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Prognostic models to predict overall and cause-specific survival for patients with middle ear cancer: a population-based analysis.

Shen W, Sakamoto N, Yang L - BMC Cancer (2014)

Bottom Line: Calibration plots showed that the predicted survival reasonably approximated observed outcomes.The resulting models demonstrated good accuracy in predicting overall survival and cause-specific survival.Nomograms should thus be considered as a useful tool for predicting clinical prognosis.

View Article: PubMed Central - PubMed

Affiliation: Epidemiology and Clinical Research Center for Children's Cancer, National Center for Child Health and Development, Tokyo, Japan. yo-r@ncchd.go.jp.

ABSTRACT

Background: The purpose of this study was to evaluate the survival outcome for middle ear cancer and to construct prognostic models to provide patients and clinicians with more accurate estimates of individual survival probability.

Methods: Patients diagnosed with middle ear cancer between 1983 and 2011 were selected for the study from the Surveillance Epidemiology and End Results Program. We used the Kaplan-Meier product limit method to describe overall survival and cause-specific survival. Cox proportional hazards models were fitted to model the relationships between patient characteristics and prognosis. Nomograms for predicting overall survival and cause-specific survival were built using the Cox models established.

Results: The entire cohort comprised 247 patients with malignant middle ear cancer. Median duration of follow-up until censoring or death was 25 months (range, 1-319 months). Five-year overall survival and cause-specific survival were 47.4% (95% Confidence Interval (CI), 41.2% to 54.6%) and 58.0% (95% CI, 51.6% to 65.3%), respectively. In multivariable analysis, age, histological subtype, stage, surgery and radiotherapy were predictive of survival. The bootstrap corrected c-index for model predicting overall and cause-specific survival was 0.73 and 0.74, respectively. Calibration plots showed that the predicted survival reasonably approximated observed outcomes.

Conclusion: The models represent an objective analysis of all currently available data. The resulting models demonstrated good accuracy in predicting overall survival and cause-specific survival. Nomograms should thus be considered as a useful tool for predicting clinical prognosis.

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Related in: MedlinePlus

Nomograms for predicting 5- and 10-year overall survival and cause-specific survival. (A) Prediction for overall survival; (B) Prediction for cause-specific survival. Abbreviations: Stage: L, localized; R, regional; D, distant. Histological subtype: S, squamous cell carcinoma; A, adenocarcinoma; O, others. Instructions: Locate the patient’s characteristic on the variable row and draw a vertical line straight up to the points row to assign a value of points for the variable. Repeat this process to obtain points for each variable. Add up the total points and drop a vertical line from the total points row to obtain the 5- and 10-year survival.
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Fig2: Nomograms for predicting 5- and 10-year overall survival and cause-specific survival. (A) Prediction for overall survival; (B) Prediction for cause-specific survival. Abbreviations: Stage: L, localized; R, regional; D, distant. Histological subtype: S, squamous cell carcinoma; A, adenocarcinoma; O, others. Instructions: Locate the patient’s characteristic on the variable row and draw a vertical line straight up to the points row to assign a value of points for the variable. Repeat this process to obtain points for each variable. Add up the total points and drop a vertical line from the total points row to obtain the 5- and 10-year survival.

Mentions: The nomograms were developed for predicting OS and CSS based on beta coefficients in finial models (Figure 2). To use the nomogram, first draw a vertical line up to the points row to assign points for each variable, then add up the points for each variable to obtain the total points, and drop a vertical line from the total points row to obtain the 5- and 10-year survival.


Prognostic models to predict overall and cause-specific survival for patients with middle ear cancer: a population-based analysis.

Shen W, Sakamoto N, Yang L - BMC Cancer (2014)

Nomograms for predicting 5- and 10-year overall survival and cause-specific survival. (A) Prediction for overall survival; (B) Prediction for cause-specific survival. Abbreviations: Stage: L, localized; R, regional; D, distant. Histological subtype: S, squamous cell carcinoma; A, adenocarcinoma; O, others. Instructions: Locate the patient’s characteristic on the variable row and draw a vertical line straight up to the points row to assign a value of points for the variable. Repeat this process to obtain points for each variable. Add up the total points and drop a vertical line from the total points row to obtain the 5- and 10-year survival.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4129120&req=5

Fig2: Nomograms for predicting 5- and 10-year overall survival and cause-specific survival. (A) Prediction for overall survival; (B) Prediction for cause-specific survival. Abbreviations: Stage: L, localized; R, regional; D, distant. Histological subtype: S, squamous cell carcinoma; A, adenocarcinoma; O, others. Instructions: Locate the patient’s characteristic on the variable row and draw a vertical line straight up to the points row to assign a value of points for the variable. Repeat this process to obtain points for each variable. Add up the total points and drop a vertical line from the total points row to obtain the 5- and 10-year survival.
Mentions: The nomograms were developed for predicting OS and CSS based on beta coefficients in finial models (Figure 2). To use the nomogram, first draw a vertical line up to the points row to assign points for each variable, then add up the points for each variable to obtain the total points, and drop a vertical line from the total points row to obtain the 5- and 10-year survival.

Bottom Line: Calibration plots showed that the predicted survival reasonably approximated observed outcomes.The resulting models demonstrated good accuracy in predicting overall survival and cause-specific survival.Nomograms should thus be considered as a useful tool for predicting clinical prognosis.

View Article: PubMed Central - PubMed

Affiliation: Epidemiology and Clinical Research Center for Children's Cancer, National Center for Child Health and Development, Tokyo, Japan. yo-r@ncchd.go.jp.

ABSTRACT

Background: The purpose of this study was to evaluate the survival outcome for middle ear cancer and to construct prognostic models to provide patients and clinicians with more accurate estimates of individual survival probability.

Methods: Patients diagnosed with middle ear cancer between 1983 and 2011 were selected for the study from the Surveillance Epidemiology and End Results Program. We used the Kaplan-Meier product limit method to describe overall survival and cause-specific survival. Cox proportional hazards models were fitted to model the relationships between patient characteristics and prognosis. Nomograms for predicting overall survival and cause-specific survival were built using the Cox models established.

Results: The entire cohort comprised 247 patients with malignant middle ear cancer. Median duration of follow-up until censoring or death was 25 months (range, 1-319 months). Five-year overall survival and cause-specific survival were 47.4% (95% Confidence Interval (CI), 41.2% to 54.6%) and 58.0% (95% CI, 51.6% to 65.3%), respectively. In multivariable analysis, age, histological subtype, stage, surgery and radiotherapy were predictive of survival. The bootstrap corrected c-index for model predicting overall and cause-specific survival was 0.73 and 0.74, respectively. Calibration plots showed that the predicted survival reasonably approximated observed outcomes.

Conclusion: The models represent an objective analysis of all currently available data. The resulting models demonstrated good accuracy in predicting overall survival and cause-specific survival. Nomograms should thus be considered as a useful tool for predicting clinical prognosis.

Show MeSH
Related in: MedlinePlus