Nomogram to predict the prognosis of parotid gland mucoepidermoid carcinoma: a population-based study of 1306 cases

Sun, Jian; Sun, Yang; Yang, Fei; Zhou, Qianrong; Liu, Wenjuan; Cheng, Yong; Wu, Xinqi; Chen, Tinglan; Li, Ruixue; Huang, Borui; Att, Wael; Bi, Wei

doi:10.7717/peerj.7237

Cited by 11 publications

(27 citation statements)

References 19 publications

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“…The prognostic value of PRT has been explored in several studies, and it remains controversial, with some authors believing that PRT can significantly improve the prognosis of PGC patients, 23 others considering that PGC patients cannot benefit from PRT, 6,22 and some even thinking that PRT will impair survival due to its adverse effects 11,42 . The present study found no significant difference in the GCD risk between patients who received both surgery and radiotherapy and those who received only surgery.…”

Section: Discussioncontrasting

confidence: 55%

“…Patients with palpable lymphadenopathy are treated with radical or modified radical neck dissection 6 . And chemotherapy is mainly applied in the palliative setting 11 . The treatment strategy in individual patients is determined based on evaluations of the prognosis, 5,6,11‐14 and so inaccurate prognostic predictions may lead to either inadequate or excessive treatment 15 .…”

Section: Introductionmentioning

confidence: 99%

“…And chemotherapy is mainly applied in the palliative setting 11 . The treatment strategy in individual patients is determined based on evaluations of the prognosis, 5,6,11‐14 and so inaccurate prognostic predictions may lead to either inadequate or excessive treatment 15 . The commonly used prognostic tool for PGC is the American Joint Committee on Cancer (AJCC) staging, which is based on the tumor size or the extent of invasion (T), nodal involvement (N), and distant metastasis (M).…”

Section: Introductionmentioning

confidence: 99%

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Competing‐risks nomograms for predicting cause‐specific mortality in parotid‐gland carcinoma: A population‐based analysis

Xue

Zhao

et al. 2021

Cancer Medicine

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Introduction Parotid‐gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing‐risks analysis to PGC patients, and then established and validated predictive nomograms for PGC. Methods Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC‐specific death (GCD) and other cause‐specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine–Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C‐index) were employed to validate the nomograms. Results We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1‐, 3‐, and 5‐year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C‐index suggested that the nomograms were well calibrated and had good discrimination ability. Conclusion We have used the SEER database to establish—to the best of our knowledge—the first competing‐risks nomograms for predicting the 1‐, 3‐, and 5‐year cause‐specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision‐making.

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Section: Discussioncontrasting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Competing‐risks nomograms for predicting cause‐specific mortality in parotid‐gland carcinoma: A population‐based analysis

Xue

Zhao

et al. 2021

Cancer Medicine

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“…Of the 11 variables determined in this study, these predictors have been proven in other studies (20,21). Nine (age, AJCC stage, radiation, T stage, M stage, grade, surgery, N stage, and histological type) were established as indicators of CSD in persons with PC via univariate coupled with the Lasso Cox regression model competing risk assessment.…”

Section: Discussionmentioning

confidence: 88%

Nomogram Predicting Cancer-Specific Death in Parotid Carcinoma: a Competing Risk Analysis

et al. 2021

Front. Oncol.

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PurposeMultiple factors have been shown to be tied to the prognosis of individuals with parotid cancer (PC); however, there are limited numbers of reliable as well as straightforward tools available for clinical estimation of individualized mortality. Here, a competing risk nomogram was established to assess the risk of cancer-specific deaths (CSD) in individuals with PC.MethodsData of PC patients analyzed in this work were retrieved from the Surveillance, Epidemiology, and End Results (SEER) data repository and the First Affiliated Hospital of Nanchang University (China). Univariate Lasso regression coupled with multivariate Cox assessments were adopted to explore the predictive factors influencing CSD. The cumulative incidence function (CIF) coupled with the Fine-Gray proportional hazards model was employed to determine the risk indicators tied to CSD as per the univariate, as well as multivariate analyses conducted in the R software. Finally, we created and validated a nomogram to forecast the 3- and 5-year CSD likelihood.ResultsOverall, 1,467 PC patients were identified from the SEER data repository, with the 3- and 5-year CSD CIF after diagnosis being 21.4% and 24.1%, respectively. The univariate along with the Lasso regression data revealed that nine independent risk factors were tied to CSD in the test dataset (n = 1,035) retrieved from the SEER data repository. Additionally, multivariate data of Fine-Gray proportional subdistribution hazards model illustrated that N stage, Age, T stage, Histologic, M stage, grade, surgery, and radiation were independent risk factors influencing CSD in an individual with PC in the test dataset (p < 0.05). Based on optimization performed using the Bayesian information criterion (BIC), six variables were incorporated in the prognostic nomogram. In the internal SEER data repository verification dataset (n = 432) and the external medical center verification dataset (n = 473), our nomogram was well calibrated and exhibited considerable estimation efficiency.ConclusionThe competing risk nomogram presented here can be used for assessing cancer-specific mortality in PC patients.

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“…A nomogram is a visualization tool that has been widely used to predict the prognosis of cancer patients [4][5][6][7] . It is valuable for clinical decision-making and patient counseling.…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Nomograms to Predict Prognosis of Oral and Oropharyngeal Mucoepidermoid Carcinoma: A SEER-Based Study

Liu

Tong

et al. 2020

Preprint

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Background: Due to the low incidence of mucoepidermoid carcinoma, there lacks sufficient studies for determining optimal treatment and predicting prognosis. The purpose of this study was to develop prognostic nomograms, to predict overall survival and disease-specific survival (DSS) of oral and oropharyngeal mucoepidermoid carcinoma patients, using the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Methods: Clinicopathological and follow-up data of patients diagnosed with oral and oropharyngeal mucoepidermoid carcinoma between 2004 and 2017 were collected from the SEER database. The Kaplan-Meier method with the log-rank test was employed to identify single prognostic factors. Multivariate Cox regression was utilized to identify independent prognostic factors. C-index, area under the ROC curve (AUC) and calibration curves were used to assess performance of the prognostic nomograms. Results: A total of 1230 patients with oral and oropharyngeal mucoepidermoid carcinoma were enrolled in the present study. After multivariate Cox regression analysis, age, sex, tumor subsite, T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for overall survival. T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for disease-specific survival. Nomograms were constructed to predict the overall survival and disease-specific survival based on the independent prognostic factors. The fitted nomograms possessed excellent prediction accuracy, with a C-index of 0.899 for OS prediction and 0.893 for DSS prediction. Internal validation by computing the bootstrap calibration plots, using the validation set, indicated excellent performance by the nomograms. Conclusion: The prognostic nomograms developed, based on individual clinicopathological characteristics, in the present study, accurately predicted the overall survival and disease-specific survival of patients with oral and oropharyngeal mucoepidermoid carcinoma.

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Nomogram to predict the prognosis of parotid gland mucoepidermoid carcinoma: a population-based study of 1306 cases

Cited by 11 publications

References 19 publications

Competing‐risks nomograms for predicting cause‐specific mortality in parotid‐gland carcinoma: A population‐based analysis

Competing‐risks nomograms for predicting cause‐specific mortality in parotid‐gland carcinoma: A population‐based analysis

Nomogram Predicting Cancer-Specific Death in Parotid Carcinoma: a Competing Risk Analysis

Development and Validation of Nomograms to Predict Prognosis of Oral and Oropharyngeal Mucoepidermoid Carcinoma: A SEER-Based Study

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