of a nomogram to predict liver metastasis in patients with pancreatic ductal adenocarcinoma: a large cohort study&lt;/p&gt;

He, Chaobin; Zhong, Liang; Cai, Zhiyuan; Lin, Xiaojun

doi:10.2147/cmar.s200684

Cited by 17 publications

(11 citation statements)

References 37 publications

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“…This result reveals that there are probably other metastases when liver metastases are found. This finding is consistent with other studies [ 23 – 25 ].…”

Section: Discussionsupporting

confidence: 94%

Development and validation of a simple-to-use nomogram to predict liver metastasis in patients with pancreatic neuroendocrine neoplasms: a large cohort study

et al. 2021

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Background Liver metastasis is an important prognostic factor for pancreatic neuroendocrine neoplasms (pNENs), but the relationship between the clinical features of patients with pNEN and liver metastasis remains undetermined. The aim of this study was to establish and validate an easy-to-use nomogram to predict liver-metastasis in patients with pNEN. Methods We obtained the clinicopathologic data of 2960 patients with pancreatic neuroendocrine neoplasms from the Surveillance, Epidemiology and End Results (SEER) database between 2010 and 2016. Univariate and multivariate logistic regression were done to screen out independent influencing factors to establish the nomogram. The calibration plots and the area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of nomogram. Decision curve analysis (DCA) was applied to compare the novel model with the conventional predictive methods. Results A total of 2960 patients with pancreatic neuroendocrine neoplasms were included in the study. Among these, 1974 patients were assigned to the training group and 986 patients to the validation group. Multivariate logistic regression identified, tumor size, grade, other site metastasis, T stage and N stage as independent risk factors. The calibration plot showed good discriminative ability in the training and validation groups, with C-indexes of 0.850 for the training cohort and 0.846 for the validation cohort. The AUC values were 0.850 (95% CI 0.830–0.869) and 0.839 (95% CI 0.812–0.866), respectively. The nomogram total points (NTP) had the potential to stratify patients into low risk, medium risk and high risk (P < 0.001). Finally, comparing the nomogram with traditional prediction methods, the DCA curve showed that the nomogram had better net benefit. Conclusions Our nomogram has a good ability to predict liver metastasis of pancreatic neuroendocrine neoplasms, and it can guide clinicians to provide suitable prevention and treatment measures for patients with medium- and high-risk liver metastasis.

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“…This result reveals that there are probably other metastases when liver metastases are found. This finding is consistent with other studies [ 23 – 25 ].…”

Section: Discussionsupporting

confidence: 94%

Development and validation of a simple-to-use nomogram to predict liver metastasis in patients with pancreatic neuroendocrine neoplasms: a large cohort study

et al. 2021

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“…Study by Cai et al combined age, grade, histology, T stage, lymph node metastasis, and tumor size to predict the metastasis in T1 and T2 gallbladder cancer [ 17 ]. In pancreatic ductal adenocarcinoma, demographic and clinicopathological characteristics were used to construct nomogram to evaluate metastasis [ 18 ]. However, there is still no similar study in osteosarcoma.…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Wang

Liu

et al. 2021

J Orthop Surg Res

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Background Osteosarcoma is most common malignant bone tumors. OS patients with metastasis have a poor prognosis. There are few tools to assess metastasis; we want to establish a nomogram to evaluate metastasis of osteosarcoma. Methods Data from the Surveillance, Epidemiology, and End Results (SEER) database of patients with osteosarcoma were retrieved for retrospective analysis. We identify risk factors through univariate logistic regression and multivariate logistic regression analysis. Based on the results of multivariate analysis, we established a nomogram to predict metastasis of patients with osteosarcoma and used the concordance index (C-index) and calibration curves to test models. Results One thousand fifteen cases were obtained from the SEER database. In the univariate and multivariate logistic regression analysis, age, primary site, grade, T stage, and surgery are risk factors. The nomogram for metastasis was constructed based on these factors. The C-index of the training and validation cohort was 0.754 and 0.716. This means that the nomogram predictions of patients with metastasis are correct, and the calibration plots also show the good prediction performance of the nomogram. Conclusion We successfully develop the nomogram which can reliably predict metastasis in different patients with osteosarcoma and it only required basic information of patients. The nomogram that we developed can help clinicians better predict the metastasis with OS and determine postoperative treatment strategies.

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“…It may be caused by different hormone levels between male and female, which deserved to be confirmed by further molecular mechanism research. Several reports indicated that the tumor with larger size had the higher risk of distant metastasis [ 20 – 22 ]. Lymph node metastasis and higher pathological grades are important predictors for the survival of ICC patients [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Construction of Nomograms for Predicting Lung and Bone Metastases in Patients with Intrahepatic Cholangiocarcinoma and Identification of Patients Who Can Benefit from Chemotherapy

Shi

Zou

Zhang

et al. 2020

Journal of Oncology

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Objective. The purpose of our study is to build nomograms for predicting the possibility of lung metastasis (LM) and bone metastasis (BM) in patients with intrahepatic cholangiocarcinoma (ICC). Methods. 1527 patients diagnosed with ICC between 2010 and 2016 were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Univariable and multivariable logistic regression analyses were used to recognize the predictors of LM and BM, respectively. Then two nomograms were established. We applied the C-index, calibration plot, receiver-operating characteristic (ROC) curve, and decision curve analysis (DCA) to evaluate the novel nomograms. The maximum values of the Youden indexes from the ROC curves were utilized to select the cutoff points of the nomograms. The Kaplan–Meier survival curves were used to evaluate the effect of chemotherapy in different groups. The bootstrap resampling method was chosen for internal validation. Results. Five predictors for LM and three predictors for BM were identified, and two nomograms were constructed. The nomograms had high values of C-indexes, reaching 0.821 (95% CI 0.772–0.871) for LM and 0.759 (95% CI 0.700–0.818) for BM. C-indexes of 0.814 for LM and 0.749 for BM were also observed in internal validation. The calibration plots, ROC curves, and DCAs exhibited favorable performances for predicting LM and BM. The cutoff points of total points in nomograms were 108 for LM and 144 for BM, which could distinguish between high-risk and low-risk groups for LM and BM. Chemotherapy is suggested to undergo for patients in high-risk groups. Conclusions. The nomograms could assess the possibility of LM and BM in ICC patients and determine the optimal treatment.

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<p>Development and validation of a nomogram to predict liver metastasis in patients with pancreatic ductal adenocarcinoma: a large cohort study</p>

Cited by 17 publications

References 37 publications

Development and validation of a simple-to-use nomogram to predict liver metastasis in patients with pancreatic neuroendocrine neoplasms: a large cohort study

Development and validation of a simple-to-use nomogram to predict liver metastasis in patients with pancreatic neuroendocrine neoplasms: a large cohort study

Construction and validation of nomogram to predict distant metastasis in osteosarcoma: a retrospective study

Construction of Nomograms for Predicting Lung and Bone Metastases in Patients with Intrahepatic Cholangiocarcinoma and Identification of Patients Who Can Benefit from Chemotherapy

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