Novel prognostic nomograms for female patients with breast cancer and bone metastasis at presentation

Wang, Zhan; Cheng, Yonggang; Chen, Shi; Shao, Haiyu; Chen, Xiaowei; Wang, Zenan; Wang, Yucheng; Zhou, Hao; Chen, Tao; Lin, Nong; Ye, Zhaoming

doi:10.21037/atm.2020.01.37

Cited by 38 publications

(71 citation statements)

References 49 publications

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“…The result of our study also showed that surgical treatment could significantly reduce the cancer‐specific risk of death in BMC patients, which is consistent with the clinical experience of most doctors. Some scholars included chemotherapy and radiotherapy in their studies by SEER database (Du et al, 2020; Han et al., 2020; Wang, Cheng, et al, 2020; Wang, Wu, et al, 2020). However, the latest statement of SEER database (https://seer.cancer.gov/data/ChemotherapyRadiation-SEER-DUA.pdf) said that because the staff could not correctly distinguish whether patients received radiotherapy or chemotherapy when collecting data, they did not recommend the above two variables to be used in the analysis of relationship between treatment and patient prognosis.…”

Section: Discussionmentioning

confidence: 99%

“…But, most nomograms are based on the traditional Kaplan–Meier approach and Cox proportional hazard model, ignoring the occurrence of competitive events, thereby misjudged the prognosis of patients. For instance, if a patient died from a cardiovascular disease or accident rather than cancer, and we did not add it to the survival analysis, it would lead to the deviation of the predicted results from the real situation (van Walraven & McAlister, 2016; Wang, Cheng, et al, 2020; Wang, Wu, et al, 2020). A competing risk model (Gray's test and Fine–Gray proportional hazard model) can well deal with the relationship between cancer outcomes and competitive events so that the accuracy of prognosis analysis of cancer patients can be improved significantly (Fine & Gray, 1999; Gray, 1988; Scrucca, Santucci, & Aversa, 2010).…”

Section: Introductionmentioning

confidence: 99%

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A competing risk nomogram for predicting cancer‐specific death of patients with buccal mucosa cancer

et al. 2020

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Objectives Our aim was to develop and validate a competing risk nomogram to determine the probability of cancer‐specific death in buccal mucosa cancer (BMC) patients. Materials and Methods We examined the records of BMC patients in the Surveillance, Epidemiology, and End Results (SEER) Program and First Affiliated Hospital of Nanchang University (China). We adopted the cumulative incidence function and Fine–Gray proportional hazards model based on univariate and multivariate analyses by R‐software to identify the risk factors associated with cancer‐specific death. Subsequently, a nomogram was developed and validated to predict the 3‐ and 5‐year probability of cancer‐specific death. Results In 1,286 BMC patients identified from SEER database, cumulative incidences of cancer‐specific death after diagnosis were 33.4% and 35.5% for 3 and 5 years, respectively. In the training cohort (n = 902) from SEER database, the Fine–Gray model indicated that age, Tumor Node Metastasis (TNM) stages, grade, surgery, and histological type were independent risk factors associated with cancer‐specific death, based on which a prognostic nomogram was developed. In the internal validation cohort from SEER database (n = 384) and the external validation cohort from our medical center (n = 174), the nomogram was well calibrated and showed remarkable prediction performance. Conclusion The nomogram created herein may prove to be a good assistant tool for assessing the prognosis of BMC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A competing risk nomogram for predicting cancer‐specific death of patients with buccal mucosa cancer

et al. 2020

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“…The nomograms of OS and CSS are shown in Figures 2 and 3 . Nomogram is a quite user-friendly predictive tool which enables clinician or patient to determine the survival probability by calculating the scores of covariate and then draw a line vertically downward [ 16 ]. The scores assigned to each factor were listed in Table 4 .…”

Section: Resultsmentioning

confidence: 99%

Establishment and Validation of Novel Clinical Prognosis Nomograms for Luminal A Breast Cancer Patients with Bone Metastasis

