Dataset Information

A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program.

ABSTRACT: Background: Patients with early stage breast cancer with lymph nodes metastasis were proven to have more aggressive biologically phenotypes. This study aimed to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer. Methods: We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivariate logistic regressions were carried out to assess the relationships between lymph node metastasis and clinicopathological characteristics. A nomogram was developed and validated by a calibration curve and receptor operating characteristic curve analysis. Result: Age, race, tumour size, tumour primary site, pathological grade, oestrogen receptor (ER) status, progesterone receptor (PR) status and human epidermal growth factor receptor 2 (HER2) status were independent predictive factors of positive lymph node metastasis in T1 breast cancer. Increasing age, tumour size and pathological grade were positively correlated with the risk of lymph node metastasis. We developed a nomogram to predict lymph node metastasis and further validated it in a validation set, with areas under the receiver operating characteristic curves of 0.733 and 0.741 in the training and validation sets, respectively. Conclusions: A better understanding of the clinicopathological characteristics of T1 breast cancer patients might important for assessing their lymph node status. The nomogram developed here, if further validated in other large cohorts, might provide additional information regarding lymph node metastasis. Together with sentinel lymph node biopsy, this nomogram can help comprehensively predict lymph node metastasis.

SUBMITTER: Zhao YX

PROVIDER: S-EPMC6584352 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program.

Zhao Ya-Xin YX Liu Yi-Rong YR Xie Shao S Jiang Yi-Zhou YZ Shao Zhi-Ming ZM

Journal of Cancer 20190527 11

<b>Background:</b> Patients with early stage breast cancer with lymph nodes metastasis were proven to have more aggressive biologically phenotypes. This study aimed to build a nomogram to predict lymph node metastasis in patients with T1 breast cancer. <b>Methods:</b> We identified female patients with T1 breast cancer diagnosed between 2010 and 2014 in the Surveillance, Epidemiology and End Results database. The patients were randomized into training and validation sets. Univariate and multivar ...[more]

PMID: 31258749

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Project description:BackgroundPatients with T1 stage early colorectal cancer (CRC) can be treated with radical surgery or endoscopic surgery. Endoscopic surgery has a number of advantages, including minimal trauma and a rapid recovery. However, it cannot remove regional lymph nodes to assess whether there is lymph node metastasis. Thus, the analysis of the risk factors of lymph node metastasis in patients with T1 stage CRC is of great significance in the selection of appropriate treatment methods. Although previous studies have explored the risk factors for lymph node metastasis in T1 stage CRC patients, the number of cases were relatively insufficient, and further exploration is necessary.MethodsA total of 2,085 patients who had been pathologically diagnosed with CRC from 2015 to 2017 from the Surveillance, Epidemiology, and End Results (SEER) database. Among the patients, 324 had lymph node metastasis. A multivariate logistic regression analysis was conducted to analyze the risk factors of lymph node metastasis in patients with T1 stage CRC. Next, we established a prediction model to predict lymph node metastasis in patients with T1 stage CRC.ResultsThe results of the multivariate logistic regression analysis showed that age at diagnosis, rectosigmoid cancer, poorly differentiated or undifferentiated tumor cells, and distant metastasis were independent factors of lymph node metastasis in patients with T1 stage CRC (P<0.05). This study used the R4.0.3 statistical software for the statistical analysis. The data set was randomly divided into a training set and verification set. The training set comprised 1,460 patients, and the verification set comprised 625 patients. The area under the receiver operating characteristic curve (AUC) of the training set was 0.675 [95% confidence interval (CI): 0.635-0.714], and the AUC of the verification set was 0.682 (95% CI: 0.617-0.747). In the validation set, the model was tested by the Hosmer-Lemeshow Goodness-of-Fit Test (χ2=4.018, P=0.855), and the results showed that the model was reliable at predicting lymph node metastasis in patients with T1 stage CRC.ConclusionsFor CRC patients with high risk factors of lymph node metastasis, endoscopic physicians should carefully evaluate the advantages and disadvantages of the endoscopic surgery before deciding whether to perform this surgery.

