Development and Validation of a Nomogram to Predict Liver Metastasis in Patients with Pancreatic Ductal Adenocarcinoma: A Large Cohort Study

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

doi:10.2139/ssrn.3266972

Cited by 9 publications

(9 citation statements)

References 28 publications

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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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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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“…First, our report could not incorporate some potentially important indicators, such as BMI, family history of hypertension, smoking and drinking, genetic markers, vascular invasion, surgical margin status and the immunotherapeutic protocols, which were not available in the SEER database (23,(48)(49)(50). Second, the training and validation groups in our study were both derived from the SEER database, and were not externally validated in an independent dataset or our hospital (51).…”

Section: Discussionmentioning

confidence: 99%

A population-based study to predict distant metastasis in patients with renal cell carcinoma

Chen

2021

Ann Palliat Med

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Background: Nomogram is potentially applied for quantitatively evaluating the probability of distant metastasis. The objective of our research was to establish a nomogram to predict distant metastasis in renal cell carcinoma (RCC) patients. Methods: We conducted a retrospective analysis on 37,190 RCC cases diagnosed between 2010 and 2015 in the Surveillance Epidemiology and End Results (SEER) database. A multivariate logistic regression model-based nomogram was applied for predicting the risk factors concerning distant metastasis of RCC individuals. The concordance index (C-index) and calibration curves were utilized to internally validate the discrimination of nomogram. Decision curve analysis (DCA) was applied for comparing the predictive performance and clinically practical values between nomogram and conventional clinicopathologic risk factors. Results: The nomogram incorporated seven clinical variables and achieved a predictive accuracy with a C-index of 0.863. The calibration plots illustrated optimal accordance between model prediction and practical observation. The DCA indicated the nomogram-based clinical utility. Receiver operating characteristic (ROC) curves also demonstrated an area under the curve (AUC) of 0.901 [95% confidence interval (CI): 0.894-0.908] in the training cohort and 0.892 (95% CI: 0.881-0.903) in the testing cohort.Conclusions: Our proposed novel nomogram potentially serves as an accurate and user-friendly clinical tool to predict occurrence of distant metastases in RCC patients.

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“…Nomogram, as a significant medical tool, could not only predict the risk of disease or survival outcomes but also help doctors screen high-risk patients and determine appropriate treatment measures (25,26). The predictive model accurately quantified the influence on each risk by integrating a large number of clinicopathological characteristics (27).…”

Section: Discussionmentioning

confidence: 99%

A novel nomogram based on SEER database for the prediction of liver metastasis in patients with small-cell lung cancer

Lu¹,

Yi²,

Yuan³

et al. 2020

Ann Palliat Med

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Background: To establish and validate a nomogram to predict liver metastasis in patients with small-cell lung cancer (SCLC).Methods: Information on patients diagnosed with SCLC between 2010 and 2015 was retrospectively retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for liver metastasis were identified by logistic regression analyses to construct a nomogram. The predictive accuracy was evaluated by concordance indexes (c-index) and calibration plots, and the comparison of discrimination between the nomogram and other routine staging systems was achieved with the area under receiver operating characteristic curve (AUC) analysis. Decision curve analysis (DCA) was performed to measure the clinical performance of the nomogram.Results: A total of 12,957 patients met our inclusion criteria and were randomly assigned to training (n=6,479) and validation (n=6,478) sets. The nomogram which was established based on independent clinicopathological factors had poor accuracy, and after other distant metastatic sites were added into the predictive model, the new nomogram displayed better discrimination power, with c-indexes of 0.703 in the training set and 0.712 in the validation set. Both internal and external calibration plots approached 45 degrees. The AUCs and net benefit of the predictive model were both higher than those of routine staging systems. Conclusions:The validated nomogram might be a practical tool for clinicians to quantify the risk of liver metastasis in patients with SCLC and improve cancer management.

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Development and Validation of a Nomogram to Predict Liver Metastasis in Patients with Pancreatic Ductal Adenocarcinoma: A Large Cohort Study

Cited by 9 publications

References 28 publications

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

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

A population-based study to predict distant metastasis in patients with renal cell carcinoma

A novel nomogram based on SEER database for the prediction of liver metastasis in patients with small-cell lung cancer

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