Zhang

et al. 2020

BioMed Research International

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Purpose. Overall survival (OS) and cancer-specific survival (CSS) of luminal A breast cancer (BC) patients with bone metastasis remain poor and vary dramatically from person to person. Our goal was to build two universally applicable nomograms to accurately predict OS and CSS for luminal A patients with bone metastasis. Methods. The data were collected from the Surveillance, Epidemiology, and End Results (SEER) database for luminal A BC patients with bone metastasis between 2010 and 2015. Univariate and multivariate Cox regression analyses were to assess and identify independent risk factors of OS and CSS. Integrating all significant predictors, nomograms and risk group stratification model was developed. The performance of the nomogram was validated with concordance index (C-index), calibration plots, and decision curve analyses (DCA) for discriminative ability, calibration, and clinical utility, respectively. Results. 3171 luminal A BC patients with bone metastasis were included. Through univariate and multivariate Cox regression analyses, 12 variables were identified as both independent OS- and CSS-related factors, including age, race, primary site, histology grade, tumor size, surgery, brain metastasis, liver metastasis, lung metastasis, estrogen receptor status, progesterone receptor status, and insurance. Our nomograms for 1-, 3-, and 5-year survival were based on those significant prognostic factors to develop. The C-indexes of OS- and CSS-nomograms in the training cohort were 0.701 and 0.704, respectively. Similar results were obtained in the validation cohort. The calibration curves and DCA presented satisfactory calibration and clinical utility. Conclusion. Two nomograms have good discrimination, calibration, and clinical utility, can accurately and effectively predict the prognosis of patients, and may benefit for clinical decision-making. In high-risk patients, more aggressive therapy and closer surveillance should be considered.

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“…Several previous studies have reported on the prediction of metastatic Ewing's sarcoma(MES) models-ROC 6 . But now, nomograms were the graphical description of predictive statistical model, which has been widely used to accurately predict the survival of cancer patients, such as bladder cancer, colon and breast cancer [7][8][9][10][11] . To the best of our knowledge, nomogram have not been used in patients with metastatic ES.…”

Section: Introductionmentioning

confidence: 99%

Individualized Prediction of Overall Survival for Metastatic Ewing sarcoma of Bone

Wang

2021

Preprint

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Background: Few models have been used to estimate the survival rate of patients metastatic Ewing sarcoma of bone are scarce. We aimed to develop nomograms for predicting 3-, 5-year survival for these patients.Methods: We extracted 686 cases of metastatic Ewing's sarcoma diagnosed between 1973 and 2016 from the Surveillance, Epidemiological and End Results (SEER) database. Univariate and multivariate Cox analysis were used to determine independent prognostic factors. The nomograms are based on the results of multivariate Cox analysis. We also evaluate the performance of these prediction models through the analysis of time-dependent receiver operating characteristic curve, concordance index, calibration curve and decision curve.Results: Age, surgery, tumor size, treatment method and chemotherapy were considered to be important predictors of overall survival of bone metastatic Ewing's sarcoma. Based on these factors, the nomogram models were established and verified internally. These models have good identification and calibration characteristics. A risk classification system based on nomogram has also been constructed to promote risk stratification of metastatic Ewing's sarcoma and to optimize clinical management.Conclusions: We developed the first nomograms and corresponding risk classification system to predict the survival of patients with bone metastatic Ewing's sarcoma. These easy-to-use tools can help oncologists and surgeons make accurate survival assessments.

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Novel prognostic nomograms for female patients with breast cancer and bone metastasis at presentation

Cited by 38 publications

References 49 publications

A competing risk nomogram for predicting cancer‐specific death of patients with buccal mucosa cancer

A competing risk nomogram for predicting cancer‐specific death of patients with buccal mucosa cancer

Establishment and Validation of Novel Clinical Prognosis Nomograms for Luminal A Breast Cancer Patients with Bone Metastasis

Individualized Prediction of Overall Survival for Metastatic Ewing sarcoma of Bone

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