Nomogram for Predicting Preoperative Regional Lymph Nodes Metastasis in Patients with Metaplastic Breast Cancer: A SEER Population-Based Study

Zhang, Mi; Wang, Biyuan; Liu, Na; Wang, Hui; Zhang, Juan; Wu, Lei; Zhao, Andi; Wang, Le; Zhao, Xianda; Yang, Jin

doi:10.21203/rs.3.rs-554799/v1

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…While nomogram can be used to estimate the distantmetastasis risk by integrating the existing clinicopathological factors, improving diagnostic efficiency, reducing time, and economic cost. [21,22] So it is necessary to establish a simple and sensitive prediction model for distant metastasis in MBC patients. The study established a risk prediction model based on the existing evidence to assess the risk of distant metastasis and screen high-risk groups, so as to guide diagnosis and treatment.…”

Section: Discussionmentioning

confidence: 99%

Nomogram for predicting distant metastasis of male breast cancer: A SEER population-based study

Wang

Yang

et al. 2022

Medicine

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The main purpose of this study was to build a prediction model for male breast cancer (MBC) patients to predict the possibility of distant metastasis. The Surveillance, Epidemiology, and End Results database was used to obtain data on patients with MBC. The patients were divided into a training set and a validation set at a ratio of 7:3. The risk variables of distant metastasis in the training set were determined by univariate and multivariate logistic regression analyses. And then we integrated those risk factors to construct the nomogram. The prediction nomogram was further verified in the verification set. The discrimination and calibration of the nomogram were evaluated by the area under the receiver operating characteristic curve, calibration plots, respectively. A total of 1974 patients (1381 in training set and 593 in validation set) were eligible for final inclusion, of whom 149 (7.55%) had distant metastasis at the diagnosed time. Multivariate logistic regression analyses presented that age, T stage, N stage, and hormone receptor status were independent risk factors for distant metastasis at initial diagnosis of male breast cancer. Finally, the 4 variables were combined to construct the nomogram. The area under the curve values for the nomogram established in the training set and validation set were 0.8224 (95%CI: 0.7796–0.8652) and 0.8631 (95%CI: 0.7937–0.9326), suggesting that the nomogram had good predictive power. The calibration plots illustrated an acceptable correlation between the prediction by nomogram and the actual observation, as the calibration curve was closed to the diagonal bisector line. An easy-to-use nomogram, being proven to be with reliable discrimination ability and accuracy, was established to predict distant metastasis for male patients with breast cancer using the easily available risk factors.

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Section: Discussionmentioning

confidence: 99%

Nomogram for predicting distant metastasis of male breast cancer: A SEER population-based study

Wang

Yang

et al. 2022

Medicine

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“…The survival prediction model showed good performance in estimating the prognosis individually. For instance, compared with similar predictive models (30,31), the net benefit of DCA plots in our nomogram was larger, which indicated that the nomogram was a reliable clinical tool for predicting survival.…”

Section: Survival Analysesmentioning

confidence: 80%

A novel nomogram predicting cancer-specific survival in small cell lung cancer patients with brain metastasis

Rong¹,

Zhu²,

Wu³

2022

Transl Cancer Res

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Background: Brain metastasis (BM) is one of the most common metastatic sites in patients with small cell lung cancer (SCLC), and the prognosis remains very poor. This study aimed to establish a novel nomogram for predicting the cancer-specific survival (CSS) in SCLC patients with BM. Methods: SCLC patients with BM from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015 were retrospectively collected. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors, which were further used to construct the prognostic nomogram. The discrimination and calibration of nomogram were evaluated by concordance index (C-index), receiver operating characteristic (ROC) curve, the area under ROC curve (AUC) and calibration plot. Decision curve analysis (DCA) was used to assess the clinical usefulness. Kaplan-Meier survival curve was applied to analyze the survival outcome. Results: A total of 2,462 patients were enrolled in this study, and randomly assigned into training cohort (n=1,723) and validation cohort (n=739). Age, N stage, surgery, radiation, chemotherapy, bone metastasis, liver metastasis and lung metastasis were identified as independent prognostic factors of CSS. The C-indexes of nomogram was 0.683 [95% confidence interval (CI): 0.667-0.699] in the training cohort, and 0.659 (95% CI: 0.634-0.684) in the validation cohort. The AUC values of 6-, 9-and 12-month CSS were 0.723, 0.742 and 0.737 respectively in the training cohort, while 0.715, 0.737 and 0.739 in the validation cohort. The ROC, calibration and DCA curves showed good discrimination, calibration and clinical applicability of this nomogram in predicting prognosis. Moreover, patients in high-risk group had a worse survival outcome than patients in medium-risk and low-risk groups. Conclusions: A novel nomogram was constructed and validated for predicting individual prognosis in SCLC patients with BM. This nomogram could help clinicians make effective treatment strategies for patients.

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Nomogram for Predicting Preoperative Regional Lymph Nodes Metastasis in Patients with Metaplastic Breast Cancer: A SEER Population-Based Study

Cited by 2 publications

References 22 publications

Nomogram for predicting distant metastasis of male breast cancer: A SEER population-based study

Nomogram for predicting distant metastasis of male breast cancer: A SEER population-based study

A novel nomogram predicting cancer-specific survival in small cell lung cancer patients with brain metastasis

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