Nomograms to Predict Individual Prognosis of Patients with Primary Small Cell Carcinoma of the Bladder

Fan, Daiming; Shen, Yifan; Gao, Feng‐Juan; Shi, Xiao; Xu, Tianmin; Wang, Xianjin; Zhang, Xiaohua; Zhong, Shan; Zhang, Minguang; Chen, Shanwen; Zhang, Shen

doi:10.7150/jca.23344

Cited by 56 publications

(54 citation statements)

References 47 publications

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“…The NRI and IDI are more sensitive indicators than the C-index, and the NRI indicated that the proposed model reclassified the risk probabilities better than did the AJCC staging system, while the IDI demonstrated the superior ability of the constructed nomogram to distinguish cases compared with the AJCC staging system. Numerous previous studies have found benefits of applying DCA [29][30][31][32][33], and the results of the current study showed that the 3-, 5-, and 10-year DCA curves displayed net benefits larger than those of the AJCC staging system, both in the training set and validation set (Fig. 4).…”

Section: Discussionsupporting

confidence: 66%

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

Wang

Yang

et al. 2020

BMC Cancer

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Background: Surgery is the only way to cure gastric adenocarcinoma (GAC), and chemotherapy is the basic adjuvant management for GAC. A significant prognostic nomogram for predicting the respective disease-specific survival (DSS) rates of GAC patients who receive surgery and chemotherapy has not been established. Objective: We were planning to establish a survival nomogram model for GAC patients who receive surgery and chemotherapy. Methods: We identified 5764 GAC patients who had received surgery and chemotherapy from the record of Surveillance, Epidemiology, and End Results (SEER) database. About 70% (n = 4034) of the chosen GAC patients were randomly assigned to the training set, and the rest of the included ones (n = 1729) were assigned to the external validation set. A prognostic nomogram was constructed by the training set and the predictive accuracy of it was validated by the validation set. Results: Based on the outcome of a multivariate analysis of candidate factors, a nomogram was developed that encompassed age at diagnosis, number of regional lymph nodes examined after surgery, number of positive regional lymph nodes, sex, race, grade, derived AJCC stage, summary stage, and radiotherapy status. The C-index (Harrell's concordance index) of the nomogram model was some larger than that of the traditional seventh AJCC staging system (0.707 vs 0.661). Calibration plots of the constructed nomogram displayed that the probability of DSS commendably accord with the survival rate. Integrated discrimination improvement (IDI) revealed obvious increase and categorical net reclassification improvement (NRI) showed visible enhancement. IDI for 3-, 5-and 10year DSS were 0.058, 0.059 and 0.058, respectively (P > 0.05), and NRI for 3-, 5-and 10-year DSS were 0.380 (95% CI = 0.316-0.470), 0.407 (95% CI = 0.350-0.505), and 0.413 (95% CI = 0.336-0.519), respectively. Decision curve analysis (DCA) proved that the constructed nomogram was preferable to the AJCC staging system. Conclusion: The constructed nomogram supplies more credible DSS predictions for GAC patients who receive surgery and chemotherapy in the general population. According to validation, the new nomogram will be beneficial in facilitating individualized survival predictions and useful when performing clinical decision-making for GAC patients who receive surgery and chemotherapy.

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Section: Discussionsupporting

confidence: 66%

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

Wang

Yang

et al. 2020

BMC Cancer

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“…[5][6][7][8] M2 macrophages secrete various anti-in ammatory molecules that can inhibit the immune system and promote tumor angiogenesis, proliferation, invasion, and metastasis, and prevent apoptosis. 5,10,11 M2 macrophage surface markers include CD163, Stabilin-1, and CD206. 4,9,10 Although some studies have explored the correlation between intertumoral macrophages and prognosis, [2][3][4][5] the results were heterogeneous.…”

Section: Introductionmentioning

confidence: 99%

“…Currently, nomograms are widely applied in major types of cancer. [6][7][8][9][10][11] In addition, nomograms are preferred compared with traditional staging systems for many types of cancer. It is because of the intuitive and easy-to-understand features of nomograms that they have gradually received more and more attention in medical research and clinical practice.…”