Project description:BackgroundThere are few methods related to predicting lymph node metastasis (LNM) in patients with clinically staged T1 or T2 colon cancer. In this study, we aimed to discover independent risk factors for patients with pathologic T-stage 1 (pT1) or pT2 colon cancer with LNM and to develop a nomogram for predicting the probability of LNM for patients with clinically staged T1 or T2 colon cancer.MethodsAll data were drawn from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors for LNM were identified using univariate and multivariate logistic regression analyses, and these factors were used to construct a nomogram. The discriminatory power, accuracy, and clinical utility of the model were evaluated using receiver operating characteristic (ROC), calibration, and decision curve analysis (DCA), respectively.ResultsAccording to the inclusion and exclusion criteria, 32,803 patients with stage pT1 or pT2 colon cancer who had undergone surgery were selected from the SEER database. The data showed that the incidence of LNM in patients with pT1 and pT2 colon cancer was 17.11%. The age, histological grade, histological type, T classification, M classification, and tumour location were independent risk factors identified through univariate and multivariate analyses, and these factors were used to construct a nomogram. The ROC curve analysis showed that the area under the curve (AUC) of the ROC of the predictive nomogram for LNM risk was 0.6714 [95% confidence interval (CI): 0.6621-0.6806] in the training set and 0.6567 (95% CI: 0.6422-0.6712) in the validation set, indicative of good discriminatory power of the model. Calibration curve analysis demonstrated good agreement between the nomogram prediction and actual observation. DCA showed excellent clinical utility of the prediction model.ConclusionsThe incidence of LNM was high in patients with pT1 and pT2 colon cancer. The nomogram established in this study can accurately predict the risk of LNM in patients with clinically staged T1 or T2 colon cancer before further clinical intervention, which allows clinicians to develop optimal treatment.

Project description:BackgroundVulvar carcinoma is a rare gynecological malignancy. The most commonly used staging system for vulvar cancer is the 2009 International Federation of Gynecology and Obstetrics (FIGO) staging system. Nevertheless, it does not incorporate many indispensable prognostic parameters, which prominently influence vulvar cancer patient survival. Thus, the development of a prediction model for evaluating survival prognosis in postoperative vulvar squamous cell cancer patients is of vital importance.MethodsData from 2,166 patients with pathologically confirmed diagnosis of vulvar squamous cell carcinoma from 2004 to 2015 were acquired from the Surveillance, Epidemiology, and End Results (SEER) database. Thirty percent of the patients were randomly assigned to the validation group, and the remainder were used to develop the nomogram. Parameters that significantly correlated with overall survival (OS) were used to create the nomogram. Concordance index (C-index), calibration curve, and decision curve analysis (DCA) were used to assess the predictive accuracy and discriminability of the nomogram model. Additionally, the C-index and DCA of the nomogram and the FIGO staging system were compared.ResultsFollowing multivariate analysis of the training cohort, independent factors for OS, including race, age at diagnosis, marital status, FIGO stage, tumor diameter, and lymph node ratio (LNR), were included in the nomogram model. The calibration curve indicated a high correlation between the nomogram-predicted and observed survival probability. The C-index of the nomogram in the training cohort was 0.772 (95% CI: 0.752-0.792), statistically superior to the C-index value of the FIGO staging system (0.676, 95% CI: 0.654-0.698). In DCA, compared to the FIGO staging system, this nomogram showed a greater net benefit and a wider range of threshold probability. Results were verified by an internal validation cohort.ConclusionsOur nomogram, based on LNR, showed superior prognostic predictive accuracy compared with the FIGO staging system for predicting OS in postoperative vulvar squamous cell carcinoma patients.

Dataset Information

A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program.

Publications

A Nomogram Predicting Lymph Node Metastasis in T1 Breast Cancer based on the Surveillance, Epidemiology, and End Results Program.

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