Section: Introductionmentioning

confidence: 99%

M2 Macrophage-based Prognostic Nomogram for Gastric Cancer After Surgical Resection: Original Article

et al. 2020

Preprint

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Background: A good prediction model is useful to accurately predict patient prognosis. Tumor–node–metastasis (TNM) staging often cannot accurately predict prognosis when used alone. Some researchers have shown infiltration of M2 macrophages in many tumors, which indicates poor prognosis. This approach has the potential to predict prognosis more accurately when used in combination with TNM staging. The present study aimed to develop and validate a nomogram to predict survival in patients with gastric cancer by combining TNM staging and the degree of M2 macrophage infiltration.Methods: Patients undergoing curative resection for gastric cancer between 2008 and 2013 at our hospital were enrolled and assigned into either the training set or the validation set. M2 macrophage markers were evaluated by immunohistochemical staining. A stepwise method was applied to screen variables associated with patient survival time, and a nomogram was constructed to predict patient survival. Concordance index, calibration curve, and decision curve analysis were used to evaluate the discrimination, calibration, and clinical benefit of the model.Results: A multivariate analysis demonstrated that CD163 expression, TNM staging, age, and gender were independent risk factors for overall survival. Thus, these parameters were assessed to develop the nomogram. In the training, validation, and overall datasets, the concordance index was >0.6. The model showed a high degree of discrimination in all three datasets. The five-year survival calibration curves were a very good fit with standard curves in all three datasets, and the model demonstrated good clinical benefit. The prognostic abilities had threshold probabilities of 10%–38% for one-year survival, 10%–75% for three-year survival, and 35%–80% for five-year survival.Conclusions: We combined CD163 expression in macrophages, TNM staging, age, and gender to develop a nomogram to predict five-year overall survival after curative resection for gastric cancer. This model has the potential to provide further diagnostic and prognostic value for patients with gastric cancer.

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“…The nomogram model can quantify, graph, and visualize Logistic regression results to achieve individualized prediction of disease risk. It has been successfully used in clinical diagnosis and prognostic assessment of various diseases [6,7].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Nomogram to Predict the Mortality Risk in Elderly Patients With ARF：A Retrospective Study in the Mimic-III Database

Chen¹,

Weng²,

Hou³

et al. 2020

Preprint

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Background: To explore the risk factors of prognosis in elderly patients with acute respiratory failure (ARF), and to develop a nomogram model to predict the short-term mortality risk of ARF.Methods: A total of 1432 patients were included in this study from MIMIC-III database. 759 patients were categorized into the training set and 673 patients were categorized into the validation set. Demographical, laboratory variables, SOFA score and APS-III score were collected within the first 24 h after the ICU admission. The univariate and multivariate logistic regression were used to identify risk factors from the training data set. A nomogram model was developed to predict the mortality risk of ARF patients within 30 days according to the risk factors.Results: Multivariate logistic regression analysis showed that the heart rate, respiratory rate, systolic pressure, SPO2, albumin and 24 h urine output were independent prognostic factors for 30-day mortality in ARF patients. A nomogram was established based on above independent prognostic factors. This nomogram had C-index of 0.741 (95% CI: 0.7058–0.7766), and the C-index was 0.687 (95%CI: 0.6458-0.7272) in the validation set. The calibration curves both in training and validation set were close to the ideal model. The SOFA had a C-index of 0.653 and the APS-III had a C-index of 0.707 in predicting 30-day mortality. The predictive performance of our nomogram is better than the SOFA score and APS-III score. Conclusions: Our nomogram performed better than APS-III and SOFA scores and should be useful as decision support on the prediction of mortality risk in elderly patients with ARF.

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Nomograms to Predict Individual Prognosis of Patients with Primary Small Cell Carcinoma of the Bladder

Cited by 56 publications

References 47 publications

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

Nomogram for predicting the survival of gastric adenocarcinoma patients who receive surgery and chemotherapy

M2 Macrophage-based Prognostic Nomogram for Gastric Cancer After Surgical Resection: Original Article

Development and Validation of a Nomogram to Predict the Mortality Risk in Elderly Patients With ARF：A Retrospective Study in the Mimic-III Database

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